A ground-based observation visibility algorithm and device for space targets taking into account predicted cloud cover

Abstract

The present invention belongs to the field of space target observation technology, and specifically relates to a ground-based observation visibility algorithm and device for space targets that takes into account predicted cloud cover, the algorithm includes using TLE data to obtain space target information; bringing TLE data into the SGP4 model to predict the orbit of the space target; based on the orbit information, considering the influence of the earth's shadow on the space target, judging whether the space target has the conditions to be observed; based on the coordinates of the space target in multiple time periods, using the coordinate conversion matrix, judging whether the space target passes over the ground-based observation station; obtaining the historical cloud cover data over the ground-based observation station, obtaining the cloud cover prediction value at the next moment based on the historical cloud cover data, judging whether the space target can be observed based on the cloud cover prediction value; calculating the magnitude of the space target based on the space target information, judging whether the space target can be observed. The present invention takes into account cloud cover prediction and improves the accuracy of the visibility judgment of the space target.

Description

Technical Field

[0001] The present invention belongs to the technical field of space target observation, and in particular relates to a ground-based observation visibility algorithm and device for space targets taking into account predicted cloud cover. Background Art

[0002] The complexity of the space environment poses a huge challenge to the monitoring of space targets. There are many types of targets in space, including satellites, rocket debris, space debris, etc., and their orbits, speeds, and motion trajectories are different. Ground-based optical observation of space targets is an important means, especially for GEO (geostationary orbit) targets. Compared with space-based observation, ground-based observation is more convenient to use and has lower cost. More importantly, the maintenance difficulty of ground-based observation equipment is much less than that of space-based equipment, which makes ground-based observation more advantageous in actual operation.

[0003] The best time to observe space targets is often around sunrise and sunset, because the sky background is relatively dim and the target is more prominent. However, atmospheric extinction often has a great impact on observation. The amount of cloud cover, the presence or absence of precipitation, and changes in humidity are all important factors that affect observation, and they may reduce the accuracy and clarity of observation.

[0004] Ground-based observation equipment mainly includes two categories: radio detection and photoelectric detection. Radio detection has strong penetration, but serious energy dissipation, and can generally only observe low-orbit targets. Photoelectric detection technology is more mature and can be used to observe space targets in high, medium and low orbits. It has advantages in observing medium and high orbit space targets. However, the weaknesses of photoelectric detection are also obvious. It is easily affected by weather. When the clouds are thick or the light is dim, its observation effect will be greatly reduced.

[0005] Therefore, the demand for predicting cloud cover to support photoelectric detection is very urgent. However, there is a certain degree of uncertainty in the existing cloud cover forecast. This is because the formation and development of clouds are affected by many complex factors. Atmospheric dynamics play an important role in this, which drives the movement and evolution of clouds. At the same time, water vapor conditions are also a key factor in determining the amount of clouds. The sufficiency of water vapor directly affects the generation and dissipation of clouds. In addition, the impact of terrain on cloud cover cannot be ignored. Topographic features such as mountains and river valleys often guide airflow, thereby affecting the distribution and morphology of clouds.

[0006] Although researchers have worked hard to improve the accuracy of cloud forecasts, the forecast results may still have certain errors due to the influence of the above-mentioned complex factors, which in turn affects the ground-based observations of space targets. Summary of the invention

[0007] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a ground-based observation visibility algorithm and device for space targets taking into account the predicted cloud cover, taking into account the cloud cover prediction and improving the accuracy of space target visibility judgment.

[0008] To achieve the above objectives, the present invention provides a ground-based observation visibility algorithm for space targets taking into account the predicted cloud cover, comprising the following steps:

[0009] S1. Use TLE data to obtain space target information, including orbital elements, time, period and cross-sectional area (RCS) information of space targets;

[0010] S2, bring TLE data into the SGP4 model to obtain the speed and coordinates of the space target in multiple time periods and predict the orbit of the space target;

[0011] S3. Based on the predicted orbital information and considering the impact of the Earth's shadow on the space target, determine whether the space target has the conditions to be observed (if it has the conditions to be observed, proceed to the subsequent steps for further determination, the same below);

[0012] S4. Based on the coordinates of the space target in multiple time periods, use the coordinate conversion matrix to determine whether the space target passes over the ground-based observation station;

[0013] S5. Obtain historical cloud cover data above the ground-based observation station, obtain the cloud cover forecast value at the next moment based on the historical cloud cover data, and determine whether the space target can be observed based on the cloud cover forecast value;

[0014] S6. Calculate the magnitude of the space target based on the space target information, compare it with the limiting magnitude of the observation equipment in the ground-based observation station, and determine whether the space target can be observed.

[0015] As a preferred solution of the present invention, in S1, TLE data is obtained by satellite size estimation or by SPACE-TRACK download.

[0016] As a preferred embodiment of the present invention, in the S4, for the coordinates of the space target in multiple time periods, the geocentric inertial coordinate system ECI is converted into the geocentric earth-fixed coordinate system ECEF, and based on the converted coordinates, it is determined whether the space target passes over the ground-based observation station.

[0017] As a preferred embodiment of the present invention, in S5, the historical cloud cover data above the ground-based observation station is obtained through ECMWF-HR, and the cloud cover prediction value is in decimal form, wherein a cloud cover prediction value greater than or equal to 0 and less than or equal to 0.1 represents a sunny day, greater than 0.1 and less than or equal to 0.3 represents a few clouds, greater than 0.3 and less than or equal to 0.7 represents cloudy, and greater than 0.7 represents a rainy day; when the cloud cover prediction value is greater than 0.8, the observation conditions of the space target are not met, and the subsequent magnitude calculation is no longer performed, and it is judged that the space target cannot be observed.

[0018] As a preferred solution of the present invention, the cloud cover forecast value at the next moment is obtained by using the inverse distance method and the linear regression equation. The process is as follows:

[0019] S51. Divide the area above the ground-based observation station into a number of square grids. Take one grid and record the four vertices as 1, 2, 3, and 4. Calculate the center longitude and latitude of the square grid based on the longitude and latitude of the four vertices of the square grid, expressed as:

[0020] (1);

[0021] (2);

[0022] In the formula, , Respectively represent the center longitude and latitude of the square grid; , , , Represent the longitudes of the four vertices of the square grid; , , , Represent the latitudes of the four vertices of the square grid;

[0023] S52, taking the cloud amount data of the diagonal quarter points and vertices of the square grid, and using the cloud amount data at these points to calculate the cloud amount of the square grid, the process is:

[0024] S521. Use n to represent the points used for calculation, including four vertices and four quartering points located on the diagonal, so n=1, 2, ..., 8; calculate the distance of each point from the center of the square grid, expressed as:

[0025] (3);

[0026] (4);

[0027] Where x is the distance from the vertex to the center; S n is the distance from the nth point to the center; , are the differences in longitude and latitude from the vertex to the center respectively; , are the longitude and latitude of the center respectively; , are the differences in longitude and latitude from the nth point to the center respectively; , are the longitude and latitude of the nth point respectively;

[0028] For vertices, x=S appears in the calculation n In this case, x / 3S in equation (5) n is 1 / 3;

[0029] S522. Based on the cloud cover data values ​​of the square grid in T historical time periods before the prediction time (i.e., the values ​​of the cloud cover data in decimal form), the inverse distance method is used to calculate the mean, which is the final cloud cover value of the square grid above the station in each historical time period. Suppose the final cloud cover value of the square grid in the i-th time period is , i=1,2,…,T; It is expressed as:

[0030] (5);

[0031] In the formula, is the cloud cover data value of the nth point in the i-th historical time period;

[0032] S53, establish the linear regression equation between time and cloud cover prediction value, and use Predict the cloud cover at the next moment, expressed as:

[0033] (6);

[0034] (7):

[0035] (8);

[0036] In the formula, c and b are the coefficients of the univariate linear regression equation; The time variable representing the i-th historical time period (a time variable is a uniform time point within a specific time period); is the predicted value of cloud cover at the next moment t; is the average of the final cloud cover values ​​for all historical time periods; is the average value of the time variable over all historical time periods;

[0037] S54. Similarly, the predicted cloud cover values ​​of other square grids at the next time t are obtained, and the predicted cloud cover distribution in the area above the ground-based observation station is obtained.

[0038] As a preferred solution of the present invention, the cloud cover forecast value at the next moment is obtained through the long short-term memory network LSTM, and the process is as follows:

[0039] Obtain historical cloud cover data from several meteorological stations. The historical cloud cover data includes cloud cover records, temperature, humidity, and air pressure at the location of the ground-based observation station at consecutive moments, as well as time information and geographic location information. Use the historical cloud cover data as a training set and input it into the LSTM model. Use the back-propagation algorithm to calculate the gradient of the loss function with respect to the model parameters, and update the model parameters according to the gradient to minimize the loss function.

[0040] Using the trained LSTM, based on the cloud cover data values ​​of the ground-based observation station in T historical time periods before the observation time, the cloud cover at the next moment is predicted to obtain the cloud cover prediction value.

[0041] As a preferred solution of the present invention, in S6, the formula for calculating the magnitude m of the space target is:

[0042] (9);

[0043] Where A is a constant; is the position of the space target from the ground-based observation station, in units of 100 km; L is the length of the space target, in meters; D is the bottom radius of the space target, in meters; is the phase coefficient; is the elevation angle between the ground-based observation station and the space target; E is the atmospheric extinction.

[0044] As a preferred solution of the present invention, assuming that the surface of the space target has a diffuse reflection coefficient, A is set to be 2.5-2.9.

[0045] The present invention also provides a ground-based observation visibility device for space targets taking into account predicted cloud cover, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the above-mentioned algorithm is implemented by executing the computer program by the processor.

[0046] The beneficial effects of the present invention are:

[0047] The present invention combines the orbit prediction model with the cloud cover prediction technology, accurately predicts the orbit of the space target through the SGP4 model, and accurately predicts the cloud cover by combining the linear regression or LSTM algorithm; it not only improves the accuracy of visibility judgment of ground-based observations of space targets, but also makes the observation plan more scientific and reliable by comprehensively considering various influencing factors such as atmospheric extinction and cloud cover.

[0048] The present invention has high cost-effectiveness and convenient operation. Compared with space-based observation, ground-based observation is not only cheaper, but also more convenient to maintain and use. The present invention also has good flexibility and adaptability, and can select the most appropriate prediction method according to different observation conditions and requirements, ensuring that space targets can be effectively observed under changing weather conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the process of the present invention;

[0050] Figure 2 Schematic diagram of a square grid divided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The embodiments of the present invention are further described below in conjunction with the accompanying drawings:

[0052] Example 1: Figure 1 As shown, a ground-based observation visibility algorithm for space targets taking into account predicted cloud cover includes the following steps:

[0053] S1. Use TLE data to obtain space target information, including orbital elements, time, period and cross-sectional area (RCS) information of space targets;

[0054] S2, bring TLE data into the SGP4 model to obtain the speed and coordinates of the space target in multiple time periods and predict the orbit of the space target;

[0055] S3. Based on the predicted orbital information, consider the impact of the earth's shadow on the space target and determine whether the space target is suitable for observation;

[0056] S4. Based on the coordinates of the space target in multiple time periods, use the coordinate conversion matrix to determine whether the space target passes over the ground-based observation station;

[0057] S5. Obtain historical cloud cover data above the ground-based observation station, obtain the cloud cover forecast value at the next moment based on the historical cloud cover data, and determine whether the space target can be observed based on the cloud cover forecast value;

[0058] S6. Calculate the magnitude of the space target based on the space target information, compare it with the limiting magnitude of the observation equipment in the ground-based observation station, and determine whether the space target can be observed.

[0059] RCS is a physical quantity that describes an object's ability to reflect radar waves. It can help determine the size, shape and surface characteristics of a target, as well as assess its detectability in a radar system.

[0060] The SGP4 model is an algorithm for predicting the orbit of space targets. It is based on Kepler's orbital dynamics theory and takes into account a series of simplified perturbation effects, such as the non-spherical gravity of the Earth, the gravitational effects of the Moon and the Sun, atmospheric drag, etc. The SGP4 model is widely used in the orbit analysis and prediction of space targets, especially in the field of orbit determination and satellite tracking.

[0061] Earth's shadow refers to the shadow area formed by the Earth blocking sunlight, which is divided into the umbra (completely dark area) and the penumbra (partially bright area). Earth's shadow also affects the visibility of space targets from ground-based observation stations, because the targets may be too dark to be detected by observation equipment. Visibility analysis based on earth's shadow is an existing technology, which is widely used in satellite observation, astronomical observation, space debris monitoring and other fields.

[0062] In S1, TLE data are obtained through satellite size estimation or downloaded through SPACE-TRACK.

[0063] Satellite size estimation refers to the determination of the physical size of a satellite, including its length, width, height or volume, through various methods and data. Satellite size is one of the important parameters for space target identification and analysis, and is of great significance for space situational awareness, collision avoidance, orbit analysis and satellite function evaluation. Common satellite size estimation methods include radar measurement, optical observation, thermal radiation measurement, orbital dynamics analysis, etc. SPACE-TRACK provides orbital data of space targets to the public, commercial entities, partners, etc., which can be used for analysis and simulation of various space targets.

[0064] In S4, for the coordinates of the space target in multiple time periods, the Earth-centered inertial coordinate system ECI is converted into the Earth-centered Earth-fixed coordinate system ECEF, and based on the converted coordinates, it is determined whether the space target passes over the ground-based observation station.

[0065] The Earth-centered inertial coordinate system (ECI) and the Earth-centered Earth-fixed coordinate system (ECEF) are two coordinate systems commonly used to describe the position of a space target. The conversion from the ECI coordinate system to the ECEF coordinate system involves the influence of the Earth's rotation, and needs to consider the Earth's rotation angular velocity and time. The conversion process is a well-known technology.

[0066] In S5, the historical cloud cover data above the ground-based observation station is obtained through ECMWF-HR, and the cloud cover prediction value is in decimal form, where a cloud cover prediction value greater than or equal to 0 and less than or equal to 0.1 represents a sunny day, greater than 0.1 and less than or equal to 0.3 represents a few clouds, greater than 0.3 and less than or equal to 0.7 represents cloudy, and greater than 0.7 represents a rainy day; when the cloud cover prediction value is greater than 0.8, the observation conditions for space targets are not met.

[0067] ECMWF-HR can provide global meteorological data, including temperature, precipitation, wind speed, wind direction, cloud cover, etc.

[0068] The cloud cover forecast value at the next moment is obtained by using the inverse distance method and linear regression equation. The process is as follows:

[0069] S51. Divide the area above the ground-based observation station into a number of square grids. Take one grid and record the four vertices as 1, 2, 3, and 4. Calculate the center longitude and latitude of the square grid based on the longitude and latitude of the four vertices of the square grid, expressed as:

[0070] (1);

[0071] (2);

[0072] In the formula, , Respectively represent the center longitude and latitude of the square grid; , , , Represent the longitudes of the four vertices of the square grid; , , , Represent the latitudes of the four vertices of the square grid;

[0073] S52, taking the cloud amount data of the diagonal quarter points and vertices of the square grid, and using the cloud amount data at these points to calculate the cloud amount of the square grid, the process is:

[0074] S521. Use n to represent the points used for calculation, including four vertices and four quartering points located on the diagonal, so n=1, 2, ..., 8; calculate the distance of each point from the center of the square grid, expressed as:

[0075] (3);

[0076] (4);

[0077] Where x is the distance from the vertex to the center; S n is the distance from the nth point to the center; , are the differences in longitude and latitude from the vertex to the center respectively; , are the longitude and latitude of the center respectively; , are the differences in longitude and latitude from the nth point to the center respectively; , are the longitude and latitude of the nth point respectively;

[0078] S522. Based on the cloud cover data values ​​of the square grid in T historical time periods before the prediction time (which can be obtained through ECMWF-HR), the inverse distance method is used to calculate the mean value, which is the final cloud cover value of the square grid above the station in each historical time period. Suppose the final cloud cover value of the square grid in the i-th time period is , i=1,2,…,T; It is expressed as:

[0079] (5);

[0080] In the formula, is the cloud cover data value of the nth point in the i-th historical time period;

[0081] A schematic diagram of a square grid is as follows Figure 2 As shown, its center is point O;

[0082] S53, establish the linear regression equation between time and cloud cover prediction value, and use Predict the cloud cover at the next moment, expressed as:

[0083] (6);

[0084] (7):

[0085] (8);

[0086] In the formula, c and b are the coefficients of the univariate linear regression equation; represents the time variable of the i-th historical period; is the predicted value of cloud cover at the next moment t; is the average of the final cloud cover values ​​for all historical time periods; is the average value of the time variable over all historical time periods;

[0087] S54. Similarly, the predicted cloud cover values ​​of other square grids at the next time t are obtained, and the predicted cloud cover distribution in the area above the ground-based observation station is obtained.

[0088] When dividing the area above the ground-based observation station, we can take the ground-based observation station as the center, take the square area above with a side length of 50 km, and then refine it into 25 square grids with a side length of 5 km.

[0089] In S6, the formula for calculating the magnitude m of a space target is:

[0090] (9);

[0091] Where A is a constant; is the position of the space target from the ground-based observation station, in units of 100 km; L is the length of the space target, in meters; D is the bottom radius of the space target, in meters; is the phase coefficient; is the elevation angle between the ground-based observation station and the space target; E is the atmospheric extinction. Assuming that the surface of the space target has a diffuse reflectance, A is taken as 2.5-2.9. The magnitude calculation can also be performed using other known calculation methods.

[0092] Embodiment 2: The difference between this embodiment and embodiment 1 is that the cloud cover forecast value at the next moment is obtained through the long short-term memory network LSTM, and the process is as follows:

[0093] Obtain historical cloud cover data from several meteorological stations (still obtained through ECMWF-HR, and can also be obtained through other well-known channels, such as WMO, NOAA, etc., this part of the data is only used for training). The historical cloud cover data includes the cloud cover records, temperature, humidity and air pressure at the continuous moment of the ground-based observation station location, as well as time information and geographic location information. Use the historical cloud cover data as a training set and input it into the LSTM model. Use the back propagation algorithm to calculate the gradient of the loss function with respect to the model parameters, and update the model parameters according to the gradient to minimize the loss function;

[0094] Using the trained LSTM, based on the cloud cover data values ​​of the ground-based observation station in T historical time periods before the observation time, the cloud cover at the next moment is predicted to obtain the cloud cover prediction value.

[0095] Taking the cloud cover data at a ground-based observation station as an example, 1826 cloud cover data before the observation time were selected to form a data set. After 500 iterations, the final prediction result was 0.9410, and the accurate value at the next moment was 0.948059082, with an error of 0.7%, which proved the feasibility of using LSTM to predict cloud cover within hourly periods.

[0096] Embodiment 3: A ground-based observation visibility device for space targets taking into account predicted cloud cover, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the algorithms described in Embodiments 1 and 2 are implemented by executing the computer program by the processor.

Claims

1. A ground-based visibility algorithm for space targets taking into account the predicted cloud cover, characterized in that The following steps are involved: S1. Use TLE data to obtain space target information, including orbital elements, time, period and cross-sectional area (RCS) information of space targets; S2, bring TLE data into the SGP4 model to obtain the speed and coordinates of the space target in multiple time periods and predict the orbit of the space target; S3. Based on the predicted orbit information, consider the impact of the earth's shadow on the space target and determine whether the space target is suitable for observation; S4. Based on the coordinates of the space target in multiple time periods, use the coordinate conversion matrix to determine whether the space target passes over the ground-based observation station; S5. Obtain the historical cloud cover data above the ground-based observation station, obtain the cloud cover prediction value at the next moment based on the historical cloud cover data, and judge whether the space target can be observed based on the cloud cover prediction value. The cloud cover prediction value at the next moment is obtained by the inverse distance method and the linear regression equation. The process is as follows: divide the area above the ground-based observation station into several square grids, take one grid, take the cloud cover data of the diagonal quarter points and vertices of the square grid, use the cloud cover data at these points to calculate the cloud cover of the square grid, and use the inverse distance method to calculate the average value based on the cloud cover data values ​​of the square grid in T historical time periods before the prediction time, which is the final cloud cover value of the square grid above the observation station in each historical time period. Suppose the final cloud cover value of the square grid in the i-th time period is , i=1,2,…,T, establish the linear regression equation between time and cloud cover prediction value, and use Predict the cloud cover at the next moment, and similarly obtain the predicted cloud cover values ​​of other square grids at the next moment, and obtain the predicted cloud cover distribution in the area above the ground-based observation station; S6. Calculate the magnitude of the space target based on the space target information, compare it with the limiting magnitude of the observation equipment in the ground-based observation station, and determine whether the space target can be observed.

2. The ground-based observation visibility algorithm for space targets taking into account the predicted cloud cover according to claim 1, characterized in that: In S1, TLE data are obtained through satellite size estimation or downloaded through SPACE-TRACK.

3. The ground-based observation visibility algorithm for space targets taking into account the predicted cloud cover according to claim 1, characterized in that: In the above S4, for the coordinates of the space target in multiple time periods, the Earth-centered inertial coordinate system ECI is converted into the Earth-centered Earth-fixed coordinate system ECEF, and based on the converted coordinates, it is determined whether the space target passes over the ground-based observation station.

4. The ground-based observation visibility algorithm for space targets taking into account the predicted cloud cover according to claim 1, characterized in that: In the above S5, the historical cloud cover data above the ground-based observation station is obtained through ECMWF-HR, and the cloud cover prediction value is in decimal form, wherein a cloud cover prediction value greater than or equal to 0 and less than or equal to 0.1 represents a sunny day, a cloud cover prediction value greater than 0.1 and less than or equal to 0.3 represents a few clouds, a cloud cover prediction value greater than 0.3 and less than or equal to 0.7 represents a cloudy day, and a cloud cover prediction value greater than 0.7 represents a rainy day; when the cloud cover prediction value is greater than 0.8, the observation conditions for the space target are not met.

5. The ground-based observation visibility algorithm for space targets taking into account the predicted cloud cover according to claim 4, characterized in that: The cloud cover forecast value at the next moment is obtained by using the inverse distance method and linear regression equation. The process is as follows: S51. Divide the area above the ground-based observation station into a number of square grids. Take one grid and record the four vertices as 1, 2, 3, and 4. Calculate the center longitude and latitude of the square grid based on the longitude and latitude of the four vertices of the square grid, expressed as: （1）； （2）； In the formula, , Respectively represent the center longitude and latitude of the square grid; , , , Represent the longitudes of the four vertices of the square grid; , , , Represent the latitudes of the four vertices of the square grid; S52, taking the cloud amount data of the diagonal quarter points and vertices of the square grid, and using the cloud amount data at these points to calculate the cloud amount of the square grid, the process is: S521. Use n to represent the points used for calculation, including four vertices and four quartering points located on the diagonal, so n=1, 2, ..., 8; calculate the distance of each point from the center of the square grid, expressed as: （3）； （4）； Where x is the distance from the vertex to the center; S n is the distance from the nth point to the center; , are the differences in longitude and latitude from the vertex to the center respectively; , are the longitude and latitude of the center respectively; , are the differences in longitude and latitude from the nth point to the center respectively; , are the longitude and latitude of the nth point respectively; S522. Based on the cloud cover data values ​​of the square grid in T historical time periods before the prediction time, the inverse distance method is used to calculate the mean value, which is the final cloud cover value of the square grid above the observation station in each historical time period. Suppose the final cloud cover value of the square grid in the i-th time period is , i=1,2,…,T; It is expressed as: （5）； In the formula, is the cloud cover data value of the nth point in the i-th historical time period; S53, establish the linear regression equation between time and cloud cover prediction value, and use Predict the cloud cover at the next moment, expressed as: （6）； （7）： （8）； In the formula, c and b are the coefficients of the univariate linear regression equation; represents the time variable of the i-th historical period; is the predicted value of cloud cover at the next moment t; is the average of the final cloud cover values ​​for all historical time periods; is the average value of the time variable over all historical time periods; S54. Similarly, the predicted cloud cover values ​​of other square grids at the next time t are obtained, and the predicted cloud cover distribution in the area above the ground-based observation station is obtained.

6. The ground-based observation visibility algorithm for space targets taking into account the predicted cloud cover according to claim 5, characterized in that: In S6, the formula for calculating the magnitude m of the space target is: （9）； Where A is a constant; is the position of the space target from the ground-based observation station, in units of 100 km; L is the length of the space target, in meters; D is the bottom radius of the space target, in meters; is the phase coefficient; is the elevation angle between the ground-based observation station and the space target; E is the atmospheric extinction.

7. The ground-based observation visibility algorithm for space targets taking into account the predicted cloud cover according to claim 6, characterized in that: Assuming that the surface of the space target has a diffuse reflection coefficient, A is taken as 2.5-2.

9.

8. A ground-based observation visibility device for space targets taking into account the predicted cloud cover, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the algorithm described in any one of claims 1 to 7 is implemented by executing the computer program by the processor.

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