Method and device for predicting long-period imaging opportunity of imaging satellite, electronic equipment and medium

Abstract

The invention provides an imaging satellite long-period imaging opportunity prediction method, which comprises the following steps of: continuously monitoring instantaneous satellite orbit elements generated by global navigation satellite system data of an imaging satellite, and taking latitude and longitude coordinate values of satellite instantaneous orbit ground projection points in a satellite orbit maintenance period as observation sample data; analyzing the change rule of the orbit; according to observation sample data, piecewise fitting the orbit change model to obtain an orbit parameter fitting value and a dynamic change compensation value in an orbit maintenance period, and establishing an initial satellite orbit reference value based on orbit parameters at epoch time after orbit maintenance is completed; outputting the forecast of the long-period satellite orbit key parameter information table of the imaging satellite by using the fitted long-period orbit dynamic change compensation value; and calculating and forecasting imaging opportunities of a plurality of satellites in a long time by using the obtained long-period satellite orbit key parameter information table in combination with imaging constraint conditions of the satellites and position information of a target area.

Description

Technical Field

[0001] The present invention relates to the technical field of natural satellite imaging prediction, and in particular to a method, device, electronic equipment and medium for predicting long-period imaging opportunities of an imaging satellite. Background Art

[0002] In the context of the rapid development of remote sensing satellite technology, the accuracy and timeliness of imaging opportunity forecasts are particularly important as a prerequisite for ensuring efficient satellite imaging operations. Currently, imaging opportunity forecasts mainly rely on satellite orbital parameters and advanced orbit calculation models. By accurately predicting the satellite's spatial position and its geometric relationship with ground targets in a specific time period in the future, combined with imaging constraints, reasonable imaging opportunity forecasts are given.

[0003] In the traditional imaging opportunity prediction process, data provided by the onboard Global Navigation Satellite System (GNSS), such as instantaneous root data or two-row orbital elements (TLE), are widely used as input, and classical algorithms such as the High Precision Orbit Propagation (HPOP) numerical integration model or the Simplified General Perturbation Model (SGP4) are used to infer the precise position of the satellite at a certain moment or time period in the future. The imaging opportunities of the imaging targets are calculated and the imaging mission plan is formulated based on the spatial constraint relationship between the predicted satellite spatiotemporal position and the planned imaging target.

[0004] However, for remote sensing satellites operating in low orbits (usually orbits with an altitude of less than 1,000 kilometers), their orbits are significantly affected by the Earth's gravity and atmospheric drag, resulting in rapid orbital decay and frequent changes in orbital parameters. This dynamic change causes the prediction model based on fixed epoch orbital parameters to produce significant cumulative errors on a long time scale (such as more than one week), which directly affects the prediction accuracy of imaging opportunities. Specifically, when the forecast time is extended, the cumulative error gradually accumulates and may exceed the tolerance range of the imaging system, resulting in calculation errors of imaging opportunities or even prediction failures.

[0005] Taking a typical 4D commercial remote sensing satellite as an example, its orbital altitude is about 500 kilometers. Even with the most advanced orbital calculation model (such as HPOP with instantaneous root data, or SGP4 with TLE data), its forecast accuracy can only support short-term (2-3 days) imaging mission planning, and it is difficult to plan missions within a week. For slightly higher orbital altitudes (such as 600-700 kilometers of high-resolution multi-mode satellites, Beijing-3B satellites, etc.), although the orbital decay rate is relatively slow, it also faces the problem of reduced long-term forecast accuracy, and can usually only support imaging planning within the next 2-3 weeks.

[0006] Faced with the growing demand for global wide-area and intercontinental imaging, higher requirements are placed on the planning cycle of remote sensing satellite imaging missions, requiring long-term planning on a monthly, quarterly, and even annual scale. However, the existing short-term prediction methods based on single-epoch orbital parameters are no longer able to meet this demand, and a technical means is urgently needed to accurately predict long-term orbital parameters to ensure high-precision mission planning and execution capabilities in global wide-area imaging missions.

[0007] Therefore, developing an efficient and accurate long-period orbit prediction method has become a key issue that needs to be urgently solved in the current field of satellite operation and control, and is of great significance for improving the imaging efficiency and business competitiveness of remote sensing satellites. Summary of the invention

[0008] In view of this, the embodiments of the present disclosure provide a method, device, electronic device and medium for predicting long-period imaging opportunities of an imaging satellite, which at least partially solve the problems existing in the prior art.

[0009] In a first aspect, an embodiment of the present disclosure provides a method for predicting long-period imaging opportunities of an imaging satellite, comprising: Continuously monitor the number of instantaneous satellite orbit elements generated by the global navigation satellite system data of the imaging satellite, use the longitude and latitude coordinate values ​​of the ground projection points of the satellite instantaneous orbit within a satellite orbit maintenance period as observation sample data, and analyze the change law of the satellite orbit within a satellite orbit maintenance period; According to the observed sample data, the satellite orbit change model is fitted in segments to obtain the satellite orbit parameter fitting value and the dynamic change compensation value within the satellite orbit maintenance period, the initial satellite orbit reference value is established based on the satellite orbit parameter at the epoch time after the satellite orbit maintenance is completed, and the forecast of the long-period satellite orbit key parameter information table of the imaging satellite is output using the fitted long-period satellite orbit dynamic change compensation value; By using the obtained long-period satellite orbit key parameter information table, combined with the satellite's imaging constraints and the location information of the target area, the imaging opportunities of multiple satellites in the long term in the future are calculated and predicted.

[0010] According to a specific implementation of an embodiment of the present disclosure, the analyzing the change law of the satellite orbit within a satellite orbit maintenance period includes: In the early stage of satellite orbit maintenance, the satellite orbit projection is evenly distributed, and the imaging opportunities of target points in various places are relatively fixed; In the mid-term stage of satellite orbit maintenance, the imaging opportunities of the target points in various places are averaged; During the final stage of satellite orbit maintenance, some target points have dense imaging opportunities, while some target points have no imaging opportunities.

[0011] According to a specific implementation manner of an embodiment of the present disclosure, the piecewise fitting satellite orbit change model uses a polynomial least squares method to piecewise fit the satellite orbit change.

[0012] According to a specific implementation of an embodiment of the present disclosure, outputting a forecast of a long-period satellite orbit key parameter information table of the imaging satellite includes: Obtain the geographic longitude value of the descending node of the descending orbit of the same satellite as the fitting value; Determine the fitting equation; Determine the fitting model; The global regions are divided, and the geographical longitude points of the descending nodes of the daily satellite orbits are output according to the divided regions to form a satellite orbit information forecast table.

[0013] According to a specific implementation manner of the embodiment of the present disclosure, the determined fitting equation is a cubic polynomial equation; the determined fitting model includes: using a given empirical limit value and reducing the power to calculate the fitting equation.

[0014] According to a specific implementation method of an embodiment of the present disclosure, the method of calculating and predicting imaging opportunities for multiple satellites in the long term in the future includes: calculating the three-dimensional spatial position of the satellite in the target area based on satellite orbit parameters; calculating the geographic longitude of the descending node of the satellite orbit based on the satellite orbit parameters; comparing the geographic longitude of the descending node of the satellite orbit with the longitude range of the target area to determine whether there is overlap; for time points with overlap, further checking whether the imaging constraints of the satellite are met; and recording the time points that meet all imaging constraints as imaging opportunities.

[0015] According to a specific implementation method of an embodiment of the present disclosure, the comparison of the geographic longitude of the descending node of the satellite orbit with the longitude range of the target area includes: selecting the satellite orbit projection line closest to the center or edge of the target area as a reference, and calculating the difference between the satellite orbit projection line and the longitude of the boundary of the target area; back-calculating the difference to the equatorial plane; and simplifying the spatial and temporal position relationship between the geographic longitude point of the satellite orbit at the equator and the target area to the overlap or proximity problem between the point and the line on the equator.

[0016] In a second aspect, an embodiment of the present disclosure provides a device for predicting long-period imaging opportunities of an imaging satellite, comprising: Satellite orbit data monitoring module: used to continuously monitor the global navigation satellite system data of imaging satellites and obtain the instantaneous satellite orbit elements of satellites; Satellite orbit change analysis module: taking the latitude and longitude coordinates of the ground projection point of the satellite instantaneous satellite orbit within a satellite orbit maintenance period as observation sample data, analyzing and determining the change law of the satellite orbit within the satellite orbit maintenance period; Satellite orbit model fitting and prediction module: Based on the observation sample data provided by the satellite orbit change analysis module, a satellite orbit change model is constructed using a piecewise fitting method; Calculate the satellite orbit parameter fitting value and dynamic change compensation value within the satellite orbit maintenance period; The initial satellite orbit reference value is established by using the satellite orbit parameters at the epoch time, and combined with the fitted long-period satellite orbit dynamic change compensation value, the forecast of the key parameter information table of the long-period satellite orbit of the imaging satellite is output; Imaging opportunity calculation and prediction module: Combined with the satellite's imaging constraints and the specific location information of the target area, the imaging opportunities of multiple satellites in the long term in the future are predicted through calculation and analysis.

[0017] In a third aspect, an embodiment of the present disclosure provides an electronic device, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the above-mentioned imaging satellite long-period imaging opportunity prediction method.

[0018] In a fourth aspect, an embodiment of the present disclosure provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the above-mentioned imaging satellite long-period imaging opportunity prediction method.

[0019] In summary, compared with the prior art, this embodiment has the following advantages: The long-term cumulative error phenomenon inherent in traditional orbit calculation models is suppressed, and through model fitting and prediction strategies, high-precision calculation of the orbit position prediction of low-orbit satellites on a long time scale is achieved. This method strictly controls the prediction error within the tolerance threshold that meets the strict constraints of satellite imaging missions, ensuring the reliability and accuracy of the prediction results.

[0020] In summary, the long-term and high-precision orbit prediction capability provides a solid data foundation for the planning of wide-area imaging missions, making it possible to accurately calculate and analyze the imaging opportunities of multiple low-orbit satellites over a long period of time in the future. This capability not only promotes the refined design of imaging missions, including the determination of the optimal observation window and the optimal configuration of imaging parameters, but also greatly enhances the flexibility and scientific nature of mission planning and program formulation.

[0021] In addition, this method provides technical support for low-orbit multi-satellite joint global imaging missions, making cross-satellite and cross-sensor collaborative imaging possible. By integrating multi-source satellite data and fusing multi-sensor information, the coverage, temporal resolution and data quality of global imaging missions can be significantly improved, thereby comprehensively improving the comprehensive efficiency and application value of multi-source multi-sensor satellite joint imaging systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific implementation methods and drawings. Here, the exemplary implementation methods of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0023] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0024] Figure 1 A schematic diagram of a flow chart of a method for predicting long-period imaging opportunities of an imaging satellite provided in a first embodiment of the present invention; Figure 2 A schematic diagram of the framework of a method for predicting long-period imaging opportunities for imaging satellites provided in an embodiment of the present application; Figure 3A This is a schematic diagram of the change in the geographical longitude of the descending node every other day within one orbit maintenance cycle; Figure 3B This is a schematic diagram of the observation of the long-period variation of orbital configuration; Figure 3C Schematic diagram of the initial orbital projection configuration for orbit maintenance; Figure 3D This is a schematic diagram of the mid-term orbital projection configuration; Figure 3E This is a schematic diagram of the final orbital projection configuration; Figure 4A This is a schematic diagram of the fitting error of the geographic longitude of the descending node; Figure 4B This is a schematic diagram of fitting error of the geographic longitude difference of the descending node; Figure 4C This is the trend chart of the difference in geographical longitude of the descending node; Figure 4D Schematic diagram of 15 imaging areas; Figure 4EThis is a schematic diagram of the track information forecast table; Figure 4F Schematic diagram of the comparison between the orbital instantaneous root calculation value and the orbital fitting value for 100 consecutive days Figure 5A A schematic diagram of the mission planning process for the eastern order-intensive area; Figure 5B-5C Schematic diagram of imaging opportunities in the eastern mission concentration area; Figure 5D This is a schematic diagram of the task planning results for the eastern order-intensive area; Figure 6 A schematic structural diagram of a device for predicting long-period imaging opportunities for an imaging satellite provided in accordance with a second embodiment of the present invention.

[0025] Figure 7 An exemplary structural diagram of a device capable of implementing the method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0026] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0027] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0028] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0029] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0030] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0031] See also Figure 1 The first embodiment of the present invention provides a method for predicting long-period imaging opportunities of an imaging satellite, which can be executed by an electronic device, and in particular, by one or more processors in the electronic device, to implement the following steps: S101, analyze orbital patterns and establish observation samples.

[0032] In this embodiment, the instantaneous orbital elements generated by the global navigation satellite system data of the imaging satellite are continuously monitored, and the longitude and latitude coordinate values ​​of the ground projection points of the satellite's instantaneous orbit within an orbit maintenance period are used as observation sample data to analyze the change pattern of the satellite's orbit within an orbit maintenance period.

[0033] This embodiment is based on continuous monitoring and analysis of GNSS data. Using GNSS receivers, long-term and continuous orbit data collection of the 4D high-view series of low-orbit satellites is performed. These data may include key parameters such as the position, speed, acceleration, etc. of the satellite, which is the basis for subsequent analysis.

[0034] Based on the collected GNSS data, the orbit determination algorithm is used to calculate the instantaneous orbit elements of the satellite (such as semi-major axis, eccentricity, inclination, right ascension of ascending node, argument of perigee and mean anomaly). These elements can accurately describe the orbit state of the satellite at any time.

[0035] Clarify the satellite's orbit maintenance period, that is, the time interval between one orbit adjustment and the next. This is the time frame for analyzing the law of orbit changes.

[0036] Based on each instantaneous orbital element, the ground projection point of the satellite orbit is calculated and its longitude and latitude coordinates are obtained. These coordinates will be used as data samples for analyzing the orbital change law. With the orbit maintenance period as the time unit, a data sample set containing the longitude and latitude coordinates of the ground projection points of the satellite instantaneous orbit is constructed. This sample set should cover the entire orbit maintenance period to fully reflect the dynamic changes of the satellite orbit.

[0037] Statistical methods are used to analyze the latitude and longitude coordinate values ​​in the data sample set to identify the main trends and periodic characteristics of orbital changes. Based on the statistical analysis results, mathematical models or machine learning algorithms are established to predict the future change trends of satellite orbits. This helps to plan satellite observation missions and orbit adjustment strategies in advance.

[0038] Specifically, the high-resolution optical satellites of the 4D Gaojing series, as a type of satellite operating at a low orbit altitude of about 500 kilometers, have significant orbital attitude characteristics, which are specifically manifested in a faster natural decay rate, which requires periodic orbit correction to maintain its operational stability. At present, each orbit maintenance period is set to about 145 days, during which the linear velocity of the satellite gradually increases due to subtle adjustments in the orbital attitude. This change is particularly obvious from the perspective of the Earth-centered Earth-fixed (ECEF) coordinate system, which is reflected in the gradual reduction of the projection spacing of the satellite's orbit around the earth's surface. The number of orbits per day is not a fixed value, but presents a trend of gradual growth. Therefore, the ground projection morphology of the satellite orbit undergoes significant changes, which directly leads to dynamic changes in the revisit time interval of ground target points.

[0039] In order to accurately analyze this complex dynamic process, the geographic longitude of the descending node is selected as the key metric. Through careful observation, it is found that within a complete orbit maintenance cycle, the relative position change range of the satellite orbit's projected descending node on the ground in geographic longitude between two consecutive days is stable between 560 and 680 kilometers, and this change follows a certain periodic law. This discovery is based on nearly a year of SV2-01 satellite instantaneous root number data (covering June 2023 to April 2024), see Figure 3A-3B . Figure 3A A schematic diagram showing the change in the geographical longitude of the descending node every other day within one orbit maintenance cycle. Figure 3B A schematic diagram showing the observation of the long-period changes in orbital configuration.

[0040] During the regular orbit maintenance period of the 4D Gaojing series satellites (about 145 days), the satellite's imaging opportunities are closely related to the temporal and spatial evolution of the orbital configuration, showing significant changes. This periodic process can be divided into several key stages, each of which is accompanied by a specific change pattern of the sub-satellite point projection, thus affecting the distribution of satellite imaging opportunities for ground targets.

[0041] Initial orbit maintenance configuration analysis: In the initial orbit maintenance period, which starts approximately in the first month after the orbit maintenance operation (taking orbit maintenance in early June 2023 as an example), refer to Figure 3C , Figure 3CThe schematic diagram of the orbital projection configuration in the initial stage of orbit maintenance is shown. At this time, the orbital projection of the satellite is evenly distributed. The characteristic of this stage is that the satellite can cover ground targets in different regions relatively stably, so that the imaging opportunities at various locations are relatively fixed.

[0042] As time goes by, entering the mid-term stage of orbit maintenance (approximately 2 to 3 months after orbit maintenance, corresponding to July to August 2023), the satellite's orbital projection distribution changes significantly, showing a more complex and diverse position distribution pattern. During this stage, the satellite's orbital configuration adjustment makes its coverage of ground targets more flexible, and the imaging opportunities in different regions tend to be averaged, closer to the theoretical uniform distribution state. Figure 3D , Figure 3D A schematic diagram of the mid-term orbital projection configuration is shown.

[0043] At the end of the orbit maintenance period, the satellite's orbital projection is no longer as uniform or diverse as in the early and middle stages, but tends to form dense coverage in some areas, while leaving obvious gaps in other areas. This uneven projection distribution directly leads to great differences in imaging opportunities: on the one hand, some surface points may not receive imaging opportunities for up to two weeks or even longer, seriously affecting the data update and monitoring needs of these areas; on the other hand, points adjacent to these "blind areas" and only a few hundred kilometers away may enjoy multiple days of multi-satellite imaging opportunities due to the specific position of the satellite orbit, and the imaging frequency is far above the average. See Figure 3E , Figure 3E A schematic diagram of the terminal orbital projection configuration is shown. This unbalanced distribution of imaging opportunities is clearly visible, which not only tests the flexibility and adaptability of satellite mission planners, but also highlights the importance of optimizing satellite orbit design and maintenance strategies. To alleviate this problem, additional orbit adjustment measures or supplementary observations using other satellite resources may be required to ensure balanced coverage and timely imaging of ground target points around the world.

[0044] In summary, by monitoring the dynamic changes of satellite orbital parameters on a daily basis, we analyze and discover the periodic changes of orbits. We extract key orbital information values ​​from the precise orbital parameters obtained daily and the orbital calculation values ​​calculated by the corresponding orbital calculation model, establish orbital data analysis samples, and perform reasonable extrapolation of the piecewise least squares model of satellite orbital parameters to obtain accurate long-period satellite orbital data.

[0045] S102, establishing an orbit fitting model and outputting an orbit prediction information table.

[0046] In this embodiment, the orbit change model is fitted in segments according to the observed sample data to obtain the orbit parameter fitting values ​​and dynamic change compensation values ​​within the orbit maintenance period, and the initial orbit reference values ​​are established based on the satellite orbit parameters at the epoch time after the orbit maintenance is completed. The fitted long-period orbit dynamic change compensation values ​​are used to output a forecast of the long-period orbit key parameter information table of the imaging satellite.

[0047] Specifically, the observed sample data determined in step S101 can be cleaned to remove noise and outliers to ensure the accuracy and reliability of the data. In view of the complexity and nonlinear characteristics of orbital changes, a segmented fitting method is used to fit orbital changes, and a polynomial least squares method can be used to fit orbital changes. For example, the observed data is segmented by time or space, and the polynomial least squares method is applied to each segment of data for fitting, to obtain an approximate polynomial expression of each segment of the orbit.

[0048] The fitting values ​​of key parameters within the orbit maintenance period (such as semi-major axis, eccentricity, inclination, etc.) are extracted from the fitted polynomial, and dynamic change compensation values ​​are calculated to reflect the subtle changes of the orbit over time.

[0049] In order to further improve the accuracy and robustness of orbit prediction, Kalman filtering technology can be combined with piecewise polynomial fitting. Kalman filtering can use historical data and current observations to estimate orbit status through a prediction-update cycle and automatically adjust the prediction error.

[0050] After the orbit maintenance mission is completed, the initial orbit reference values ​​are established based on the precise satellite orbit parameters at the epoch time (i.e., a specific time point). These parameters will serve as the basis for subsequent orbit predictions. The initial orbit reference values ​​are adjusted using the long-term orbit dynamic change compensation values ​​obtained from piecewise polynomial fitting (and possible Kalman filtering) to reflect the orbit change trend over a long period of time in the future. Based on the adjusted orbital parameters, a forecast of the long-period orbit key parameter information table of the low-orbit imaging satellite is generated. The table should contain the predicted orbit parameter values ​​at multiple future time points, as well as the corresponding prediction error range.

[0051] Specifically, the orbital projection position of day T0 is calculated and output using the current commonly used satellite orbit calculation model. The geographic longitude of the descending node of the orbital projection is used as the key information for subsequent orbit calculations. According to the orbital data analysis sample, the piecewise least squares fitting model is used to predict the sub-satellite position of the orbital descending node geographic longitude of the satellite in the future for a long time (such as more than 140 days), and the daily orbital descending node geographic longitude point position is output to form a satellite orbit lookup table. The specific process is as follows: 1. Determine the fitted values.

[0052] Accurately select the time window, for example, determine the UTC (Coordinated Universal Time) time 1:00 to 2:36 as the data collection window to ensure that the same descending node geographic longitude data is captured to reduce the uncertainty caused by orbit changes. Figure 4A , Figure 4A The figure shows the fitting error diagram of the geographic longitude of the descending node. By analyzing the historical data of the geographic longitude of the descending node, a periodic fluctuation pattern every 4 days is identified, which will produce a large error area when fitting.

[0053] The daily change of the descending node's geographic longitude difference follows a stable increasing trend. When this difference reaches the preset limit threshold, the orbit maintenance mechanism will be activated. In view of this, the descending node's geographic longitude difference is taken as the key fitting item, and the reference Figure 4B , Figure 4B A schematic diagram of the fitting error of the geographic longitude difference of the descending node is shown.

[0054] 2. l Determine the fitting equation.

[0055] In this embodiment, according to the changing trend of the difference in geographical longitude of the descending node within a complete cycle, see Figure 4C , Figure 4C The trend graph of the difference in geographic longitude of the descending node is shown. Comparing the trend of the function graphs of the linear equation, quadratic equation, and cubic equation, the trend of the cubic equation is more consistent with the trend of the difference in geographic longitude of the descending node, so the fitting equation is a cubic polynomial equation.

[0056] 3. Determine the fitting model.

[0057] In this embodiment, for the stable orbit period (about 145 days) of the 4D Gaojing series satellites, a three-stage fitting model is used to accurately simulate the changes in the geographic longitude difference of the descending node. Specifically, the orbit period is divided into three intervals: 0 to 50 days is the first interval, 50 to 100 days is the second interval, and 100 days to the number of days before the next orbit maintenance is the third interval. In each interval, the geographic longitude difference of the descending node is fitted independently.

[0058] Considering that there may be incomplete true value data in the interval, this will cause the error of the estimated value of the fitting equation to accumulate over time. To reduce this error, first, set a reasonable empirical limit value as a reference; second, use the power reduction calculation method in the fitting process, that is, give priority to fitting with lower-order polynomials to balance model complexity and data adaptability; if there is no true value data in a certain interval, construct a first-order fitting equation based on the empirical limit value as a temporary substitute.

[0059] In addition, in order to further improve the fitting accuracy, a method of dynamically calculating the parameters of the fitting equation is adopted. Whenever new orbital data is obtained, the parameters of the fitting equation are iteratively updated to ensure that the model can reflect the latest status of the satellite orbit in real time.

[0060] 4. Global regional divisions.

[0061] For example, based on the orbital projection line of SVN01-1 on November 8, a detailed division of the global imaging area was performed, and a total of about 15 independent imaging areas were defined. Figure 4D , Figure 4D A schematic diagram of 15 imaging areas is shown. This division ensures a more accurate capture of the geographic range covered by the satellite orbit and facilitates subsequent data processing and analysis.

[0062] According to these divided areas, the geographic longitude position information of the descending node of the satellite orbit is calculated and output daily. This information is integrated into the satellite orbit information forecast table, see Figure 4E , Figure 4E The schematic diagram of the orbit information forecast table is shown. The orbit information forecast table not only records the position information of the satellite at different time points, but also intuitively displays the coverage of the satellite to various regions around the world.

[0063] Through this forecast table, users can clearly understand the satellite's orbital trajectory, coverage area and daily imaging plan, providing an important reference for subsequent satellite mission planning, data reception and processing.

[0064] 5. Benchmark verification.

[0065] During the benchmarking verification, a time window of about 100 days was selected, and the high-precision HPOP (or similar high-precision orbit prediction models) was used as the reference standard. Every day, based on the instantaneous root data at the current moment, the orbit calculation values ​​for the next day are generated under the HPOP model. These values ​​represent high-precision predictions of the future orbital state of the satellite under current technical conditions. Subsequently, the prediction results under these HPOP models are compared and analyzed with the orbital prediction fitting values ​​previously obtained through the three-stage fitting model (or other optimization models). Through careful comparative verification, it is found that the orbit change trends predicted by the two models are in good agreement, which shows that the adopted fitting model has high prediction accuracy and stability on a long time scale. See Figure 4F , Figure 4F A schematic diagram showing the comparison between the orbital instantaneous root calculation values ​​and the orbital fitting values ​​for 100 consecutive days is shown.

[0066] S103, using the obtained long-period orbit key parameter information table, combined with the satellite's imaging constraints and the location information of the target area, calculate and predict the imaging opportunities of multiple satellites in the long term in the future.

[0067] In this embodiment, the three-dimensional dynamic space-time position relationship obtained by combining the satellite ephemeris orbit data with the target position information is converted into an overlapping relationship between the satellite orbit descending node and the target area, and the change of the overlapping relationship is predicted through long-term forecasting. Combined with the imaging constraints of different satellites, forecast calculations of multi-satellite long-period imaging opportunities are carried out.

[0068] Specifically, the long-period orbit information table contains the orbital parameters of each satellite at multiple time points in the future (such as semi-major axis, eccentricity, inclination, right ascension of ascending node, argument of perigee, etc.) and the predicted error range.

[0069] The target area location information is the geographic coordinate range (longitude, latitude), shape and size of the target area. Satellite imaging constraints include satellite imaging resolution, imaging angle limit, imaging time window, etc. According to the long-period orbit information table, the satellite orbit parameters at any given time point can be calculated using appropriate interpolation methods (such as linear interpolation, polynomial interpolation, etc.). For example, the orbital parameters obtained by interpolation can be used to calculate the three-dimensional spatial position of the satellite in the target area. Based on the satellite's orbital parameters, the geographic longitude of the descending node of the satellite orbit is calculated.

[0070] Compare the geographic longitude of the descending node of the satellite orbit with the longitude range of the target area to determine whether there is overlap. For overlapping time points, further verify whether the satellite's imaging constraints are met (such as whether the imaging angle is within the allowable range, whether the imaging time is within the time window, etc.).

[0071] The time points that meet all imaging constraints are recorded as imaging opportunities, and the corresponding satellite number, time, imaging conditions, etc. are marked. For imaging opportunities of multiple satellites, coordination and optimization are carried out to avoid waste and conflict of imaging resources.

[0072] The calculated imaging opportunities can be output in a table format, including detailed information such as satellite number, imaging time, target area, imaging conditions, etc. It is also possible to visualize the imaging opportunities on a map using tools such as Geographic Information Systems (GIS), which facilitates an intuitive understanding of the satellite's imaging capabilities and coverage.

[0073] In this embodiment, when processing the imaging target area, geographic coordinate conversion and mapping are performed for a specific target area (taking Guangdong Province as an example). First, the longitude and latitude coordinate range in the east-west direction of Guangdong Province is determined. In order to map this area to the equatorial plane for more intuitive orbit planning and analysis, the method of the nearest orbit projection line is adopted. Specifically, the satellite orbit projection line closest to the center or edge of the target area is selected as a reference, and by calculating the difference between the projection line and the longitude of the east-west boundary of the target area, these differences can be back-calculated to the equatorial plane.

[0074] Through the above conversion method, the spatial and temporal position relationship between the geographic longitude point of the satellite orbit at the equator and the target area is simplified to the overlap or proximity problem between points and lines on the equator. Next, the imaging constraints of different satellites (such as the satellite's observation angle, imaging resolution, cloud coverage, etc.) are further quantified as the distance relationship between points and lines. Specifically, one or more "effective imaging distance" thresholds are defined. When the distance between the satellite's projection point and the line segment of the target area is less than or equal to these thresholds, it is considered that there is an effective imaging opportunity.

[0075] Based on this quantitative standard, the imaging opportunities of each satellite in the future can be calculated, and the imaging opportunities of multiple satellites with long periods can be predicted and analyzed. This analysis process not only takes into account the imaging capabilities of a single satellite, but also integrates the orbital characteristics, imaging parameters and spatiotemporal changes of multiple satellites, thereby achieving the optimal configuration and efficient use of satellite imaging resources.

[0076] This method can provide strong support for subsequent work such as satellite mission planning, data acquisition and processing, ensuring that satellite imaging missions can proceed smoothly as planned and maximize user needs.

[0077] In summary, in order to meet the application needs of multi-satellite joint global wide-area long-period imaging, a set of long-period orbit position prediction methods is provided. This method aims to ensure accurate prediction of the orbit attitude of low-orbit imaging satellites through efficient data processing and analysis, thereby supporting the planning and execution of long-period imaging tasks. First, collect the real-time orbit attitude data of low-orbit imaging satellites, including position (longitude, latitude, altitude), velocity vector, attitude angle (pitch, yaw, roll), etc. Use the piecewise least squares method (or more advanced optimization algorithms such as genetic algorithms, particle swarm optimization, etc.) to fit the measured data and determine the key parameters in the model, such as the six elements of the initial orbit, perturbation coefficients, etc. Based on the fitted model, extract the key parameters that affect the long-term changes of the orbit, such as the average semi-major axis, eccentricity, inclination, etc. Develop or select mature long-term orbit prediction algorithms, such as numerical integration methods, analytical methods, etc., and combine the extracted key parameters to calculate the orbital position of the satellite at any time in the future for a long period of time (month, quarter, half a year). See Figure 2 , Figure 2 A schematic diagram of the framework of the imaging satellite long-period imaging opportunity prediction method provided in an embodiment of the present application is shown.

[0078] In order to further verify the effectiveness of the long-period imaging opportunity prediction method for imaging satellites provided in the embodiment of the present application, the embodiment of the present application verified the method in the scenario of "imaging analysis of satellite imaging order-intensive areas in the eastern region of my country".

[0079] Specifically, based on the method provided in the embodiments of the present application, taking the four-dimensional Gaojing No. 1 binary satellite and the GFDM satellite as examples, the long-period imaging mission in the eastern region of my country is planned. First of all, considering that the satellite orbits the earth about 15 times a day, and each circle is about 24 degrees apart, the earth is divided into 15 equally divided orbital partitions along the satellite orbit. For the eastern region of my country, an area with intensive demand for remote sensing imaging, it is roughly located in the 5th zone of these 15 partitions. The eastern region of my country spans about 2,500 kilometers from east to west. In terms of satellite imaging needs, orders are mainly concentrated between two adjacent orbits, that is, within a longitude range of about 24 degrees. The imaging side swing angle constraint of the preset optical satellite is less than 25 degrees. Under this constraint, the maximum distance between the boundary point of satellite imaging and the sub-satellite point of the satellite is about 220 kilometers. Taking into account the satellite's left and right side-viewing capabilities, each imaging analysis area of ​​the target area is expanded to a width of approximately 440 kilometers (220 kilometers on each side) and projected along its centerline to the equator along the orbit to determine the corresponding five descending nodes, which are 101 degrees, 105 degrees, 109 degrees, 113 degrees, and 117 degrees. The analysis begins with the above five descending nodes as key inputs, combined with the constructed 15-region orbit imaging opportunity table. The longitude difference (i.e., offset) between the orbit ground projection and the centerline of the target area is automatically calculated for each satellite within the predetermined time. Specific imaging opportunities are generated for each satellite in different time windows based on imaging constraints (including swing angle restrictions, cloud cover predictions, lighting conditions, etc.). See Figure 5A , Figure 5A A schematic diagram of the task planning process in the eastern order-intensive area is shown.

[0080] In an orbit maintenance period of about 145 days, the imaging opportunities and corresponding side view angles of the above five areas are automatically and statistically calculated in the calculation table. Figure 5B-Figure 5C , Figure 5B and Figure 5C A schematic diagram of imaging opportunities in the eastern mission concentration area is shown.

[0081] Based on the above analysis of imaging opportunities, it is estimated that there will be 68 to 77 imaging opportunities in each area of ​​about 440 kilometers in a 145-day orbit maintenance period. Figure 5D , Figure 5D The schematic diagram of the mission planning results for the eastern order-intensive area is shown. The further 145-day cycle imaging planning results are shown in Table (1), which shows the statistics of imaging satellite resources for one orbit maintenance cycle (about 145 days) in the eastern order-intensive area.

[0082]

[0083] Through the above verification, it can be seen that the method provided by the embodiment of the present application has been effectively verified in the wide-area imaging mission scenario involving the joint execution of multiple satellites such as 4D Gaojing-1, 4D Gaojing-2 and Gaofen Multi-mode Satellite. By applying this method to the prediction and calculation of long-period imaging opportunities of low-orbit remote sensing satellites, the accuracy of the fitting calculation value of the orbital position forecast of long periods such as monthly and quarterly is successfully controlled within the tolerance range that meets the satellite imaging constraints. It provides solid support for the accurate calculation of wide-area long-period imaging opportunities, the meticulous planning and design of imaging tasks, and the efficient formulation of imaging plans.

[0084] At present, this engineering technology method has been fully realized in the code-level program algorithm, covering the entire process from automatic capture of orbital instantaneous root data, high-precision orbit fitting, real-time update of orbital lookup tables, to accurate calculation of imaging opportunities and long-term imaging forecasts. It not only improves the efficiency of data processing and analysis, but also greatly enhances the flexibility and response speed of interactive applications such as front-end satellite imaging planning, laying the foundation for the widespread application and efficient management of remote sensing satellites.

[0085] In summary, compared with the prior art, this embodiment has the following advantages: This method builds a model based on the measured data of the orbital attitude of low-orbit imaging satellites, and uses the piecewise least squares model to fit the key parameters of the orbital position within a complete orbit maintenance period. The orbital position at a certain moment in the long-term future (month, quarter, half a year) is calculated using the fitted key parameters of the orbital position to support the calculation of long-term imaging opportunities. By comparing the satellite orbit ephemeris data obtained by the latest orbital parameters every day, it is verified that this algorithm can ensure that the orbital position accuracy of the satellite sub-satellite point is maintained within 20km over a long period.

[0086] A second embodiment of the present invention provides a long-period imaging opportunity prediction device for an imaging satellite (eg Figure 6 shown), which includes: Satellite orbit data monitoring module 610: used to continuously monitor the global navigation satellite system data of the imaging satellite and obtain the instantaneous satellite orbit elements of the satellite; Satellite orbit change analysis module 620: uses the latitude and longitude coordinate values ​​of the ground projection point of the satellite instantaneous satellite orbit within a satellite orbit maintenance period as observation sample data, analyzes and determines the change law of the satellite orbit within the satellite orbit maintenance period; Satellite orbit model fitting and prediction module 630: Based on the observation sample data provided by the satellite orbit change analysis module, a satellite orbit change model is constructed using a piecewise fitting method; Calculate the satellite orbit parameter fitting value and dynamic change compensation value within the satellite orbit maintenance period; The initial satellite orbit reference value is established by using the satellite orbit parameters at the epoch time, and combined with the fitted long-period satellite orbit dynamic change compensation value, the forecast of the key parameter information table of the long-period satellite orbit of the imaging satellite is output; Imaging opportunity calculation and prediction module 640: Combined with the satellite's imaging constraints and the specific location information of the target area, through calculation and analysis, the imaging opportunities of multiple satellites in the long term in the future are predicted to provide data support for satellite imaging mission planning.

[0087] A third embodiment of the present invention further provides an electronic device, the electronic device comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the imaging satellite long-period imaging opportunity prediction method of any of the aforementioned embodiments.

[0088] The fourth embodiment of the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the imaging satellite long-period imaging opportunity prediction method described in any of the aforementioned embodiments.

[0089] The fifth embodiment of the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer executes the imaging satellite long-period imaging opportunity prediction method of any of the aforementioned embodiments.

[0090] The sixth embodiment of the present invention further provides a computer program, which includes program instructions. When the program instructions are executed by a computer, the computer is caused to execute the imaging satellite long-period imaging opportunity prediction method of any of the aforementioned embodiments.

[0091] Figure 7 A schematic diagram of a method or device 1000 that can implement an embodiment of the present invention is shown, which may include more or fewer devices than shown in the figure in some embodiments. In some embodiments, it can be implemented using a single or multiple devices. In some embodiments, it can be implemented using cloud or distributed devices.

[0092] like Figure 7As shown, the device 1000 includes a processor 1001, which can perform various appropriate operations and processes according to the program and / or data stored in the read-only memory (ROM) 1002 or the program and / or data loaded from the storage part 1008 to the random access memory (RAM) 1003. Processor 1001 can be a multi-core processor or include multiple processors. In some embodiments, processor 1001 can include a general main processor and one or more special coprocessors, such as a central processing unit (CPU), a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In RAM 1003, various programs and data required for the operation of device 1000 are also stored. Processor 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Input / output (I / O) interface 1005 is also connected to bus 1004.

[0093] The processor and the memory are used together to execute the program stored in the memory. When the program is executed by the computer, the methods, steps or functions described in the above embodiments can be implemented.

[0094] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, a touch screen, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed. Figure 7 Only some components are schematically shown in the figure, which does not mean that the device 1000 only includes Figure 7 Components shown.

[0095] The systems, devices, modules or units described in the above embodiments may be implemented by a computer or its associated components. The computer may be, for example, a mobile terminal, a smart phone, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a personal digital assistant, a media player, a navigation device, a game console, a tablet computer, a wearable device, a smart TV, an Internet of Things system, a smart home, an industrial computer, a server or a combination thereof.

[0096] Although not shown, in an embodiment of the present invention, a computer-readable storage medium is provided, on which a computer program / instruction is stored, and when the computer program / instruction is executed by a processor, the imaging satellite long-period imaging opportunity prediction method described in Example 1 is implemented.

[0097] Storage media in embodiments of the present invention include permanent and non-permanent, removable and non-removable items that can be used to store information by any method or technology. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0098] Although not shown, an embodiment of the present invention further provides a computer program product, including: a computer program / instruction, which, when executed by a processor, implements the imaging satellite long-period imaging opportunity prediction method described in Example 1.

[0099] The methods, programs, systems, devices, etc. of the embodiments of the present invention may be executed or implemented in a single or multiple networked computers, or may be practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks may be performed by remote processing devices connected via a communication network.

[0100] Those skilled in the art should understand that the embodiments of the present specification may be provided as methods, systems or computer program products. Therefore, those skilled in the art may imagine that the functional modules / units or controllers and related method steps described in the above embodiments may be implemented in software, hardware or a combination of software / hardware.

[0101] Unless explicitly stated, the actions or steps of the methods, programs, and embodiments of the present invention do not have to be performed in a specific order and can still achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0102] In this article, multiple embodiments of the present invention are described, but for the sake of simplicity, the description of each embodiment is not exhaustive, and the same or similar features or parts between the embodiments may be omitted. In this article, "one embodiment", "some embodiments", "example", "specific example", or "some examples" are intended to be applicable to at least one embodiment or example of the present invention, but not all embodiments. The above terms do not necessarily mean to refer to the same embodiment or example. In the absence of contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0103] The exemplary systems and methods of the present invention have been specifically shown and described with reference to the above embodiments, which are merely examples of the best modes for implementing the present systems and methods. It will be appreciated by those skilled in the art that various changes may be made to the embodiments of the systems and methods described herein when implementing the present systems and / or methods without departing from the spirit and scope of the present invention as defined in the appended claims.

Claims

1. A method for predicting long-period imaging opportunities of an imaging satellite, characterized in that: include: Continuously monitor the number of instantaneous satellite orbit elements generated by the global navigation satellite system data of the imaging satellite, use the longitude and latitude coordinate values ​​of the ground projection points of the satellite instantaneous orbit within a satellite orbit maintenance period as observation sample data, and analyze the change law of the satellite orbit within a satellite orbit maintenance period; According to the observed sample data, the satellite orbit change model is fitted in sections to obtain the satellite orbit parameter fitting values ​​and dynamic change compensation values ​​within the satellite orbit maintenance period, an initial satellite orbit reference value is established based on the satellite orbit parameter at the epoch time after the satellite orbit maintenance is completed, and a forecast of the long-period satellite orbit key parameter information table of the imaging satellite is output using the fitted long-period satellite orbit dynamic change compensation value; By using the obtained long-period satellite orbit key parameter information table, combined with the satellite's imaging constraints and the location information of the target area, the imaging opportunities of multiple satellites in the long term in the future are calculated and predicted.

2. The imaging satellite long-period imaging opportunity prediction method according to claim 1, characterized in that: The analysis of the changing law of the satellite orbit within a satellite orbit maintenance period includes: In the early stage of satellite orbit maintenance, the satellite orbit projection is evenly distributed, and the imaging opportunities of target points in various places are relatively fixed; In the mid-term stage of satellite orbit maintenance, the imaging opportunities of the target points in various places are averaged; During the final stage of satellite orbit maintenance, some target points have dense imaging opportunities, while some target points have no imaging opportunities.

3. The method for predicting long-period imaging opportunities of an imaging satellite according to any one of claims 1 to 2, characterized in that: The piecewise fitting satellite orbit change model uses a polynomial least squares method to piecewise fit the satellite orbit change.

4. The imaging satellite long-period imaging opportunity prediction method according to claim 1, characterized in that: Outputting a forecast of a long-period satellite orbit key parameter information table of the imaging satellite includes: Obtain the geographic longitude value of the descending node of the descending orbit of the same satellite as the fitting value; Determine the fitting equation; Determine the fitting model; The global regions are divided, and the geographical longitude points of the descending nodes of the daily satellite orbits are output according to the divided regions to form a satellite orbit information forecast table.

5. The imaging satellite long-period imaging opportunity prediction method according to claim 4, characterized in that: The determined fitting equation is a cubic polynomial equation; The determining of the fitting model includes: using a given empirical limit value and calculating the fitting equation by descending power.

6. The imaging satellite long-period imaging opportunity prediction method according to claim 1, characterized in that: The method calculates and predicts imaging opportunities of multiple satellites over a long period of time in the future, including: Based on the satellite orbit parameters, calculate the three-dimensional spatial position of the satellite in the target area; Based on the satellite orbit parameters, the geographic longitude of the descending node of the satellite orbit is calculated; Compare the geographical longitude of the descending node of the satellite orbit with the longitude range of the target area to determine whether there is overlap; For overlapping time points, further verification is performed to determine whether the satellite imaging constraints are met; The time points at which all imaging constraints were met were recorded as imaging opportunities.

7. The imaging satellite long-period imaging opportunity prediction method according to claim 6, characterized in that: The comparing the geographical longitude of the descending node of the satellite orbit with the longitude range of the target area comprises: Selecting the satellite orbit projection line closest to the center or edge of the target area as a reference, and calculating the difference between the satellite orbit projection line and the longitude of the boundary of the target area; back-calculating the difference to the equatorial plane; The spatial and temporal relationship between the geographic longitude point of the satellite orbit at the equator and the target area is simplified to the problem of overlap or proximity between the point and the line on the equator.

8. A device for predicting long-period imaging opportunities of an imaging satellite, characterized in that: include: Satellite orbit data monitoring module: used to continuously monitor the global navigation satellite system data of imaging satellites and obtain the instantaneous satellite orbit elements of satellites; Satellite orbit change analysis module: taking the latitude and longitude coordinates of the ground projection point of the satellite instantaneous satellite orbit within a satellite orbit maintenance period as observation sample data, analyzing and determining the change law of the satellite orbit within the satellite orbit maintenance period; Satellite orbit model fitting and prediction module: Based on the observation sample data provided by the satellite orbit change analysis module, a satellite orbit change model is constructed using a piecewise fitting method; Calculate the satellite orbit parameter fitting value and dynamic change compensation value within the satellite orbit maintenance period; The initial satellite orbit reference value is established by using the satellite orbit parameters at the epoch time, and combined with the fitted long-period satellite orbit dynamic change compensation value, the forecast of the key parameter information table of the long-period satellite orbit of the imaging satellite is output; Imaging opportunity calculation and prediction module: Based on the satellite's imaging constraints and the specific location information of the target area, the imaging opportunities of multiple satellites in the long term in the future are predicted through calculation and analysis.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the imaging satellite long-period imaging opportunity prediction method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, which are used to enable the computer to execute the imaging satellite long-period imaging opportunity prediction method as described in any one of claims 1 to 7.

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