A design method for constellation orbit inclination for atmospheric occultation detection

Abstract

The present invention relates to the field of occultation detection technology, and discloses a design method for the orbital inclination of an atmospheric occultation detection constellation, wherein the atmospheric occultation detection constellation is composed of 70 or more low-orbit satellites; three design indicators are set: indicator 1 is the atmospheric occultation event grid coverage rate used to characterize the global atmospheric occultation event coverage performance, indicator 2 is the standard deviation of the atmospheric occultation events in the middle and low latitudes used to characterize the uniformity of the distribution of atmospheric occultation events in the middle and low latitudes, and indicator 3 is the density distribution of atmospheric occultation events in the Chinese region used to characterize the density of atmospheric occultation events in the Chinese region; receiving simulation data: receiving the global atmospheric occultation event distribution latitude and longitude simulation data under different orbital inclinations; calculating the indicator results; calculating the comprehensive indicator d; comparing the comprehensive indicators d corresponding to all orbital inclinations, and taking the orbital inclination corresponding to the smallest comprehensive indicator d as the optimal inclination. The orbital inclination designed by the present invention optimizes the constellation detection efficiency.

Description

Technical Field

[0001] The invention relates to the technical field of occultation detection, and in particular to a method for designing a constellation orbit inclination for atmospheric occultation detection. Background Art

[0002] Occultation detection technology is considered to be one of the most promising means of atmospheric detection. It can provide globally uniformly distributed neutral atmosphere and ionosphere information around the clock. It has the advantages of high vertical resolution, long-term stability, no need for calibration, global coverage, and all-weather. Its detection data is of great significance in numerical weather forecasting, global climate monitoring, and space weather forecasting, especially in improving the accuracy of numerical weather forecasting. The European Centre for Mesoscale Numerical Weather Forecasting (ECMWF) analysis shows that if 3% of the initial field data for global weather forecasts comes from occultation detection data, the contribution rate to the reduction of the error of numerical weather forecasts is 8%.

[0003] The World Meteorological Organization requires a minimum temporal and spatial resolution of 6 hours and 500 km x 500 km for the temperature parameters of global numerical weather forecasts. To meet the above requirements, it is necessary to launch nearly 100 low-orbit satellites equipped with occultation detectors for atmospheric detection. But in reality, this number is far more than what most countries have now. For example, my country currently has only a few satellites equipped with occultation detectors, which does not meet the above-mentioned temporal and spatial coverage requirements. Internationally, only the United States has shown a rapid development trend in the launch of occultation satellite constellations. For example, the US SPIRE company has launched a Lemur constellation consisting of more than 110 low-orbit small satellites into space, but its main function is only about 60 micro satellites for meteorological occultation detection.

[0004] At present, my country has become very mature in the design of single or multiple meteorological satellites, such as the Fengyun series of satellites. However, there are deficiencies in the design of large-scale occultation constellations, especially atmospheric occultation detection constellations used for meteorological occultation detection. Summary of the invention

[0005] The present invention aims to provide a method for designing the orbital inclination of an atmospheric occultation detection constellation, so as to optimize the detection efficiency of the atmospheric occultation detection constellation formed by multiple low-orbit satellites.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme:

[0007] A method for designing the orbital inclination of an atmospheric occultation detection constellation, wherein the atmospheric occultation detection constellation is composed of 70 or more low-orbit satellites; the method comprises the following steps:

[0008] Step 1: Set three design indicators: Indicator 1 is the atmospheric occultation event grid coverage rate used to characterize the global atmospheric occultation event coverage performance; Indicator 2 is the standard deviation of the atmospheric occultation events in the mid- and low-latitudes used to characterize the uniformity of the distribution of atmospheric occultation events in the mid- and low-latitudes; Indicator 3 is the density distribution of atmospheric occultation events in the Chinese region used to characterize the density of atmospheric occultation events in the Chinese region;

[0009] Step 2, receiving simulation data: receiving the longitude and latitude simulation data of the global atmospheric occultation event distribution under different orbital inclinations;

[0010] Step 3: Calculate the index results: For the global atmospheric occultation event distribution latitude and longitude simulation data corresponding to each orbital inclination, calculate index 1, index 2 and index 3 in turn, and normalize the corresponding calculation results to obtain the index 1 result. , Indicator 2 Results And indicator 3 results ;

[0011] Step 4: Calculate the comprehensive index results: For each orbital inclination, according to the preset optimal index ( , , ), corresponding to the three index results, calculate the comprehensive index d:

[0012]

[0013] in, , , They are respectively the optimal index 1, the optimal index 2, and the optimal index 3 corresponding to each preset orbital inclination;

[0014] Step 5, obtain the optimal inclination: compare the comprehensive index d corresponding to all orbital inclinations, and take the orbital inclination corresponding to the smallest comprehensive index d as the optimal inclination.

[0015] Preferably, the content of calculating the grid coverage rate of atmospheric occultation events of indicator 1 is as follows: the world is divided into 200km×200km grids, and the number of occultation events in the grids is counted. If the number of occultation events in the grid is greater than 0, it means that the grid is covered. If the number in the grid is less than 0, it means that the grid is not covered. The global occultation event grid coverage rates at different orbital inclinations are counted according to the following formula:

[0016] Global occultation event grid coverage = number of covered grids / total number of grids in the world.

[0017] Preferably, the standard deviation of low-latitude atmospheric occultation events in indicator 2 is calculated as follows:

[0018] The angle range between the two orbital inclinations corresponding to the middle and low latitudes is divided into latitude bands at intervals of 2 degrees, and the density distribution of atmospheric occultation events in each latitude band is counted.

[0019] Density distribution of atmospheric occultation events in the latitude belt = total number of atmospheric occultation events in the latitude belt / area of ​​the latitude belt;

[0020] Then, the standard deviation S of atmospheric occultation events at mid- and low-latitudes at different orbital inclinations is calculated according to the following formula:

[0021]

[0022] in, is the total number of occultation events in each latitude band in the mid- and low-latitude range, is the number of latitude bands in the mid- and low-latitude range, It is the average value of occultation events in all latitudes in the mid- and low-latitude range.

[0023] Preferably, the calculation content of the density distribution of atmospheric occultation events in the Chinese region of indicator 3 is as follows:

[0024] Divide China into 200km 200km grid, statistics for 200km The density distribution of atmospheric occultation events within a 200km grid, and the spatial distribution average of the atmospheric occultation event density at different orbital inclinations are calculated:

[0025] China area 200km The density distribution of atmospheric occultation events within a 200km grid = the total number of atmospheric occultation events within the grid / grid area;

[0026] The average spatial distribution of atmospheric occultation event density in China = 200 km in China The sum of the density of atmospheric occultation events within the 200km grid / the total number of grids in the Chinese region.

[0027] Preferably, the optimal indicator ( , , ) are (1, 0, 1) respectively.

[0028] Preferably, the low and middle latitudes refer to the latitude range between an orbital inclination of -6 degrees and an orbital inclination of +6 degrees.

[0029] The advantages of this solution are:

[0030] There are many parameters that affect the detection performance of constellations. Compared with other parameters, the design of orbital inclination has a huge impact on detection performance. The choice of orbital inclination will directly affect the coverage and spatial distribution of occultation detection. Therefore, it is urgent to design a reasonable solution for the orbital inclination of large-scale atmospheric occultation detection constellations to optimize their detection performance.

[0031] The present invention cleverly sets three design indicators, focuses on the three goals of global atmospheric occultation event coverage, uniform distribution of atmospheric occultation events in mid- and low-latitudes, and density of atmospheric occultation events in China, designs three corresponding indicators, and establishes a comprehensive indicator through these three indicators. Through comparison of the comprehensive indicators, the optimal orbital inclination that optimizes its detection efficiency can be quickly found in a large-scale atmospheric occultation detection constellation consisting of more than 70 satellites.

[0032] The present invention can quickly complete the design of the optimal orbital inclination with minimal calculations and only on the basis of simulation data, and optimize the actual detection efficiency after real operation.

[0033] Compared with the small-scale atmospheric occultation detection constellation, the orbital inclination angle designed by this design method can ensure the best arrangement when the total number of satellites with meteorological detection functions is the largest, which will be more suitable for meteorological detection needs. From the perspective of closely fitting the needs of meteorological applications, the inclination angle design obtained by this design method realizes the global 200km×200km×6 hours of atmospheric and ocean data needs, and the occultation detection is relatively dense in the middle and low latitudes, which can focus on the densely populated areas where humans live. While meeting the real-time meteorological detection needs of the main human activity areas, the number of satellites is minimized, cost is saved, and maintenance and replacement are more convenient to operate. It can improve the reliability and stability of the entire detection constellation, and help to reduce costs while improving detection efficiency, effectively filling the gap in the design of large-scale atmospheric occultation detection constellations in China.

[0034] In addition, the comprehensive indicators and the optimal indicators can be adjusted according to actual needs, so that the inclination angle that meets the corresponding needs can be quickly obtained, so that the detection efficiency of the constellation is optimized and the actual detection needs can be better met. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 is a flow chart of an embodiment of the present invention.

[0036] Figure 2 This is a graph of the daily average number of occultation events within a 200*200 km grid when the inclination is 50°.

[0037] Figure 3 This is the distribution diagram of daily average occultation events along the latitude belt when the inclination angle is 50°.

[0038] Figure 4This is the distribution diagram of daily average occultation events along the latitude belt when the inclination angle is 25°.

[0039] Figure 5 This is the distribution diagram of daily average occultation events along the latitude belt when the inclination is 41°.

[0040] Figure 6 This is the distribution diagram of daily average occultation events along the latitude belt when the inclination is 42°.

[0041] Figure 7 Select schematic diagram for low and mid-latitude regions. DETAILED DESCRIPTION

[0042] The following is further described in detail through specific implementation methods:

[0043] Embodiment 1

[0044] The embodiment is basically as shown in the attached Figure 1 As shown: A design method for the orbital inclination of an atmospheric occultation detection constellation, wherein the atmospheric occultation detection constellation is composed of 70 or more low-orbit satellites; comprising the following steps:

[0045] S1, simulation results of global atmospheric occultation events at different inclinations, focusing on the following design goals:

[0046] Objective 1: Global coverage of atmospheric occultation events;

[0047] Goal 2: Even distribution of atmospheric occultation events in low and mid-latitudes;

[0048] Objective 3: the density of atmospheric occultation events in China;

[0049] Among them, this embodiment particularly imposes special restrictions on mid- and low-latitudes, and this embodiment refers to [orbital inclination -6°, orbital inclination +6°].

[0050] S2, for the above purposes, the corresponding indicators are designed as follows:

[0051] Indicator 1: Grid coverage of atmospheric occultation events:

[0052] The world is divided into 200km×200km grids, and the number of occultation events in the grids is counted. If the number of occultation events in the grid is greater than 0, it means that the grid is covered. If the number in the grid is less than 0, it means that the grid is not covered. The global occultation event grid coverage rate at different inclination angles is counted separately.

[0053] Global occultation event grid coverage = number of covered grids / total number of grids in the world.

[0054] Standard deviation of low-latitude atmospheric occultation events in indicator 2:

[0055] The low and middle latitudes (here set to [orbital inclination -6°, orbital inclination +6°]) are divided into latitude bands at intervals of 2 degrees, and the density distribution of atmospheric occultation events on the latitude bands is counted. Then, the standard deviation of atmospheric occultation events in the low and middle latitudes at different inclinations is counted respectively.

[0056] Density distribution of atmospheric occultation events in the latitude belt = total number of atmospheric occultation events in the latitude belt / area of ​​the latitude belt

[0057] Standard deviation of atmospheric occultation events in mid- and low-latitudes:

[0058]

[0059] in, is the total number of occultation events in each latitude band in the mid- and low-latitude range, is the number of latitude bands in the mid- and low-latitude range, It is the average value of occultation events in all latitudes in the mid- and low-latitude range.

[0060] Indicator 3: Density distribution of atmospheric occultation events in China:

[0061] The Chinese region is divided into a rectangular area on the earth's surface between 97 and 121 degrees east longitude and 18 and 53 degrees north latitude, which is 200 km 200km grid, statistics for 200km The density distribution of atmospheric occultation events within a 200km grid is calculated, and the spatial distribution average of the density of atmospheric occultation events at different inclination angles is calculated.

[0062] China area 200km The density distribution of atmospheric occultation events within a 200 km grid = the total number of atmospheric occultation events within the grid / grid area

[0063] The average spatial distribution of atmospheric occultation event density in China = 200 km in China The sum of the density of atmospheric occultation events within the 200km grid / the total number of grids in China.

[0064] S3, comprehensive indicators

[0065] Different indicators may result in different optimal orbital inclinations. The above three indicators are comprehensively considered for different orbital inclinations, and a comprehensive indicator is set to find the optimal orbital inclination.

[0066] Comprehensive indicators:

[0067]

[0068] in, , , are normalized index 1, index 2, and index 3, , , are the ideal optimal indicators (1, 0, 1 respectively)

[0069] S4, the orbital inclination corresponding to the smallest comprehensive index is the optimal inclination.

[0070] This method focuses on the three goals of global atmospheric occultation event coverage, uniform distribution of atmospheric occultation events in mid- and low-latitudes, and density of atmospheric occultation events in China, and designs three corresponding indicators. A comprehensive indicator is established through these three indicators to find the optimal orbital inclination for large-scale atmospheric occultation detection constellations.

[0071] This method adopts a unique multi-objective-oriented design, which introduces a multi-objective-oriented design by focusing on three key goals: global atmospheric occultation event coverage performance, uniform distribution of atmospheric occultation events in mid- and low-latitudes, and the density of atmospheric occultation events in China. This means that it not only pursues the optimization of one performance indicator, but also focuses on multiple performance indicators at the same time to achieve more comprehensive performance improvement in the design.

[0072] This method has created a unique index-based evaluation system. By introducing three specific performance indicators and establishing a comprehensive index, this technology introduces an index-based evaluation system for quantitatively evaluating the performance of atmospheric occultation detection constellations. This evaluation system can provide objective standards for comparing and selecting the best design solutions.

[0073] This method obtains the optimal design through comprehensive performance judgment. Finding the optimal orbital inclination for a large-scale atmospheric occultation detection constellation is an innovation of this technology. This means not only determining an optimal design, but also determining the orbital inclination that is most suitable for achieving this goal. This comprehensive performance optimization can improve the efficiency of atmospheric occultation detection and the uniformity of low and medium latitudes.

[0074] This method can be applied globally, and its focus is not limited to a specific geographical area, but on the global scale, which is an innovation. This helps to ensure that the technology can provide reliable atmospheric occultation data as much as possible around the world, thus meeting the research and application needs of the world.

[0075] In addition, this method places special emphasis on the need to cover the territory of China, which shows a high level of attention to domestic needs. This priority of domestic needs helps to meet the requirements of domestic scientific research and application projects and provide more useful data support for domestic users.

[0076] In summary, this method provides a more comprehensive and accurate method and solution for the design and performance of atmospheric occultation detection constellations through innovative points such as multi-objective-oriented design, index-based evaluation, comprehensive performance optimization, global application and domestic demand priority, so that the coverage and spatial distribution of occultation detection can be optimized. This will help promote the development and application of atmospheric occultation technology.

[0077] In order to prove the superiority of this method, the following comparative examples are specially set:

[0078] Comparative Example 1: Supplementing the single-target single-indicator setting (only indicator 1), the corresponding detection performance is demonstrated, proving that the detection performance of this scheme is the best.

[0079] If we only pursue indicator 1, the grid coverage of global atmospheric occultation events, the larger the inclination angle, the higher the grid coverage and the better the detection efficiency. However, the average number of daily occultation events in a 200×200km grid ( Figure 2 ) It can be seen that when the inclination is large, the distribution of occultation events in the mid- and low-latitude regions is uneven, and an obvious low-value band of occultation events appears in the equatorial region. From the frequency distribution histogram of occultation events at an inclination of 50° ( Figure 3 ) can also be seen that there are fewer occultation events in the equatorial region, more occultation events near 50°, and the distribution of occultation events in mid- and low-latitudes is extremely uneven.

[0080] Comparative Example 2: Supplement the single-target single-indicator setting (only indicator 2), and the corresponding detection performance is demonstrated to prove that the detection performance of this solution is the best.

[0081] If we only pursue the uniform distribution of atmospheric occultation events in low-latitudes in indicator 2, the detection efficiency is optimal when the inclination angle is 25° (Table 1). However, from the simulation visualization results ( Figure 4 ), does not cover the Chinese region.

[0082] Table 1 Standard deviation of mid- and low-latitude event density at different inclinations

[0083]

[0084] Comparative Example 3: Supplement the single-target single-indicator setting (only indicator 3), and the corresponding detection performance display, proving that the detection performance of this solution is the best.

[0085] If we only pursue indicator 3, the maximum density of occultation events in the Chinese region, the detection efficiency is optimal when the inclination is 41° and 42° (Table 2). However, from the simulation visualization results ( Figure 5 and Figure 6 ), the distribution of occultation events in mid- and low-latitudes is relatively uneven, and the number of occultation events in the equatorial region is relatively small.

[0086] Table 2 Standard deviation of mid- and low-latitude event density at different inclinations

[0087]

[0088] #Note: The unit of occultation event density is times / (200*200km), i.e. the number of occultation events within a grid area of ​​200*200km.

[0089] Comparative Example 4: The latitude range of indicator 2 is changed (various), and the corresponding detection performance is displayed, proving that the detection performance of this solution is the best.

[0090] like Figure 7 As shown in the figure, the middle and low latitudes in this scheme are not the middle and low latitudes in meteorology. Here, it refers to the latitude interval where occultation events are concentrated (the area between the red lines). Generally, in addition to the subsatellite latitude interval (the area between the blue lines), there are also many occultation events near the subsatellite latitude interval (the area between the red and blue lines). Research and analysis show that if the subsatellite latitude interval expands by 6° (that is, the area is [orbital inclination -6°, orbital inclination +6°]), the density of occultation events in the area is about 50% of the density of the subsatellite latitude interval; if the subsatellite latitude interval expands by 8° (that is, the area is [orbital inclination -8°, orbital inclination +8°]), the density of occultation events in the area is about 50% of the density of the subsatellite latitude interval. The density of occultation events accounts for about 30% of the density of the subsatellite latitude interval; if the subsatellite latitude interval is expanded by 4° (that is, the area is [orbital inclination -4°, orbital inclination +4°]), the density of occultation events in the area accounts for about 70% of the density of the subsatellite latitude interval. If 4° is selected, some areas with dense occultation events will be lost. If 8° is selected, some areas with sparse occultation events will be added. Therefore, it is most appropriate to select the range of [orbital inclination -6°, orbital inclination +6°] as the mid- and low-latitudes.

[0091] Among them, the sub-satellite point latitude interval refers to the latitude range of the sub-satellite point in a specific area on the surface of the earth. The sub-satellite point refers to the projection point of the satellite on the ground, and its latitude is relative to the center of the earth. Therefore, the sub-satellite point latitude interval can be used to describe the coverage of the satellite in a specific area, which is of great significance for satellite communication, navigation, observation and other applications. Specifically, the calculation of the sub-satellite point latitude interval needs to consider factors such as the orbital parameters of the satellite, the ellipsoid shape of the earth, and the rotation of the earth. In some applications, in order to improve the coverage effect or reduce signal interference, it may be necessary to select a specific sub-satellite point latitude interval. It should be noted that due to the influence of factors such as the rotation of the earth and the orbital parameters of the satellite, the sub-satellite point latitude interval is not a fixed value, but changes with time and the position of the satellite. Therefore, in practical applications, it is necessary to calculate and select according to the specific situation.

[0092] This scheme can greatly shorten the time required to determine the latitude range of the sub-satellite point required for detection by reasonably setting the orbital inclination range, and can quickly and accurately provide the appropriate working angle for occultation detection within a limited time.

[0093] Comparative Example 5: Changing the grid division of indicator 1 (multiple) and the corresponding detection performance display proves that the detection performance of this scheme is the best.

[0094] Dividing the earth into 200km×200km grids not only meets the resolution requirements of atmospheric and ocean data detection published by the World Meteorological Organization (WMO), but also fully considers the shape of the earth. Since the earth is an ellipse, if the earth is divided into 2°×2° small grids according to degrees, the area of ​​the 2°×2° small grid in the equatorial region is much larger than the area of ​​the 2°×2° small grid in the high latitude region. Dividing the earth into small grids at intervals of 200km fully considers that the areas of the small grids are equal. If the grid division area is too small, there will be over-division, which will waste computing time.

[0095] The grid division of this scheme will be most suitable for atmospheric detection in the case of large-scale (more than 70) satellites, and will have the best performance.

[0096] In addition, by comparing different grid divisions, it can be proved that the division in this scheme is optimal. By comparing the meteorological detection occultation with fixed orbital inclination, this scheme has the best detection efficiency when the number of such satellites is large and the orbital inclination of each occultation detection satellite is [orbital inclination -6°, orbital inclination +6°].

[0097] Embodiment 2

[0098] In this embodiment, for the step-by-step large-scale constellation configuration optimization design for multiple target areas, the orbital inclinations in different target areas are adjustable after setting. Specifically,

[0099] Establishing a constellation optimization model for multiple target areas according to mission requirements, and setting optimization targets, optimization indicators and comprehensive indicators of the constellation optimization model;

[0100] For the simulation results of global atmospheric occultation events at different inclinations, the following design goals are focused on: 1) global atmospheric occultation event coverage performance; 2) uniform distribution of atmospheric occultation events in mid- and low-latitudes; 3) density of atmospheric occultation events in China. For the above purposes, the corresponding indicators are designed as follows: Indicator 1) atmospheric occultation event grid coverage; Indicator 2) standard deviation of atmospheric occultation events in mid- and low-latitudes; Indicator 3) density distribution of occultation events in China. Taking the three indicators into consideration, a comprehensive indicator with the Euclidean distance algorithm as the core is designed.

[0101] If you need to pay attention to other points besides the above three design goals in the later stage, you can add indicators to the indicators accordingly, and add a dimension when calculating the comprehensive indicators. For example, how much detection data the user pays attention to in the northwest Pacific region. You can add indicator 4) the number of occultation events in the northwest Pacific region to the indicators, and add a latitude to the comprehensive indicators.

[0102]

[0103] in, , , , are normalized indicators 1, 2, 3, and 4. , , , are the ideal optimal indicators (respectively 1, 0, 1, 1, because the normalized indicator values ​​are all in [0,1], either the optimal indicator is 1 or 0, whether it is 0 or 1 depends on the indicator definition. If the indicator calculates the standard deviation, the smaller the standard deviation, the better, so the optimal is 0; if the indicator calculates the total number of occultation events or the density of occultation events, the larger the better, so the optimal is 1). Here, the Euclidean distance represents the comprehensive indicator. The smaller its value, the better the corresponding detection efficiency.

[0104] If you only focus on two of the above three design goals in the later design, you can delete the indicators accordingly in the indicators, and reduce one dimension when calculating the comprehensive indicators.

[0105]

[0106] in, , are normalized index 1 and index 2, , It is the ideal optimal indicator.

[0107] Embodiment 3

[0108] When increasing or decreasing indicators, judgments are made through the preset indicator judgment strategy. The indicator judgment strategy dynamically compares and recommends corresponding relationships based on the continuously collected design data and actual feedback data, and then makes dynamic adjustments. Specifically, through methods such as keyword tagging, the design data (including purpose data and indicator data) and actual feedback data are compared and a relationship is established. The actual feedback data and the purpose data are compared and judged, and the indicator data corresponding to the actual feedback data with a similarity of more than 85% is marked to form positive indicator data, and the indicator data corresponding to the actual feedback data with a similarity of less than 35% is marked to form negative indicator data.

[0109] In a unit of time, the positive indicator data and negative indicator data are counted. First, the negative indicator data with the greatest negative impact is removed. After more than 10-20 simulation tests and more than 3-6 actual feedbacks, it is proved that removing the negative indicator data is beneficial to improving the design effect. Then, the negative indicator data is removed from the current indicator data. Then, when adding positive indicator data, it is necessary to conduct more than 6-10 simulation tests and more than 1-3 actual feedbacks. After proving that adding the positive indicator data is beneficial to improving the design effect, the positive indicator data is added from the current indicator data as a new indicator.

[0110] It should be noted that the length of the unit time is different for specific design requirements. The unit time for general projects is generally 1-2 years, while the unit time for special requirements can be adjusted to 2-3 months.

[0111] This embodiment helps to improve the conformity between design goals and actual feedback data and improve the overall design effect through precise control and adjustment of design indicators.

[0112] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A method for designing the orbital inclination of a constellation for atmospheric occultation detection, characterized in that: The atmospheric occultation detection constellation is composed of 70 or more low-orbit satellites; comprising the following steps: Step 1: Set three design indicators: Indicator 1 is the atmospheric occultation event grid coverage rate used to characterize the global atmospheric occultation event coverage performance; Indicator 2 is the standard deviation of the atmospheric occultation events in the mid- and low-latitudes used to characterize the uniformity of the distribution of atmospheric occultation events in the mid- and low-latitudes; Indicator 3 is the density distribution of atmospheric occultation events in the Chinese region used to characterize the density of atmospheric occultation events in the Chinese region; the event density is the ratio of the total number of atmospheric occultation events in the grid to the grid area; Step 2, receiving simulation data: receiving the longitude and latitude simulation data of the global atmospheric occultation event distribution under different orbital inclinations; Step 3: Calculate the index results: For the global atmospheric occultation event distribution latitude and longitude simulation data corresponding to each orbital inclination, calculate index 1, index 2 and index 3 in turn, and normalize the corresponding calculation results to obtain the index 1 result. , Indicator 2 Results And indicator 3 results ; Step 4: Calculate the comprehensive index results: For each orbital inclination, according to the preset optimal index ( , , ), corresponding to the three index results, calculate the comprehensive index d: in, , , They are respectively the optimal index 1, the optimal index 2, and the optimal index 3 corresponding to each preset orbital inclination; Step 5, obtain the optimal inclination: compare the comprehensive index d corresponding to all orbital inclinations, and take the orbital inclination corresponding to the smallest comprehensive index d as the optimal inclination.

2. The method for designing the orbital inclination of the atmospheric occultation detection constellation according to claim 1, characterized in that: The content of calculating indicator 1, the grid coverage rate of atmospheric occultation events, is as follows: the world is divided into 200km×200km grids, and the number of occultation events in the grid is counted. If the number of occultation events in the grid is greater than 0, it means that the grid is covered. If the number in the grid is less than 0, it means that the grid is not covered. The global occultation event grid coverage rate at different orbital inclinations is counted according to the following formula: Global occultation event grid coverage = number of covered grids / total number of grids in the world.

3. The method for designing the orbital inclination of the atmospheric occultation detection constellation according to claim 1, characterized in that: The standard deviation of low-latitude atmospheric occultation events in metric 2 is calculated as follows: The angle range between the two orbital inclinations corresponding to the middle and low latitudes is divided into latitude bands at intervals of 2 degrees, and the density distribution of atmospheric occultation events in each latitude band is counted. Density distribution of atmospheric occultation events in the latitude belt = total number of atmospheric occultation events in the latitude belt / area of ​​the latitude belt; Then, the standard deviation S of atmospheric occultation events at mid- and low-latitudes at different orbital inclinations is calculated according to the following formula: in, is the total number of occultation events in each latitude band in the mid- and low-latitude range, is the number of latitude bands in the mid- and low-latitude range, It is the average value of occultation events in all latitudes in the mid- and low-latitude range.

4. The method for designing the orbital inclination of a constellation for atmospheric occultation detection according to claim 1, characterized in that: Indicator 3: The density distribution of atmospheric occultation events in China is calculated as follows: Divide China into 200km 200km grid, statistics for 200km The density distribution of atmospheric occultation events within a 200km grid, and the spatial distribution average of the atmospheric occultation event density at different orbital inclinations are calculated: China area 200km The density distribution of atmospheric occultation events within a 200km grid = the total number of atmospheric occultation events within the grid / grid area; The average spatial distribution of atmospheric occultation event density in China = 200 km in China The sum of the density of atmospheric occultation events within the 200km grid / the total number of grids in China.

5. The method for designing the orbital inclination of a constellation for atmospheric occultation detection according to claim 1, characterized in that: The optimal indicator ( , , ) are (1, 0, 1) respectively.

6. The method for designing the orbital inclination of the atmospheric occultation detection constellation according to claim 3, characterized in that: The low and middle latitudes refer to the latitude range between an orbital inclination of -6 degrees and an orbital inclination of +6 degrees.

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