Remote Sensing Satellite Coverage Prediction Method Considering Meteorological Prediction Cloud Maps and Sensor Pendulation

Abstract

The present invention relates to the field of satellite remote sensing images, and discloses a remote sensing satellite coverage prediction method considering meteorological prediction cloud images and sensor pendulum measurement. Through steps such as TLE parameter update for orbit prediction, orbit prediction calculation, swath buffer zone setting, coverage area orbit truncation, meteorological prediction cloud image association, satellite pendulum measurement parameter simulation, and prediction result effective coverage rate calculation, auxiliary data of meteorological cloud images is introduced to better display the effective coverage area of the predicted orbit of the remote sensing satellite. The area that can be covered by the satellite with the satellite orbit inclination is fully considered, thereby constructing a method for the effective coverage rate of satellite photography considering meteorological prediction cloud images and sensor pendulum measurement models, and completely solving the problem that the effective coverage rate of the predicted photography scheme cannot be quantified.

Description

Technical Field

[0001] The present invention relates to the field of satellite remote sensing images, and in particular to a remote sensing satellite coverage prediction method taking into account meteorological forecast cloud images and sensor pendulum measurement. Background Art

[0002] In recent years, new digital products and services related to satellite remote sensing have continued to emerge, and the industry has ushered in a historical opportunity for explosive growth. Satellite remote sensing has become an important field of innovation and development. With the rapid development of ecological civilization construction and green economy, remote sensing will be more widely and deeply applied in resource surveys, special surveys, environmental monitoring, ecological restoration, spatial planning, land and space use control, property rights registration, dynamic law enforcement, and outgoing audits. The demand for remote sensing data is showing an explosive growth trend; the efficient planning and shooting guarantee of remote sensing image data is of great significance, so it is very necessary to develop a device that can efficiently and real-time assist satellite shooting planning.

[0003] The precise revisit period of satellite orbits is one of the core factors that affect the effective photography of remote sensing satellites. The period for a single remote sensing satellite to revisit the same area of ​​the earth is generally several days. The multi-satellite combination can greatly reduce the period for revisiting the same area. At the same time, considering that the conventional orbit prediction method will gradually diverge with the extension of prediction time, resulting in inaccurate prediction results; therefore, it is of great significance to consider the revisit prediction and orbit accuracy assurance methods of multi-satellite coordination.

[0004] The principle of the classic satellite orbit determination model in the prior art is that the satellite's orbit is generally relatively stable, so the satellite's visible range is generally relatively fixed. According to the satellite's dynamic principle, the six elements of the satellite orbit are used to determine the satellite position information of the time series; however, the satellite orbit is also affected by some other external factors, such as solar pressure, regional gravity field and relativistic effects, etc. As time goes by, the orbit prediction accuracy gradually diverges; currently, conventional orbit simulation prediction systems on the market all use static and fixed orbital elements (such as STK, N2YO, student.space, CelesTrak, etc.), lack an orbital element update and correction mechanism, and cannot achieve long-term and precise orbit prediction. At the same time, they do not have the superposition analysis function that takes into account meteorological cloud map prediction and satellite pendulum simulation, and cannot accurately calculate the effective coverage rate of future satellite shooting of a specified area. Summary of the invention

[0005] The object of the present invention is to provide a remote sensing satellite coverage prediction method taking into account meteorological forecast cloud maps and sensor pendulum measurement, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A remote sensing satellite coverage prediction method considering meteorological prediction cloud images and sensor pendulum measurement, comprising the following steps:

[0008] Obtain the TLE orbit parameters corresponding to the satellite through the TLE orbit real-time acquisition program, and store the TLE orbit parameters in the local database. The TLE orbit parameters are expressions used to describe the position and velocity of a space flyer.

[0009] Obtain several of the TLE orbit parameters within a preset time period length, and predict and simulate the future orbit of the satellite through the SGP4 / SDP4 satellite orbit calculation method to generate satellite orbit prediction information. The SGP4 / SDP4 satellite orbit calculation method calculates the satellite orbit based on the initial satellite motion state and perturbing forces. The perturbing forces include the Earth and non-spherical gravity, solar and lunar gravity, solar radiation, and atmospheric drag.

[0010] Establish a data orbit library according to several of the satellite orbit prediction information for a future preset time period, and set a width buffer according to the preset width of the satellite. The width is used to characterize the strip width of the satellite's ground shooting.

[0011] Establish the shooting coverage ability attributes of the satellite based on the orbit height, the width, and the maximum pendulum angle of each satellite. Obtain the shooting model corresponding to the satellite according to the coverage ability attributes, introduce meteorological cloud image prediction data, and simulate the shooting ability of the satellite in a specified area in the future according to the meteorological cloud image prediction data.

[0012] Satellite pendulum parameter simulation and pendulum coverage width calculation. Calculate and obtain the pendulum coverage width actually covered by the satellite according to the pendulum angle of the satellite, the ground height of the satellite, the width, and a preset width calculation method.

[0013] Receive the prediction range from the user, perform an intersection operation on the coverage result sets of multiple satellites and the cloud image vector coverage results at the transit time of each satellite to obtain a spatial intersection result set. The spatial intersection result set is used to characterize the effective shooting area at the prediction time.

[0014] As a further solution of the present invention: it further includes:

[0015] Establish a three-dimensional dynamic model and a two-dimensional dynamic model of the satellite based on the satellite orbit prediction information and a preset simulation system and output them. Receive user query information and output feedback content corresponding to the queried satellite.

[0016] As a further aspect of the present invention: The step of obtaining the TLE orbit parameters corresponding to the satellite through the TLE orbit real-time acquisition program and storing the TLE orbit parameters in the local database specifically includes:

[0017] Determine whether the target platform provides an API. If the API is not provided, obtain the TLE orbit parameters through a preset real-time acquisition program;

[0018] Data structure analysis and data storage. First, determine the required fields, determine the construction table and connection relationship according to the required fields, and select a local database for storage;

[0019] Data flow analysis, including determining the acquisition range and cutting into the source, jumping between multi-layer web page structures, range subdivision, access mode analysis, URL and parameter analysis;

[0020] Data acquisition. Use the scrapy module, BeautifulSoup parsing tool and Pandas method to organize the data and write it into the database.

[0021] As a further aspect of the present invention: The step of predicting and simulating the future orbit of the satellite through the SGP4 / SDP4 satellite orbit extrapolation method to generate satellite orbit prediction information specifically includes:

[0022] Introduce the TLE orbit parameters and use the TLE orbit parameters to recover the mean orbital elements;

[0023] Calculate the long-term term, long-period term, and short-period term according to the mean orbital elements, and calculate the spatial point position and velocity at the prediction time point.

[0024] As a further aspect of the present invention: The step of establishing a data orbit library based on several pieces of the satellite orbit prediction information for a preset future time period and setting a width buffer according to the preset width of the satellite specifically includes:

[0025] After the TLE orbit parameters are stored in the local database, perform future one-week orbit parameter prediction calculations in a serial manner;

[0026] Calculate the position information of the satellite orbit in the WGS84 ellipsoid framework at a preset time step T for a preset future duration for each epoch;

[0027] Set a buffer area for the default coverage area according to the width of each satellite. After connecting the prediction points of the position information front and back, form a line graph with a direction, and perform spatial buffering on the line graph according to the width, using the single-line fixed-distance buffering method to obtain the orbit prediction coverage area of this point;

[0028] Store the obtained position information and the orbit prediction coverage area in the database.

[0029] As a further solution of the present invention: it further includes the step of network publishing of meteorological cloud maps:

[0030] Obtain the meteorological cloud layer distribution for multiple days in the future at a preset update duration, perform mathematical simulation based on the meteorological cloud layer distribution, and store the prediction layer results of the mathematical simulation in a time series;

[0031] Store the prediction layer results in GeoJson format respectively in the form of raster data and spatial vector data. In the raster data form, the area not covered by clouds is set as a transparent value, and the area covered by clouds is set as a gray transition color value from black to white according to the cloud thickness. The vector data form is used to record the range of cloud amount.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. By adding the overlay analysis of using satellite cloud maps to assist in predicting the coverage area under the remote sensing satellite orbit, the present invention can more effectively and accurately display the effective coverage area under the predicted remote sensing satellite pre-orbit, facilitating satellite operators and satellite users to further control the shooting window (cloud-free window) of the area of interest, so as to improve the satellite shooting ability.

[0034] 2. By considering the side-sway ability of the satellite and using the calculation model of the present invention to analyze the effective coverage area under the predicted remote sensing satellite orbit at different side-sway angles, the area covered by clouds at the prediction moment can be avoided by simulating the satellite side-sway, thereby improving the effective coverage rate of the expected shooting.

[0035] 3. By automatically updating the satellite TLE orbit every day, the orbit prediction accuracy is converged. At the same time, through the mode of advance prediction calculation and front-end real-time query, the waiting time of users is greatly saved (the efficiency can be increased by more than 10 times), and the orbit prediction calculation for the user level is realized in real time.

[0036] 4. A method for comprehensively calculating the effective rate coverage of the prediction time point by considering the predicted satellite orbit, satellite cloud map coverage, satellite side-sway, user-specified sensor and range realizes the quantitative calculation of the effective prediction coverage of the satellite, providing an important decision-making reference for satellite-assisted planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of a remote sensing satellite coverage prediction method considering meteorological prediction cloud maps and sensor side-sway.

[0038] Figure 2 It is a schematic diagram of four topological relationships in the plane space in the remote sensing satellite coverage prediction method considering meteorological prediction cloud maps and sensor side-sway.

[0039] Figure 3 An example of the orbital buffer surface for a remote sensing satellite coverage prediction method that takes into account meteorological prediction cloud images and sensor pendulum measurements.

[0040] Figure 4 Schematic diagram of the pyramidal planar structure in a remote sensing satellite coverage prediction method that takes into account meteorological prediction cloud images and sensor pendulum measurements.

[0041] Figure 5 Schematic diagram of the intuitive pyramidal structure in a remote sensing satellite coverage prediction method that takes into account meteorological prediction cloud images and sensor pendulum measurements.

[0042] Figure 6 The organizational structure of cloud image tile files in a remote sensing satellite coverage prediction method that takes into account meteorological prediction cloud images and sensor pendulum measurements.

[0043] Figure 7 Schematic diagram of the satellite side-sway angle and coverage in a remote sensing satellite coverage prediction method that takes into account meteorological prediction cloud images and sensor pendulum measurements. Detailed implementation manners

[0044] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0045] The following describes in detail the specific implementation manners of the present invention with reference to specific embodiments.

[0046] As Figure 1 、 Figure 2 and Figure 7 shown, a remote sensing satellite coverage prediction method that takes into account meteorological prediction cloud images and sensor pendulum measurements provided by an embodiment of the present invention includes the following steps:

[0047] This embodiment provides a remote sensing satellite prediction method that takes into account meteorological prediction cloud maps and sensor pendulum measurements. In satellite remote sensing photography, cloud and rain are the most core factors affecting effective satellite photography. Especially in regions such as southern China, southern India's Himalayas, Hawaii in the United States, and New Zealand in Oceania, which are cloudy and rainy all year round, it greatly affects the satellite image shooting window, resulting in a low effective guarantee rate for many satellites in the above regions. Limited by satellite power, the on - camera shooting time for each satellite within 24 hours is very limited. If weather factors are not accurately considered and blind on - camera shooting is carried out in relevant regions, it will inevitably cause huge waste of resources. Accurately controlling the satellite shooting window and improving the effective coverage rate of satellite images in cloudy and rainy areas have great economic value; in addition, the satellite pendulum measurement ability is also one of the important parameters affecting satellite shooting. If, after orbital prediction and superposition analysis of meteorological cloud maps, there are still areas that cannot be covered in the specified area, the satellite's side - swing angle can be adjusted by simulation to effectively cover cloud - free areas. Therefore, considering the influence of the side - swing angle in satellite assisted photography can further improve the accuracy of satellite effective coverage and has high research value.

[0048] However, in existing technologies, most current software only realizes the orbital prediction of a small number of remote sensing satellites and the display of the coverable area. The combined prediction of multiple satellites is difficult, and there is a lack of a correction strategy for satellite orbital elements, resulting in the gradual divergence of satellite orbital prediction accuracy. Moreover, the influence of meteorological cloud maps on the effective coverage rate of remote sensing images is not considered, causing the visible area under the predicted orbit of the remote sensing satellite to be different from the theoretical effective coverage area. For decision - makers, not considering the spatial distribution of cloud cover will lead to a waste of a large amount of shooting resources. If there is cloud cover in the frontal view state of the satellite orbit, the satellite can deflect at a certain angle through pendulum measurement to photograph cloud - free areas. There is currently no relevant device on the market that can achieve the precise spatial analysis ability of the superposition of remote sensing satellite prediction and meteorological cloud map prediction. All in all, there is currently no platform that can achieve the effective coverage rate prediction ability based on accurate geographical spatial positions at the prediction level. Most only stay at the qualitative level for selecting shooting schemes and cannot select shooting schemes from quantitative indicators at the prediction level.

[0049] S10. Obtain the TLE orbital parameters corresponding to the satellite through the TLE orbit real - time acquisition program, and store the TLE orbital parameters in the local database. The TLE orbital parameters are expressions used to describe the position and velocity of a space - flying object.

[0050] S20. Obtain several of the TLE orbital parameters within a preset time period length, predict and simulate the future orbit of the satellite through the SGP4 / SDP4 satellite orbit calculation method, and generate satellite orbit prediction information. The SGP4 / SDP4 satellite orbit calculation method calculates the satellite orbit based on the initial satellite motion state and perturbing forces. The perturbing forces include the Earth's and non-spherical gravitational forces, solar and lunar gravitational forces, solar radiation, and atmospheric drag.

[0051] In this embodiment, TLE (Two-Line Orbital Element), which is an expression used to describe the position and velocity of a space flyer. By using the two-line orbital elements of the satellite TLE, the real-time and predicted positions of each satellite at specified times can be estimated based on the SDP4 model. Specifically, it includes orbital prediction information such as the satellite's altitude, velocity, and spatial position. Based on the two-line TLE orbital parameters, the SGP4 / SDP4 model is used for prediction calculations. This model takes into account the influences of solar and lunar gravitational forces, atmospheric drag, and the Earth's non-spherical gravitational perturbation, etc., and has a relatively high medium- and long-term orbital prediction accuracy. This model can accurately predict satellites with a period less than 225 minutes. The current mainstream Earth observation remote sensing satellites all have a period less than 225 minutes, and this model is fully applicable to the orbital prediction of such satellites.

[0052] S30. Establish a data orbit library based on the several satellite orbit prediction information for a future preset time period, and set a width buffer according to the preset width of the satellite. The width is used to represent the strip width of the satellite's Earth observation shooting.

[0053] In this embodiment, this step serves the purpose of storing the orbit prediction information in the database and optimizing the query. Since orbit calculation consumes a large amount of computing resources and is limited by the computer's configuration, taking the prediction of the future one-week orbit of a single satellite as an example, it generally takes 1 - 3 minutes. If there are too many satellites to be predicted, it will cause too long a waiting time for users, greatly affecting the user experience. To solve the problem of too long orbit prediction time, the present invention adopts a method of pre-calculation and real-time front-end query, shortening the waiting time for predicting the future one-week orbit of a single satellite to within 3 seconds.

[0054] S40. Establish the shooting coverage ability attribute of the satellite based on the orbital altitude, the width, and the maximum swing angle of each satellite. Obtain the shooting model corresponding to the satellite according to the coverage ability attribute, introduce meteorological cloud map prediction data, and simulate the shooting ability of the satellite in a specified area in a future time period according to the meteorological cloud map prediction data.

[0055] In this embodiment, it is to judge the shooting ability of the satellite at a certain position at a certain moment based on the meteorological cloud map, so as to realize the judgment of the satellite's ability.

[0056] S50. Simulate the satellite's pendulum parameters and calculate the pendulum coverage width. Calculate and obtain the pendulum coverage width actually covered by the satellite according to the pendulum angle of the satellite, the ground height of the satellite, the width, and a preset width calculation method.

[0057] In this embodiment, under normal conditions, the satellite sensor points vertically to the ground for shooting, and its pendulum angle θ = 0°. Assume the height of the satellite from the ground is H and the satellite width is D. Then, the satellite's shooting field angle α = 2arctan(D / 2H) can be calculated. Further, after the satellite's pendulum, the actually covered width D1 = Htan(θ + α / 2) - Htan(θ - α / 2) can be calculated.

[0058] S60. Receive the prediction range from the user. Perform an intersection operation on the coverage result sets of multiple satellites and the cloud map vector coverage results at the transit times of each satellite to obtain a spatial intersection result set, which is used to represent the effective shooting area at the prediction time.

[0059] In this embodiment, the user inputs the satellite prediction range as A. Use the coverage result sets of multiple satellites {S1, S2, S3... Sn} and perform intersections with the cloud map vector coverage results {Y1, Y2, Y3... Yn} at the transit times of each satellite respectively. The intersection method uses the spatial topology algorithm described in the previous section of the present invention for calculation to obtain the spatial intersection result set {C1, C2, C3... Cn}. This result set represents the cloud-free area (i.e., the effective shooting area) photographed at the prediction time. Considering that there may be cross-repetition among the intersection result sets, in order to ensure that the effective coverage rate of the overlapping area is only calculated once when calculating the effective coverage rate, the spatial intersection fusion method is used to perform a union operation on them to obtain the combined effective coverage situation C predicted by all satellites. Finally, calculate the effective coverage rate μ = C / A * 100% of the specified range. Here, the spatial topology algorithm is described:

[0060] Including a determination algorithm: When performing the intersection of the orbit and the prediction area, it is often necessary to determine whether there is an inclusion relationship between the orbit graph and the prediction area. The present invention uses the Euler number operator to judge the orbit intersection to improve the intersection efficiency.

[0061] Connectivity number: It refers to the number of non-connected parts within the research object.

[0062] The Euler number is also called the Euler characteristic number in topology and is a topological invariant. Any topological transformation does not change the Euler characteristic number of the patch. The Euler characteristic number is mathematically expressed as the number of connected components minus the number of holes:

[0063] Eul = C – H; where Eul is the Euler number, C is the connectivity number, and H is the number of holes.

[0064] The relationship between the orbital position information and the prediction range can be simplified to the relationship between surfaces, mainly including surface-surface coincidence (the two surfaces completely overlap), surface-surface separation (all points on one surface are not within the other surface), surface-surface inclusion (all points on one surface are within the inclusion range of the other surface), and surface-surface intersection (some points on one surface are within the inclusion range of the other surface). The Euler number is used to describe the above four cases, and the topological matrices of the above four cases are calculated respectively using the set operation operator model of the Euler number:

[0065] Surface-surface coincidence: Extract the boundary lines of the two surfaces and judge their intersection situation. If there is an intersection, then judge whether the boundaries are equal. If they are equal, it is determined that the two surfaces coincide, satisfying that A\B and B\A are empty, that is:

[0066]

[0067] Surface-surface separation: Judge the boundary lines of the two surfaces. If the boundaries do not intersect, it is judged as surface-surface separation, satisfying that A∩B, A / B, and B / A are all empty, and its topological matrix is:

[0068]

[0069] Surface-surface inclusion: If A is inside B, then A / B is empty, and its topological matrix is:

[0070]

[0071] Surface-surface intersection: The topological matrix is closely related to the number of intersection lines. Its topological matrix is:

[0072]

[0073] Where l is the number of intersection targets of A and B; m is the number of lines connected to the inside of surface A by the boundary line of the intersection surface of A and B, and n is the number of lines connected to the inside of surface B by the boundary line of the intersection surface of B and A. In the present invention, there are two cases of surface-surface separation and intersection. Through the Euler operator, the intersection result is obtained by combining the range data passed in by the user with the orbital information, and the coverage area is used to perform spatial truncation on the orbital prediction result as the output.

[0074] As another preferred embodiment of the present invention, it further includes:

[0075] Based on the satellite orbit prediction information and a preset simulation system, a three-dimensional dynamic model and a two-dimensional dynamic model of the satellite are established and output, and user query information is received and feedback content corresponding to the queried satellite is output.

[0076] In this embodiment, the visualization display of the client is made based on the Cesium open-source framework. First, the maps of the three-dimensional earth and the two-dimensional earth are made respectively, and the dynamic position information of each satellite is displayed according to the orbit prediction information such as the height, speed, and spatial position of each satellite calculated in the second step. Based on the mainstream Web development framework (Node), the front-end and back-end systems are built. The back-end mainly integrates functions such as orbit data capture and orbit algorithm deduction, and the front-end mainly realizes the orbit simulation display under the 3D earth, the meteorological cloud map display, the coverage analysis display, etc. The client receives information such as the satellite object, time, and spatial area queried by the user, intersects the orbit information and the information of various buffers in the steps of the present invention with the satellite object, time, and spatial area input by the user, and obtains the information to be displayed for the user's target satellite, target time, and target spatial area. Finally, the display and auxiliary shooting decision are carried out on the terminal. Specifically, the overall interactive interface of this system uses the 3D earth as a carrier, and the left panel integrates controllable screening parameters such as the resolution, nationality, and satellite name of several satellites. The specific functions may include: providing a place name query function: providing a query function for regions above the county level in the country, highlighting the region according to the region input by the user and using it as a prediction region for prediction; obtaining an image prediction function for the observation region: the prediction result is a list of data and areas that can be obtained emergently within the next 15 days, and the coverage rate is counted; after the orbit prediction is completed, click play to play the ascending and descending orbit states of the satellite passing by, and at the same time superimpose the predicted cloud map at the passing moment for the analysis of effective shooting.

[0077] As another preferred embodiment of the present invention, the step of obtaining the TLE orbit parameters corresponding to the satellite through the TLE orbit real-time acquisition program and storing the TLE orbit parameters in the local database specifically includes:

[0078] Judging whether the target platform provides an API. If the API is not provided, the TLE orbit parameters are obtained through a preset real-time acquisition program.

[0079] Data structure analysis and data storage. First, determine the required fields, determine the construction table and connection relationship according to the required fields, and select a local database for storage.

[0080] Data flow analysis, including determining the acquisition range and the entry source, jumping between multi-layer web page structures, range subdivision, access mode analysis, URL and parameter analysis.

[0081] Data acquisition, data sorting is carried out through the use of the scrapy module, the BeautifulSoup parsing tool, and the Pandas method, and written into the database.

[0082] Further, the steps of predicting and simulating the future orbit of the satellite by the SGP4 / SDP4 satellite orbit extrapolation method to generate satellite orbit prediction information specifically include:

[0083] Introduce the TLE orbit parameters and restore the mean orbital elements using the TLE orbit parameters.

[0084] Calculate the long-term term, long-period term, and short-period term according to the mean orbital elements, and calculate the spatial point position and velocity at the prediction time point.

[0085] In this embodiment, the two-line orbital elements of TLE are described here. For example:

[0086] GF7;

[0087] 44703U 19072A 22132.22829630.00003149 00000-0 14349-3 0 9990;

[0088] 44703 97.4056 211.3973 0007666 282.4155 161.8180 15.21374051140042.

[0089] For the 0th line, considering the actual first line as the 0th line, it is the satellite common name, with a maximum length of 24 characters; the 1st and 2nd lines are in the standard satellite ephemeris format (TLE format), each line having 69 characters, including 0-9, A-Z (uppercase), spaces, dots, and plus / minus signs. Among them, for the satellite ephemeris number:

[0090] The 1st line:

[0091] (1) 44703U, 44703 is the satellite number. U represents unclassified. C and S are classified;

[0092] (2) 19072A, the international number, 19 indicates launched in 2019, 072 indicates the 72nd launch of this year, A indicates one satellite per launch vehicle, B indicates two satellites per launch vehicle, and so on with A, B, C....;

[0093] (3) 22132.22829630, represents the time point of this set of orbit data, 22 is 2022, 132 represents the 132nd day, and 22829630 represents the moment within this day;

[0094] (4).00003149 00000-0 14349-3, are the orbit model parameters;

[0095] (5) 0, is the orbit model type, the SGP4 / SDP4 orbit model;

[0096] (6)999 indicates the 999th group of TLE parameters of the space satellite;

[0097] (7)0 is the check bit;

[0098] Line 2:

[0099] (1)44703 is the satellite number;

[0100] (2)97.4056 is the orbital inclination;

[0101] (3)211.3973 is the right ascension of the ascending node;

[0102] (4)0007666 is the orbital eccentricity, and the actual value is 0.0007666;

[0103] (7)282.4155 is the argument of perigee;

[0104] (8)161.8180 is the mean anomaly, indicating the position of the satellite in the orbit at the moment corresponding to this group of TLEs;

[0105] (9)15.21374051140042 is the number of orbits around the Earth per day, and its reciprocal is the period.

[0106] As Figure 3 shown, as another preferred embodiment of the present invention, the step of establishing a data orbit library according to several pieces of the satellite orbit prediction information in a future preset time period and setting a width buffer according to the preset width of the satellite specifically includes:

[0107] After the TLE orbit parameters are stored in the local database, the orbit parameter prediction calculation for the next week is carried out in a serial manner.

[0108] With a preset step size T, the position information of the satellite orbit in the WGS84 ellipsoid framework is calculated epoch by epoch for a preset future duration.

[0109] According to the width of each satellite, a buffer area for the default coverage area is set. After connecting the predicted points of the position information before and after, a line graph with a direction is formed. According to the width, spatial buffering is performed on the line graph, and the single-line fixed-distance buffering method is used to obtain the orbit prediction coverage area of this point.

[0110] The obtained position information and the orbit prediction coverage area are stored in the database.

[0111] In this embodiment, let the step size T = 2s, and the position information be (LONt, LATt, H), where LONt represents the longitude at the future time point t, LATt represents the latitude at the future time point t, and H represents the height of the satellite from the ground. Starting from the current moment T0, a single satellite can obtain all the position information of the satellite within seven days after cumulative calculation 302,400 times; according to the swath width of each satellite, a buffer area for the default coverage area is set. The strip width of the ground photograph taken by each satellite is defined as the swath width. Generally, the swath width of near-earth remote sensing satellites ranges from several kilometers to dozens of kilometers. After connecting the predicted orbit points calculated in the previous step in sequence, a line graph with a direction is formed. According to the swath width, spatial buffering is performed on the line graph, and the single-line fixed-distance buffering method is used to obtain the predicted coverage area of the orbit at this point. The orbit buffer (i.e., the expected photographing range) is the object or set A of the given orbit line. According to the satellite photographing swath width D, with r = D / 2 as the radius, buffer r distance outward from the center of the orbit line to form the expected orbit photographing area range, that is, P = {x|d(x,A) ≤ r}, where d is the Euclidean distance; when storing information in the database, the form of the database is SAT (satellite source), LON (longitude), LAT (latitude), the_geom (predicted coverage area range of the orbit), time (transit time); generally speaking, traverse all the satellite lists to be calculated, and obtain the position information of each satellite in the next week one by one. At the same time, store the information in the database in a standardized data format. After 0:00 every day, after the orbit of each satellite is updated, repeat the steps of establishing a data orbit database based on the predicted information of several satellite orbits in a preset future time period and setting a swath buffer according to the preset swath width of the satellite to update the predicted position information of the orbit, so as to avoid the divergence of prediction accuracy and ensure the accuracy of the prediction information of each satellite. Package the interface. The user selects parameter information such as a specified satellite, prediction range, and prediction time on the front-end page and clicks the prediction button. The system device will transmit the above parameters to the background server, perform operations such as association with the database, time intersection, and spatial intersection, and return a set of satellite predicted position points that meet the conditions to the front-end. Connect the point sets in the query results in sequence to form a linear predicted orbit line.

[0112] As Figure 4 , Figure 5 and Figure 6 shown, as another preferred embodiment of the present invention, it further includes the step of network publishing of meteorological cloud images:

[0113] Obtain the meteorological cloud layer distribution in the next few days at a preset update duration, perform mathematical simulation according to the meteorological cloud layer distribution, and store the prediction layer results of the mathematical simulation in time series.

[0114] The prediction layer results are stored in GeoJson format using raster data and spatial vector data respectively. In the raster data form, the cloud-uncovered area is set to a transparent value, and the cloud-covered area is set to a gray transition color value from black to white according to the cloud thickness. The vector data form is used to record the range of the cloud amount.

[0115] In this embodiment, the specific method used here is to update once every three hours, perform mathematical simulation on the meteorological cloud distribution in the next 7 days, form a prediction layer result with a step length of three hours, and store it in the form of time series; in the form of raster data, the cloud coverage information is stored in the form of a pixel grid raster, the cloud uncovered area is set to a transparent value, and the covered area is set to rgb (X, X, X) according to the cloud thickness, where X has a value range of [0, 255], indicating a gray transition color value from black to white, and the raster data form is used for superimposed display in the device of the present invention; in the form of vector data, the cloud range is stored in a vector format, specifically in the form of a spatial coordinate string structure, indicating the range of cloud coverage, and stored in the GeoJson format to facilitate the spatial intersection of the orbit prediction result and the cloud vector in the later link, where the GeoJson format is as follows:

[0116]

[0117] This step also includes the steps of hierarchical slicing of raster cloud images and network service publishing. Specifically, the raster cloud image data is sliced ​​according to the quadtree index mechanism, and a cloud image in GEOTIFF format is cut into an n-layer pyramid model through a tile pyramid. When constructing a terrain pyramid, the original cloud image data is first used as the bottom layer of the pyramid, that is, the 0th layer, and it is divided into blocks to form the 0th layer tile matrix. On the basis of the 0th layer, the 1st layer is generated by synthesizing each 2×2 pixels into one pixel, and it is divided into blocks to form the 1st layer tile matrix. This goes on to form the entire tile pyramid, in which the tile pyramid segmentation of the image mainly includes two most important concepts: tiles and pyramids:

[0118] Tile: A tile is a picture divided into several small square grids according to a certain scale. Each grid is a tile. Pyramid: An image is divided into areas from small to large according to the needs of the user. After the division, the image forms a pyramid structure with a scale from small to large and a data volume from small to large. The user can perform certain processing operations on each area. Figure 6A pyramidal structure is described. Among them, the "map tile dataset" is the root directory of the meteorological cloud map tile file data. The directories below it are the map tile levels (the naming method of the directory name: "L + level", L1, L2, L3,...). Under the map tile level directory, the rows of the map tile matrix at this level are used as directories (the naming method of the directory name: "R + row number", R0, R1, R2,...). Under the row directory are the specific map tile files (the naming method of the file name: "C + column number", C0.png (or C0.jpg), C1.png (or C1.jpg), C2.png (or C2.jpg),...).

[0119] The tile level is to classify the map according to the display scale or ground resolution. The calculation method of the display scale is as follows:

[0120] Display scale = 1: ground resolution × screen resolution / (0.0254 m / inch);

[0121] Among them:

[0122] Ground resolution = [cos(latitude × pi / 180) × 2 × pi × earth's semi-major axis (m)] / (256 × 2^level pixels); The latitude uses the equatorial latitude, that is, the latitude is 0; pi is the pi; The earth's semi-major axis takes the parameter specified by the 2000 National Geodetic Coordinate System, which is 6378137 m; Level represents the level of the scale, with the minimum being 0; The screen resolution value is 96 dpi.

[0123] Thus, the cloud map level is determined, as shown in Table 1. When making an electronic map, each level should correspond to the corresponding scale data source listed in Table 1, and the selection of its element content should follow the following principles:

[0124] A. On the premise that the map load of each level of the map is adapted to the corresponding display scale, as much information of the data source as possible should be retained completely;

[0125] B. The element content of the next level should not be less than that of the previous level, that is, as the display scale increases continuously, the element content increases continuously;

[0126] C. When selecting elements, the smooth transition of cross-level data calls should be ensured, that is, the change in the map load of adjacent two levels is relatively gentle.

[0127] Table 1 Meteorological Cloud Map Slicing Classification

[0128]

[0129] According to the respective parameters in the WMS and WMTS services of the OGC open protocol, complete the functions of GetMap, GetCapabilites, and GetFeatureInfo, improve the response to the client's requests, perform real-time image base map service updates within 24 hours after image acquisition, select the set-top box as the carrier of the front-end server, and the image map service and metadata service provided by the set-top box are standard-compliant OGC-WMS services, which can be seamlessly accessed by common GIS software platforms, meeting the project requirements; through the front-end server, publish the generated image tile data, referring to the WMS service and WMTS service in OGC. First, obtain the request URL of the client and extract the parameters that meet the OGC standard from it; secondly, parse each parameter and convert it into a data structure understandable by the front-end server; based on this data structure, connect to the database, obtain data from it for a series of operations; return the data after the operations are completed to the front-end and display it superimposed on the 3D earth.

[0130] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0131] Other embodiments of the present disclosure will be readily contemplated by those skilled in the art upon consideration of the disclosure in the specification and the embodiments. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include known common general knowledge or conventional technical means in the technical field not disclosed in the present disclosure. The specification and the embodiments are only regarded as exemplary, and the true scope and spirit of the present disclosure are pointed out by the claims.

[0132] It should be understood that the present disclosure is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present disclosure is only limited by the appended claims.

Claims

1. A method for predicting the coverage of a remote sensing satellite considering meteorological prediction cloud images and sensor pendulum measurements, characterized in that, It includes the following steps: Obtain the TLE orbit parameters corresponding to the satellite through a TLE orbit real-time acquisition program, and store the TLE orbit parameters in a local database. The TLE orbit parameters are expressions used to describe the position and velocity of a space vehicle; Obtain several of the TLE orbit parameters within a preset time period length, and predict and simulate the future orbit of the satellite through the SGP4 / SDP4 satellite orbit calculation method. Generate satellite orbit prediction information. The SGP4 / SDP4 satellite orbit calculation method calculates the satellite orbit based on the initial satellite motion state and perturbing forces. The perturbing forces include the Earth and non-spherical gravity, solar and lunar gravity, solar radiation, and atmospheric drag; Establish a data orbit library based on several of the satellite orbit prediction information for a future preset time period, and set a width buffer according to the preset width of the satellite. The width is used to characterize the strip width of the satellite's ground shooting. The steps of establishing a data orbit library based on several of the satellite orbit prediction information for a future preset time period and setting a width buffer according to the preset width of the satellite specifically include: when the TLE orbit parameters are successfully stored in the local database, perform future one-week orbit parameter prediction calculations in a serial manner; calculate the position information of the satellite orbit in the WGS84 ellipsoid framework for a future preset duration at a preset step T for each epoch; set a buffer area for the default coverage area according to the width of each satellite. After connecting the predicted points of the position information front and back, form a line graph with a direction. Perform spatial buffering on the line graph according to the width, using the single-line fixed-distance buffering method to obtain the orbit prediction coverage of this point; store the obtained position information and the orbit prediction coverage in the database; Establish the shooting coverage ability attribute of the satellite based on the orbit height, the width, and the maximum swing angle of each satellite. Obtain the shooting model corresponding to the satellite according to the coverage ability attribute, and introduce meteorological cloud map prediction data. Simulate the shooting ability of the satellite in a specified area in the future period according to the meteorological cloud map prediction data; Satellite swing parameter simulation and swing coverage width calculation. Calculate and obtain the swing coverage width actually covered by the satellite according to the swing angle of the satellite, the ground height of the satellite, the width, and a preset width calculation method; Receive the prediction range from the user, perform an intersection operation on the coverage result sets of multiple satellites and the cloud map vector coverage results at the transit time of each satellite to obtain a spatial intersection result set. The spatial intersection result set is used to characterize the effective shooting area at the prediction moment.

2. The method for predicting the coverage of a remote sensing satellite considering meteorological prediction cloud images and sensor pendulum measurements according to claim 1, characterized in that, It also includes the steps: Establish a three-dimensional dynamic model and a two-dimensional dynamic model of the satellite based on the satellite orbit prediction information and a preset simulation system and output them. Receive user query information and output the feedback content corresponding to the queried satellite.

3. The method for predicting the coverage of a remote sensing satellite considering meteorological prediction cloud images and sensor pendulum measurements according to claim 1, characterized in that, The steps of obtaining the TLE orbit parameters corresponding to the satellite through a TLE orbit real-time acquisition program and storing the TLE orbit parameters in a local database specifically include: Determine whether the target platform provides an API. If the API is not provided, obtain the TLE orbit parameters through a preset real-time acquisition program; Data structure analysis and data storage. First, determine the required fields, determine the construction table and connection relationship according to the required fields, and select a local database for storage; Data flow analysis, including determining the acquisition range and accessing the source, jumping between multi-layer web page structures, range subdivision, access mode analysis, URL and parameter analysis; Data acquisition. Use the scrapy module, BeautifulSoup parsing tool and Pandas method to organize the data and write it into the local database.

4. The method for predicting the coverage of a remote sensing satellite considering meteorological prediction cloud images and sensor pendulum measurements according to claim 3, characterized in that, The steps of predicting and simulating the future orbit of the satellite by the SGP4 / SDP4 satellite orbit calculation method to generate satellite orbit prediction information specifically include: Introduce the TLE orbit parameters and use the TLE orbit parameters to restore the mean orbital elements; Calculate the long-term term, long-period term, and short-period term according to the mean orbital elements, and calculate the spatial point position and velocity at the prediction time point.

5. The method for predicting the coverage of a remote sensing satellite considering meteorological prediction cloud images and sensor pendulum measurements according to claim 1, characterized in that, It also includes the network publishing steps of meteorological cloud maps: Obtain the meteorological cloud layer distribution for multiple days in the future at a preset update duration, perform mathematical simulation according to the meteorological cloud layer distribution, and store the prediction layer results of the mathematical simulation in time series; Store the prediction layer results in GeoJson format in raster data form and spatial vector data form respectively. In the raster data form, the area not covered by clouds is set as a transparent value, and the area covered by clouds is set as a gray transition color value from black to white according to the cloud thickness. The vector data form is used to record the range of cloud amount.

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