A method for detecting atmospheric density by layer scanning, computer equipment and storage medium

Abstract

The present invention relates to an atmospheric density tomography scanning detection method, a computer device and a storage medium. The method comprises: constructing a forward model of the energy spectrum of the astronomical X-ray source in the process of the earth's atmospheric occultation based on the detector response matrix of the X-ray satellite calibration database, the X-ray cross section of the earth's atmospheric components calculated by using the XCOM database, the energy spectrum of the celestial X-ray source obtained by Xspec fitting, and the atmospheric density basis function provided by the NRLMSIS 2.0 model; constructing a likelihood function between the hypothesis and the actual measurement under Poisson statistics based on the X-ray satellite measured energy spectrum data with a sufficiently high signal-to-noise ratio and vertical resolution obtained by satellite platform processing; and fitting the observed energy spectrum and the model energy spectrum using a Markov chain Monte Carlo algorithm (MCMC) based on the Bayesian theorem and Bayesian estimation, thereby realizing the inversion of the atmospheric density in the near-space of the earth. The method can overcome the defects of the poor spatial coverage of the existing detection means and the difficulty in realizing global measurement.

Description

Technical Field

[0001] The present invention relates to the fields of high-energy astrophysics and space physics, and in particular to a tomographic scanning detection method and system for atmospheric density in the Earth's near-space based on X-rays. Background Art

[0002] The Earth's near-space atmosphere is affected by both solar and geomagnetic activities from the upper layer and by topography, weather activities, and gravity waves from the lower layer. Therefore, the near-space atmosphere exhibits complex and changeable characteristics. As an important parameter to characterize the structure of the near-space atmosphere, the near-space atmospheric density is of great significance for studying the relationship between the Sun and the Earth and the coupling mechanism between the upper and lower layers. At the same time, near-space is the passage area for aerospace vehicles. The near-space atmospheric density is very important for the precise orbit determination, maneuver planning, collision avoidance of space debris, and re-entry control of re-entry vehicles of low-orbit satellites. Therefore, the near-space atmospheric density is also widely used in the field of aerospace technology. In addition, the near-space atmospheric density affects the aerodynamic design and flight control of hypersonic vehicles. The design optimization of the vehicle has a great influence on the performance of the final vehicle. Therefore, the near-space atmospheric density also has outstanding scientific research needs.

[0003] At present, the commonly used methods for measuring atmospheric density mainly include ground-based detection, air-based detection, and space-based detection.

[0004] (1) Ground-based detection mainly refers to Rayleigh LiDAR detection technology, which uses the Rayleigh scattering echo signal collected by LiDAR to detect the neutral atmospheric density below 90 km. It has high detection accuracy and high vertical resolution of detection results. However, this technology is greatly affected by bad weather such as rain, snow, hail, and dust. The detection height is limited and it is difficult to implement in deserts, glaciers, oceans and other areas.

[0005] (2) Air-based detection mainly refers to the direct detection of atmospheric density using instruments and equipment carried by sounding balloons or sounding rockets. During the ascent, sounding balloons will explode due to excessive internal pressure. Therefore, the detection altitude of sounding balloons is below 35 km and is mostly used for meteorological research. The detection altitude of sounding rockets can reach 80-100 km above the ground. Sounding balloons and sounding rockets are an in-situ detection method with low cost and simple operation. They can also observe short-term local phenomena such as magnetic storms and auroras. However, this method has poor spatial coverage, limited detection altitude, and is difficult to achieve long-term monitoring of atmospheric density on a global scale.

[0006] (3) Space-based detection mainly refers to the measurement of atmospheric density based on satellite platforms, mainly including limb / occultation detection technology. The limb / occultation detection method based on satellite platforms as a space-based measurement method can solve the shortcomings of poor spatial coverage and difficulty in long-term monitoring of ground-based detection and air-based detection. However, at present, the bands of limb / occultation detection are mostly concentrated in the ultraviolet, visible light, infrared and radio bands. The limb / occultation detection technology based on the ultraviolet-visible-infrared band is mainly used to detect the number density of trace gases or single components in the earth's atmosphere based on the selective absorption characteristics of electromagnetic radiation by the atmosphere, and it is difficult to detect the neutral atmospheric density. The occultation detection technology based on the radio band can be used to detect the neutral atmospheric density of 0-60km based on the refraction of radio signals by the earth's atmosphere. This band can well avoid the influence of clouds and aerosols on the detection results, but the detection height is concentrated below 60km.

[0007] Through the above technical means, it is found that there are limitations in the technical platform for the detection of the near-space atmosphere. It is difficult to achieve long-term detection of the near-space atmosphere based on the above means. Therefore, there is a serious lack of measured data on the near-space atmospheric density, especially in the 100-200km altitude range. At present, this part of the data is mainly based on theoretical extrapolation, but the calculation results have large errors. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of existing atmospheric density measurement methods, such as poor spatial coverage, limited detection height and difficulty in real-time and long-term monitoring, so as to provide an atmospheric density measurement algorithm for the Earth's near-space that can be used for real-time and long-term monitoring and is applicable to the global range.

[0009] In order to achieve the above object, the present invention provides an atmospheric density tomography scanning detection method, comprising:

[0010] Step 101: Select an astronomical X-ray source with high stability as an observation target, and obtain the orbital position coordinates and detector response matrix of the X-ray satellite;

[0011] Step 102: selecting exposure time and energy grid points based on the orbital position coordinates determined in step (101), processing the X-ray satellite occultation Level 1 data, and extracting energy spectrum occultation data with high signal-to-noise ratio and high vertical resolution;

[0012] Step 103, based on the energy grid points obtained in step 102, obtain the X-ray cross sections of each component of the atmosphere;

[0013] Step 104, based on the vertical resolution energy spectrum occultation data obtained in step 102, calculate the coordinates of the tangent point between the X-ray satellite line of sight and the atmospheric layer during the occultation process and the geographical longitude and latitude coordinates of the tangent point "sub-satellite point", and calculate the tangent point height grid points during the occultation process;

[0014] Step 105, based on the geographical longitude and latitude coordinates of the tangent point "sub-satellite point" obtained in step 104 and the tangent point height grid point, obtain the atmospheric density basis function;

[0015] Step 106: Based on the X-ray cross sections of the atmospheric components obtained in step 103 and the atmospheric density basis functions obtained in step 105, and in combination with the unattenuated energy spectrum of the astronomical X-ray source obtained by Xspec fitting, a forward model of the X-ray energy spectrum during the occultation process is constructed;

[0016] Step 107: construct a likelihood function between the hypothesis and the data under the Poisson distribution based on the energy spectrum occultation data obtained in step 102 and the forward model of the X-ray energy spectrum during the occultation process obtained in step 106;

[0017] Step 108: Using the likelihood function between the hypothesis and the data under the Poisson distribution constructed in step 107, the inversion of the atmospheric density in the space near the Earth is realized.

[0018] Based on an improvement of the above method, step 101 is: select an astronomical X-ray source with high stability as the observation target, perform Lagrangian interpolation operation on the orbital position of the X-ray satellite during the occultation period, and obtain orbital position coordinates with higher resolution; multiply the corresponding elements of the auxiliary response file ARF and the redistribution matrix file RMF based on the calibration database provided by the X-ray satellite to obtain the detector response matrix R:

[0019] R(E′,E)=ARF(E)×RMF(E′,E)

[0020] Among them, E' is the detector output energy grid, E is the incident photon energy grid, and is also the input energy channel of the detector response matrix.

[0021] Based on an improvement of the above method, step 102 is: based on the orbital position coordinates determined in step 101, the exposure time is selected so that the spatial vertical resolution of the altitude is less than 1 km, the energy grid is determined according to the energy channel input by the detector response matrix, the occultation Level 1 data of the X-ray satellite is processed, and the energy spectrum occultation data with high signal-to-noise ratio and high vertical resolution is extracted.

[0022] Based on an improvement of the above method, step 104 is:

[0023] First, the right ascension and declination (α, δ) of the X-ray source of the observed target celestial body are converted into Cartesian coordinates (xsource ,y source ,z source )Unit vector:

[0024]

[0025] By satellite orbit position vector and the unit vector of the X-ray source position of the observed target celestial body By using the law of cosines, we can obtain the angle θ between the satellite orbital position and the X-ray source of the observed target celestial body, and its complementary angle β:

[0026]

[0027] sin(β)=sin(π-θ)=sin(θ)

[0028] Next, find the tangent point height Z′:

[0029] Z′=r·sin(β)-R E

[0030] Among them, R E is the radius of the earth; r is the distance from the orbital position to the center of the earth, calculated by the following formula:

[0031]

[0032] Based on an improvement of the above method, step 105 is: based on the geographical longitude and latitude coordinates of the tangent point "sub-satellite point" obtained in step 104 and the tangent point height grid point, the atmospheric density basis function is obtained through the NRLMSIS 2.0 model.

[0033] Based on an improvement of the above method, step 106 is:

[0034] Based on the X-ray cross section calculated from the XCOM database and the unattenuated energy spectrum of the X-ray source of the observed target celestial body obtained by Xspec fitting, the energy spectrum model during the occultation process is created through the basis functions obtained from the NRLMSIS 2.0 atmospheric model:

[0035] FM(E,h)=RI0(E)e -τ(E,h) +B

[0036] Where R is the detector response matrix, each row of which represents the probability density distribution of photons within a certain incident energy range being detected on each electronic channel; B is the background noise parameter, ranging from 0 to 20, with the unit of counts / s; I0(E) is the unattenuated energy spectrum of the X-ray source of the observed target celestial body obtained by fitting the measured data based on Xspec; τ(E,h) is the optical thickness, which represents the transmittance level of the atmosphere in a certain line of sight direction to X-ray photons of a certain energy band, calculated by the following formula:

[0037]

[0038] Where s represents the composition of the atmosphere, including oxygen atoms (O), oxygen molecules (O2), nitrogen atoms (N), nitrogen molecules (N2) and argon atoms (Ar); l0 is the position coordinate of the X-ray satellite; σ s (E) is the X-ray cross section of each component in the atmosphere; n s (h) is the basis function of each atmospheric component at the tangent point height in the line of sight direction provided by the NRLMSIS 2.0 model; γ is the correction factor, ranging from 0 to 10.

[0039] The energy spectrum model formula has two dimensions: energy and tangent point height. The energy spectrum modeling during the occultation is realized by summing the tangent point heights.

[0040] Based on an improvement of the above method, step (107) is:

[0041] The X-ray photon counts conform to the Poisson distribution, and the likelihood function is constructed based on the statistical relationship between the hypothesis and the data under the Poisson distribution; the Poisson distribution is shown as follows:

[0042]

[0043] Among them, D i represents the i-th data point, M i represents the i-th model count.

[0044] Based on an improvement of the above method, step 108 is: using the likelihood function between the hypothesis and the data under the Poisson distribution constructed in step 107, based on Bayesian estimation, using the Markov Chain Monte Carlo (MCMC) algorithm to achieve the inversion of the atmospheric density in the near-space of the Earth, the calculation formula is:

[0045] Among them, γ is the correction factor, n s (h) is the basis function of each atmospheric component in the line of sight direction at the tangent point height h given by NRLMSIS 2.0.

[0046] The present invention also provides a computer 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 as described in any one of claims 4, 6, and 7 when executing the computer program.

[0047] The present invention also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the method as claimed in any one of claims 4, 6, and 7.

[0048] The advantages of the present invention are:

[0049] The present invention is based on the detector response matrix of the X-ray satellite calibration database, the X-ray cross section of the earth's atmospheric components calculated using the XCOM database, the celestial X-ray source energy spectrum obtained by Xspec fitting, and the atmospheric density basis function provided by the NRLMSIS2.0 model, to construct the energy spectrum forward model of the earth's atmospheric occultation process of the astronomical X-ray source; based on the satellite platform processing to obtain the X-ray satellite measured energy spectrum data with a sufficiently high signal-to-noise ratio and vertical resolution, to construct the likelihood function between the hypothesis and the actual measurement under Poisson statistics; based on the Bayesian theorem, the Bayesian estimation is specifically used to fit the observed energy spectrum and the model energy spectrum using the Markov chain Monte Carlo algorithm (MCMC), thereby realizing the inversion of the atmospheric density in the near-space of the earth. This method can overcome the defects of poor spatial coverage of general ground-based detection and air-based detection means, which are difficult to achieve global measurement, and can realize long-term monitoring of the atmospheric density in the near-space of the earth. The tomographic scanning detection of atmospheric density in the Earth's near-space based on X-ray occultation has opened up a new field. The present invention can finally realize the measurement of atmospheric density in the range of about 50-250km in the Earth's near-space by processing occultation data in the X-ray energy band and performing energy spectrum modeling in the occultation process, thus making up for the shortage of measured atmospheric density data in this range. In addition, X-ray photons directly interact with K-layer and L-layer electrons inside atoms and are not affected by atmospheric chemistry, thermodynamics, etc. This method can directly invert the neutral density of the atmosphere in the Earth's near-space.

[0050] The method of the present invention can invert high-precision atmospheric density results at all altitudes in the Earth's near-space. Based on a satellite platform, real-time and dynamic measurement of atmospheric density within a range of about 50–250 km in the Earth's near-space can be achieved, and the measurement results can achieve global coverage. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Shown is a schematic diagram of X-ray occultation observation geometry;

[0052] Figure 2 Shown is a flow chart of the atmospheric refraction correction method of the present invention;

[0053] Figure 3 Shown is a schematic diagram of the atmospheric refraction correction method of the present invention;

[0054] Figure 4 Shown is the posterior probability density distribution plot. DETAILED DESCRIPTION

[0055] The atmospheric density in the Earth's near-space is an important parameter that characterizes the atmospheric structure in near-space. It is of great significance for studying the coupling relationship and physical processes between the upper and lower layers, and plays an important role in the design and optimization of aircraft. In addition, the atmospheric density in near-space affects the aerodynamic design and flight control of hypersonic aircraft, and there is a very prominent scientific research demand. However, due to the limitations of detection technology, there is a serious lack of measured data on the atmospheric density in near-space. At present, this part of the data is mainly based on theoretical extrapolation, but the calculated results have large errors.

[0056] At present, the commonly used atmospheric density measurement methods mainly include ground-based detection, air-based detection, and space-based detection. The detection range of ground-based detection and air-based detection methods is limited, and it is difficult to achieve global measurement and long-term monitoring of the Earth's atmospheric density. For example, the detection range of Rayleigh scattering lidar is mostly concentrated in the stratosphere and lower mesosphere, the detection altitude of sounding balloons can only reach 30-40km, and the detection range of sounding rockets is also concentrated at altitudes below 100km. Therefore, it is necessary to develop new technologies to detect high-precision atmospheric density in the near-space of the Earth. The limb / occultation detection method based on satellite platforms as a space-based measurement method can solve the shortcomings of poor spatial coverage and difficulty in long-term monitoring of ground-based detection and air-based detection. However, at present, the bands of limb / occultation detection are mostly concentrated in the ultraviolet, visible light, infrared and radio bands. Due to the selective absorption of electromagnetic radiation by the atmosphere, the detection of the above bands is mostly limited to the measurement of trace gases or single components of the atmosphere, and the detection altitude is also limited.

[0057] The purpose of the present invention is to overcome the shortcomings of existing atmospheric density measurement methods, such as poor spatial coverage, limited detection height and difficulty in real-time and long-term monitoring, so as to provide an atmospheric density measurement algorithm for the Earth's near-space that can be used for real-time and long-term monitoring and is applicable to the global range.

[0058] The present invention will now be further described with reference to the accompanying drawings.

[0059] refer to Figure 2 and Figure 3 The present invention provides an atmospheric density tomography scanning detection method, which specifically comprises the following steps:

[0060] Step 101: Select an astronomical X-ray source with high stability as an observation target, and obtain the orbital position coordinates and detector response matrix of the X-ray satellite.

[0061] Astronomical X-ray sources with high stability are selected as observation targets, and Lagrangian interpolation operations are performed on the orbital positions of X-ray satellites during occultation to obtain orbital position coordinates with higher resolution. The detector response matrix R is obtained by multiplying the corresponding elements of the auxiliary response file ARF and the redistribution matrix file RMF based on the calibration database provided by the X-ray satellite:

[0062] R(E′,E)=ARF(E)×RMF(E′,E)

[0063] Among them, E' is the detector output energy grid, E is the incident photon energy grid, and is also the input energy channel of the detector response matrix.

[0064] Step 102: Based on the orbital position coordinates determined in step 101, the exposure time and energy grid points are selected to process the X-ray satellite occultation Level 1 data to extract energy spectrum occultation data with high signal-to-noise ratio and high vertical resolution. The exposure time is selected so that the vertical resolution of the altitude space is less than 1 km, and the energy grid points are determined according to the energy channel input by the detector response matrix.

[0065] Step 103, based on the energy grid points obtained in step 102, obtaining the X-ray cross section of each component of the atmosphere;

[0066] Step 104, based on the vertical resolution obtained in step 102, calculate the coordinates of the tangent point between the X-ray satellite line of sight and the atmospheric layer during the occultation process and the geographic longitude and latitude coordinates of the tangent point "sub-satellite point", and calculate the tangent point height grid during the occultation process.

[0067] The right ascension and declination (α, δ) of the X-ray source of the observed target celestial body are converted into Cartesian coordinates (x source ,y source ,z source )Unit vector:

[0068]

[0069] By satellite orbit position vector and the unit vector of the X-ray source position of the observed target celestial body Using the law of cosines, we can obtain the angle θ and its complementary angle β between the satellite orbital position and the X-ray source of the observed target celestial body:

[0070]

[0071] sin(β)=sin(π-θ)=sin(θ)

[0072] The tangent point height Z′ can be calculated by the following formula:

[0073] Z′=r·sin(β)-R E

[0074] Where R E is the radius of the earth, and r is the distance from the orbital position to the center of the earth, which can be expressed by the following formula:

[0075]

[0076] Step 105: Obtain the atmospheric density basis function based on the geographical longitude and latitude coordinates of the tangent point "sub-satellite point" and the tangent point height grid point obtained in step 104. The atmospheric density basis function can be obtained through the NRLMSIS 2.0 model.

[0077] Step 106: Based on the X-ray cross sections of the atmospheric components obtained in step 103 and the atmospheric density basis functions obtained in step 105, and in combination with the unattenuated energy spectrum of the astronomical X-ray source obtained by Xspec fitting, a forward model of the X-ray energy spectrum during the occultation process is constructed:

[0078] The energy spectrum model in the occultation process is created based on the X-ray cross section calculated from the XCOM database, the unattenuated energy spectrum of the X-ray source of the observed target celestial body obtained by Xspec fitting, and the basis function obtained through the NRLMSIS 2.0 atmospheric model:

[0079] FM(E,h)=RI0(E)e -τ(E,h) +B

[0080] Where R is the detector response matrix, each row of which represents the probability density distribution of photons within a certain incident energy range being detected on each electronic channel; B is the background noise parameter, which needs to be fitted, with a value range of 0-20 and a unit of counts / s; I0(E) is the unattenuated energy spectrum of the X-ray source of the observed target celestial body obtained by fitting the measured data based on Xspec; τ(E,h) is the optical thickness, which represents the transmittance level of the atmosphere in a certain line of sight direction to X-ray photons of a certain energy band, which can be expressed by the following formula:

[0081]

[0082] Where s represents the composition of the atmosphere, including oxygen atoms (O), oxygen molecules (O2), nitrogen atoms (N), nitrogen molecules (N2) and argon atoms (Ar); l0 is the position coordinate of the X-ray satellite; σ s (E) is the X-ray cross section of each component in the atmosphere; n s (h) is the basis function of each atmospheric component at the tangent point height in the line of sight direction provided by the NRLMSIS 2.0 model.

[0083] In order to invert the atmospheric density, we introduce a correction factor γ and use it to correct the basis function n of each atmospheric component at the tangent point height in the line of sight direction. s (h), the correction factor γ is a dimensionless quantity with a parameter range of 0-10, so the expression of optical thickness τ(E,h) can be expressed as:

[0084]

[0085] The energy spectrum model formula has two dimensions: energy and tangent point height. The energy spectrum modeling during the occultation can be achieved by summing the tangent point heights.

[0086] Step 107: constructing a likelihood function between the hypothesis and the data under the Poisson distribution based on the energy spectrum occultation data obtained in step 102 and the X-ray energy spectrum model during the occultation process obtained in step 106;

[0087] The X-ray photon counts conform to the Poisson distribution, and the likelihood function is constructed based on the statistical relationship between the hypothesis and the data under the Poisson distribution; the Poisson distribution is shown as follows:

[0088]

[0089] Among them, D i represents the i-th data point, M i represents the count of the ith model; for the convenience of calculation, the continuous multiplication of the above formula is changed into continuous addition, and the following operations are performed on the above formula: ① take the natural logarithm, ② change its sign, ③ remove the factorial term, ④ multiply by 2, and obtain the likelihood function between the hypothesis and the data under the Poisson distribution:

[0090]

[0091] Step 108: Using the likelihood function between the hypothesis and the data under the Poisson distribution constructed in step 107, the inversion of the atmospheric density in the space near the Earth is realized. Using the likelihood function between the hypothesis and the data under the Poisson distribution constructed in step 107, the atmospheric density in the space near the Earth is calculated based on the Bayesian theorem using the Bayesian estimation and the Markov Chain Monte Carlo (MCMC) algorithm and its uncertainty is calculated.

[0092] First of all, the implementation of the Markov Chain Monte Carlo (MCMC) algorithm should be based on Bayes' theorem.

[0093]

[0094] Wherein, Θ={γ,B}; P(Θ|D,M) represents the posterior probability distribution of parameter Θ; P(Θ|M) represents the prior information of the parameter; P(D|Θ,M) represents the likelihood function between hypothesis M and data D, which is calculated by step 107; P(D|M) represents Bayesian evidence, also known as the normalization constant. In parameter estimation, we are only interested in the parameter value, and Bayesian evidence has no effect on the acquisition of the parameter value. Therefore, the posterior probability is proportional to the product of the likelihood function and the prior information:

[0095] P(Θ|D,M)∝P(Θ|M)P(D|Θ,M)

[0096] Where P(Θ|M)P(D|Θ,M) represents the total probability density distribution function. For the convenience of calculation, the multiplication operation in the above formula is converted into an addition operation, and the logarithm of both ends of the above formula is taken at the same time. After taking the logarithm, the nature and correlation of the data will not be changed:

[0097] log(P(Θ|D,M))∝log(P(Θ|M))+log(P(D|Θ,M))

[0098] The log-likelihood function log(P(D|Θ,M)) is given by step 107.

[0099] Assuming that the parameter Θ satisfies a uniform distribution, according to the parameter range of parameter Θ={γ,B}, the prior information of the parameter is given:

[0100]

[0101] The logarithmic form of the parameter prior information is as follows:

[0102]

[0103] Then, random sampling is performed on Θ={γ, B} in the parameter space, specifically implemented by the Python package emcee (reference: Foreman-Mackey, D., Hogg, DW, Lang, D., and Goodman, J.: emcee: The MCMC Hammer, Publications of the Astronomical Society of 500 the Pacific, 125, 306, https: / / doi.org / 10.1086 / 670067, 2013). The posterior probability distribution of the parameter Θ calculated by emcee requires the number of Markov chains (nwalker), the number of sampling steps (nsteps), the number of parameters (ndim), and the step length (a). In the present invention, the parameters are selected according to the following conditions: nwalker=10, nsteps=10000, ndim=2, a=0.01, and the Python package corner is used (reference: Foreman-Mackey, D.corner.py: Scatterplot matrices in Python.J OpenSource Softw.2016) draws a graph to obtain the posterior probability density distribution of parameter Θ = {γ, B}, such as Figure 4 As shown, the value of parameter Θ = {γ, B} is obtained.

[0104] Thus, the inversion result of the Earth's atmospheric density is calculated.

[0105]

[0106] Among them, γ is the correction factor parameter obtained by inversion, n s (h) is the basis function of each atmospheric component in the line of sight direction at the tangent point height h given by NRLMSIS 2.0.

[0107] The present invention has opened up a new field for the tomographic scanning detection of atmospheric density in the Earth's near-space based on X-ray occultation. By processing the occultation data in the X-ray energy band and performing energy spectrum modeling during the occultation process, the atmospheric density in the Earth's near-space within a range of about 50-250km can be measured, which makes up for the shortage of measured atmospheric density data in this range. In addition, X-ray photons directly interact with K-layer and L-layer electrons inside atoms and are not affected by atmospheric chemistry, thermodynamics, etc. This method can directly invert the neutral density of the atmosphere in the Earth's near-space.

[0108] The present invention may also provide a computer device, comprising: at least one processor, a memory, at least one network interface and a user interface. The various components in the device are coupled together through a bus system. It is understood that the bus system is used to achieve connection and communication between these components. In addition to the data bus, the bus system also includes a power bus, a control bus and a status signal bus.

[0109] The user interface may include a display, a keyboard, or a pointing device (eg, a mouse, a trackball, a touch pad, or a touch screen).

[0110] It is understood that the memory in the disclosed embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus random access memory (DRRAM). The memories described herein are intended to include, but are not limited to, these and any other suitable types of memories.

[0111] In some embodiments, the memory stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system and applications.

[0112] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. The program for implementing the method of the embodiment of the present disclosure can be included in the application.

[0113] In the above embodiment, the processor may also be used for the steps of the method of the present invention by calling a program or instruction stored in a memory, specifically, a program or instruction stored in an application.

[0114] The method of the present invention 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, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor may be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in Example 1 can be implemented or executed. 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 Example 1 can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a 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 a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0115] It is understood that the embodiments described in the present invention can be implemented by hardware, software, firmware, middleware, microcode or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application or a combination thereof.

[0116] For software implementation, the technology of the present invention can be implemented by executing the functional modules (such as procedures, functions, etc.) of the present invention. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0117] The present invention may also provide a non-volatile storage medium for storing a computer program. When the computer program is executed by a processor, each step in the above method embodiment can be implemented.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention is described in detail with reference to the embodiments, it should be understood by those skilled in the art that any modification or equivalent replacement of the technical solutions of the present invention does not depart from the spirit and scope of the technical solutions of the present invention and should be included in the scope of the claims of the present invention.

Claims

1. An atmospheric density tomography scanning detection method, comprising: Step (101), selecting an astronomical X-ray source with high stability as an observation target, and obtaining the orbital position coordinates and detector response matrix of the X-ray satellite; Step (102), selecting exposure time and energy grid points based on the orbital position coordinates determined in step (101), processing the X-ray satellite occultation Level 1 data, and extracting energy spectrum occultation data with high signal-to-noise ratio and high vertical resolution; Step (103), based on the energy grid points obtained in step (102), obtaining the X-ray cross sections of each component of the atmosphere; Step (104), based on the vertical resolution energy spectrum occultation data obtained in step (102), calculating the coordinates of the tangent point between the X-ray satellite line of sight and the atmospheric layer during the occultation process and the geographical longitude and latitude coordinates of the tangent point "sub-satellite point", and calculating the tangent point height grid points during the occultation process; Step (105), based on the geographical longitude and latitude coordinates of the tangent point "sub-satellite point" obtained in step (104) and the tangent point height grid point, obtain the atmospheric density basis function; Step (106), based on the X-ray cross sections of the atmospheric components obtained in step (103) and the atmospheric density basis functions obtained in step (105), combined with the unattenuated energy spectrum of the astronomical X-ray source obtained by Xspec fitting, construct a forward model of the X-ray energy spectrum during the occultation process; Step (107), based on the energy spectrum occultation data obtained in step (102) and the forward model of the X-ray energy spectrum during the occultation process obtained in step (106), construct a likelihood function between the hypothesis and the data under the Poisson distribution; Step (108), using the likelihood function between the hypothesis and the data under the Poisson distribution constructed in step (107), to achieve the inversion of the atmospheric density in the space near the earth; The step (101) is: selecting an astronomical X-ray source with high stability as an observation target, performing a Lagrangian interpolation operation on the orbital position of the X-ray satellite during the occultation period to obtain orbital position coordinates with higher resolution; and multiplying the corresponding elements of the auxiliary response file ARF and the redistribution matrix file RMF of the calibration database provided by the X-ray satellite to obtain the detector response matrix R: R(E′,E)=ARF(E)×RMF(E′,E) Among them, E' is the detector output energy grid, E is the incident photon energy grid, and is also the input energy channel of the detector response matrix; The step (104) is specifically as follows: First, the right ascension and declination (α, δ) of the X-ray source of the observed target celestial body are converted into Cartesian coordinates (x source ,y source ,z source )Unit vector: By satellite orbit position vector and the unit vector of the X-ray source position of the observed target celestial body Using the law of cosines, we can obtain the angle θ and its complementary angle β between the satellite orbital position and the X-ray source of the observed target celestial body: sin(β)=sin(π-θ)=sin(θ) Next, find the tangent point height Z': Z'=r·sin(β)-R E Among them, R E is the radius of the earth; r is the distance from the X-ray satellite orbital position to the center of the earth, which is calculated by the following formula: The step (106) is: Based on the X-ray cross section calculated from the XCOM database and the unattenuated energy spectrum of the X-ray source of the observed target celestial body obtained by Xspec fitting, the energy spectrum model formula in the occultation process is created through the basis function obtained from the NRLMSIS2.0 atmospheric model: FM(E,h)=RI0(E)e -τ(E,h) +B Where R is the detector response matrix, each row of which represents the probability density distribution of photons within a certain incident energy range being detected on each electronic channel; B is the background noise parameter, ranging from 0 to 20, in counts / s; I0(E) is the unattenuated energy spectrum of the X-ray source of the observed target celestial body obtained by fitting the measured data based on Xspec; τ(E,h) is the optical thickness, which represents the transmittance level of the atmosphere in a certain line of sight direction to X-ray photons of a certain energy band, calculated by the following formula: Where s represents the composition of the atmosphere, including oxygen atoms (O), oxygen molecules (O2), nitrogen atoms (N), nitrogen molecules (N2) and argon atoms (Ar); l0 is the position coordinate of the X-ray satellite; σ s (E) is the X-ray cross section of each component in the atmosphere; n s (h) is the basis function of each atmospheric component at the tangent point height in the line of sight direction provided by the NRLMSIS 2.0 model; γ is the correction factor, ranging from 0 to 10; The energy spectrum model formula has two dimensions: energy and tangent point height. The tangent point height is summed to achieve energy spectrum modeling during the occultation period. The step (107) is: the X-ray photon count conforms to the Poisson distribution, and a likelihood function is constructed based on the statistical relationship between the hypothesis and the data under the Poisson distribution; the Poisson distribution is shown in the following formula: C=2∑(-D i logM i +M i ) Among them, D i represents the i-th data point, M i represents the i-th model count; The step (108) is: using the likelihood function between the hypothesis and the data under the Poisson distribution constructed in step (107), based on Bayesian estimation, using the Markov Chain Monte Carlo (MCMC) algorithm to achieve the inversion of the atmospheric density in the space near the Earth, and the calculation formula is: n(h)=∑γ·n s (h) Among them, γ is the correction factor, n s (h) is the basis function of each atmospheric component at the tangent point height h in the line of sight direction given by NRLMSIS2.

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2. The atmospheric density tomography detection method according to claim 1, characterized in that: The step (102) is: based on the orbital position coordinates determined in step (101), an exposure time is selected so that the vertical resolution of the altitude space is less than 1 km, an energy grid is determined according to the energy channel input by the detector response matrix, and the occultation Level 1 data of the X-ray satellite is processed to extract energy spectrum occultation data with high signal-to-noise ratio and high vertical resolution.

3. The atmospheric density tomography detection method according to claim 1, characterized in that: The step (105) is: based on the geographical longitude and latitude coordinates of the tangent point "sub-satellite point" obtained in step (104) and the tangent point height grid point, the atmospheric density basis function is obtained through the NRLMSIS2.0 model.

4. A computer 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 computer program, the method according to claim 1 is implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the method of claim 1 .

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