Longitudinal multiple determination method, device and equipment for earth coverage period calculation

Abstract

The application provides a longitudinal multiple determination method and device for earth coverage period calculation and equipment, relates to the field of space technology, and comprises the following steps: orbit recursion is carried out based on satellite parameters to form target longitude and latitude sequence data of the satellite under each regression orbit; sea and land identification processing is performed on the target longitude and latitude sequence data to obtain a target imaging arc segment timing diagram; the imaging time length of the load per orbit is taken as a constraint to determine the number of regression periods required for completing all the imageable arc segments in the target imaging arc segment timing diagram under each regression orbit, and the number of regression periods is taken as a longitudinal multiple for the calculation of the earth coverage period of the satellite. The application can adapt to the coverage period calculation requirements under the requirements of "only land imaging" or "only ocean imaging", improves the adaptability of the earth coverage period calculation, and meets more refined analysis requirements in actual work.

Description

Technical Field

[0001] This invention relates to the field of aerospace technology, and in particular to a method, apparatus and equipment for determining the longitudinal multiple for calculating the Earth coverage period. Background Technology

[0002] In the mission development phase of remote sensing satellites, indicators such as the satellite's Earth coverage period are required to evaluate its orbital performance. General-purpose orbital simulation software can be used for coverage analysis within the industry. However, due to the limitations of simulation software evaluation, a simpler evaluation method is more feasible. This method uses orbital regression parameters, payload swath width, and payload operating time per orbit as inputs to calculate and output the coverage period, which is the number of days required for the satellite to cover the entire theoretical global visible area at least once. The algorithm calculates the coverage period according to the following formula:

[0003] In the formula, the coverage period is... , It is a vertical multiple. This represents a horizontal multiple. (Symbol) This indicates rounding up to the nearest integer.

[0004] Among the parameters required for this algorithm, the vertical multiplier is... Calculate according to the following formula:

[0005] In the formula, For orbital period, The duration of load operation per rail is known.

[0006] While this algorithm is well-suited for global and regional coverage within specified dimensions, it cannot meet the more refined analytical needs of practical work. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a method, apparatus and equipment for determining the longitudinal multiple for land cover period calculation, which can adapt to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improve the adaptability of land cover period calculation, and meet the more refined analysis requirements in actual work.

[0008] In a first aspect, embodiments of the present invention provide a method for determining the longitudinal multiple for calculating the earth coverage period, including:

[0009] Obtain the satellite parameters and the imaging duration per orbit of the payload corresponding to the satellite;

[0010] Based on satellite parameters, the satellite orbit is recursively extrapolated to form a sequence of target latitude and longitude data for each return orbit;

[0011] The target latitude and longitude sequence data of the satellite under each return orbit are processed for land and sea identification to obtain the target imaging arc time sequence map, which includes the land imaging arc time sequence map or the ocean imaging arc time sequence map.

[0012] Using the imaging duration per orbit of the payload as a constraint, the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time sequence diagram is determined. The number of regression cycles is used as the longitudinal multiple, which is used to calculate the satellite's Earth coverage cycle.

[0013] In one implementation, orbital extrapolation is performed on the satellite based on satellite parameters to generate target latitude and longitude sequence data for each return orbit, including:

[0014] The satellite orbit is recursively extrapolated based on satellite parameters to form the initial latitude and longitude sequence data corresponding to the satellite;

[0015] The initial latitude and longitude sequence data is segmented to obtain the intermediate latitude and longitude sequence data of the satellite under each return orbit;

[0016] Based on the satellite's corresponding ascent and descent orbit patterns, target latitude and longitude sequence data for each return orbit of the satellite are selected from the intermediate latitude and longitude sequence data.

[0017] In one implementation, based on the satellite's corresponding ascent and descent orbit pattern, the target latitude and longitude sequence data of the satellite under each return orbit is selected from the intermediate latitude and longitude sequence data, including:

[0018] The latitude angle range is determined based on the corresponding satellite's ascent and descent orbit pattern;

[0019] Based on the latitude angle segment, target latitude and longitude sequence data of the satellite under each return orbit are selected from the intermediate latitude and longitude sequence data.

[0020] In one implementation, the target latitude and longitude sequence data of the satellite under each return orbit are processed for land and sea identification to obtain a time series map of the target imaging arc, including:

[0021] For each data point in the target latitude and longitude sequence data of the satellite under each regression orbit, identify whether the data point is located on land or in the ocean;

[0022] A land imaging arc time series map can be generated based on data points located on land, or an ocean imaging arc time series map can be generated based on data points located on the ocean. The land imaging arc time series map is used to describe the latitude and longitude sequence corresponding to the satellite passing over land in each regression orbit, and the ocean imaging arc time series map is used to describe the latitude and longitude sequence corresponding to the satellite passing over the ocean in each regression orbit.

[0023] In one implementation, using the imaging duration per orbit of the payload as a constraint, the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time series diagram is determined, including:

[0024] For each return orbit in the target imaging arc, based on the imaging duration of the payload per orbit and the imageable arc corresponding to the satellite in the return orbit in the previous return cycle, the imageable arc corresponding to the satellite in the return orbit in the current return cycle is determined from the imageable arcs not covered in the return orbit in the target imaging arc time sequence diagram.

[0025] Mark the imageable arcs corresponding to the satellite in the current regression cycle under the regression orbit as covered imageable arcs, in order to update the imageable arcs not covered under the regression orbit in the target imaging arc time series diagram;

[0026] The next regression period is taken as the new current regression period. The imaging arcs corresponding to the satellite in the current regression orbit are determined from the updated uncovered imaging arcs in the new current regression period until every imaging arc under every regression orbit in the target imaging arc time series diagram is covered, and the number of regression periods is obtained.

[0027] In one implementation, based on the payload's imaging duration per orbit and the imageable arc segments corresponding to the satellite in the previous regression cycle within that regression orbit, the imageable arc segments corresponding to the satellite in the current regression cycle are determined from the uncovered imageable arc segments in the target imaging arc segment time sequence diagram within that regression orbit, including:

[0028] Based on the imageable arc segment corresponding to the satellite in the previous regression cycle under the regression orbit, the cumulative starting point corresponding to the current regression cycle is determined from the imageable arc segments not covered under the regression orbit in the target imaging arc segment time sequence diagram.

[0029] Starting from the cumulative starting point, the cumulative duration of the imageable arc segments not covered under the regression track in the target imaging arc segment time sequence diagram is counted until the imaging duration of each track of the payload is reached, so as to obtain the cumulative termination point corresponding to the current regression cycle.

[0030] The imageable arc segment between the cumulative starting point and the cumulative ending point is determined as the imageable arc segment corresponding to the satellite in this regression orbit within the current regression cycle.

[0031] Secondly, embodiments of the present invention also provide a longitudinal multiple determination device for calculating the earth coverage period, comprising:

[0032] The data acquisition module is used to acquire the satellite parameters and the imaging duration per orbit of the payload corresponding to the satellite.

[0033] The orbit recursion module is used to recursively predict the satellite's orbit based on satellite parameters, in order to generate target latitude and longitude sequence data for each return orbit.

[0034] The land-sea identification module is used to perform land-sea identification processing on the target latitude and longitude sequence data of the satellite under each return orbit to obtain the target imaging arc time sequence map, which includes the land imaging arc time sequence map or the ocean imaging arc time sequence map.

[0035] The longitudinal multiplier determination module is used to determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time sequence diagram, with the imaging duration per orbit of the payload as a constraint. The number of regression cycles is used as the longitudinal multiplier, which is used to calculate the satellite's Earth coverage cycle.

[0036] In one implementation, the orbit recursion module is specifically used for:

[0037] The satellite orbit is recursively extrapolated based on satellite parameters to form the initial latitude and longitude sequence data corresponding to the satellite;

[0038] The initial latitude and longitude sequence data is segmented to obtain the intermediate latitude and longitude sequence data of the satellite under each return orbit;

[0039] Based on the satellite's corresponding ascent and descent orbit patterns, target latitude and longitude sequence data for each return orbit of the satellite are selected from the intermediate latitude and longitude sequence data.

[0040] Thirdly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any of the methods provided in the first aspect.

[0041] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement any of the methods provided in the first aspect.

[0042] This invention provides a method, apparatus, and device for determining the longitudinal multiple for calculating Earth coverage period. After obtaining the satellite parameters and the imaging duration per orbit of the payload, the method first performs orbital recursion on the satellite based on the satellite parameters to form target latitude and longitude sequence data for each return orbit. Then, it performs land and sea identification processing on the target latitude and longitude sequence data for each return orbit to obtain a target imaging arc time sequence map, which includes a land imaging arc time sequence map or a sea imaging arc time sequence map. Finally, using the imaging duration per orbit of the payload as a constraint, the method determines the number of return cycles required to complete all imageable arcs under each return orbit in the target imaging arc time sequence map. The number of return cycles is used as the longitudinal multiple, which is used for calculating the satellite's Earth coverage period. The above method generates target latitude and longitude sequence data for each satellite in each return orbit through orbit recursion. Then, it performs land and sea identification processing on the target latitude and longitude sequence data to generate land imaging arc time series maps or ocean imaging arc time series maps respectively. Based on this, the longitudinal multiple adapted to land or ocean is determined by using the imaging duration of the payload per orbit as a constraint. Compared with the traditional technology that calculates the longitudinal multiple with a simple formula, the embodiments of the present invention can distinguish between land and ocean, thereby adapting to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improving the adaptability to land coverage period calculation, and meeting the more refined analysis requirements in actual work.

[0043] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0044] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0046] Figure 1 A flowchart illustrating a method for determining the longitudinal multiple for calculating the earth coverage period, provided in an embodiment of the present invention;

[0047] Figure 2 An imaging arc timing diagram provided in an embodiment of the present invention;

[0048] Figure 3 An imageable arc segment time series diagram under the first regression period is provided in an embodiment of the present invention;

[0049] Figure 4 This invention provides an imageable arc segment time series diagram under a second regression period.

[0050] Figure 5 This invention provides an imageable arc segment time series diagram under a third regression period.

[0051] Figure 6 This invention provides an imageable arc segment time series diagram under the fourth regression period;

[0052] Figure 7 This is a schematic diagram of a longitudinal multiple determination device for calculating the earth coverage period provided in an embodiment of the present invention;

[0053] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Currently, traditional algorithms are well-suited for global and regional coverage within specified latitude ranges. However, for more refined requirements, specific calculations of the necessary parameters for these algorithms are required. Therefore, this invention provides a method, apparatus, and device for determining the longitudinal multiple of land cover cycle calculations. This method can adapt to the coverage cycle calculation needs under requirements such as "land imaging only" or "ocean imaging only," improving the adaptability of land cover cycle calculations and meeting the more refined analytical needs in practical work.

[0056] To facilitate understanding of this embodiment, a method for determining the longitudinal multiple for calculating the earth cover period, as disclosed in this embodiment of the invention, will first be described in detail. (See [link to relevant documentation]). Figure 1 The flowchart shown illustrates a method for determining the longitudinal multiple for calculating the earth cover period. This method mainly includes the following steps S102 to S108:

[0057] Step S102: Obtain the satellite parameters and the imaging duration per orbit of the payload corresponding to the satellite.

[0058] The satellite parameters include orbital epoch, epochal orbital parameters, orbital return days, and the current ascent / descent orbit mode (ascent or descent mode). The satellite can be a SAR (Synthetic Aperture Radar) satellite.

[0059] Step S104: Based on the satellite parameters, perform orbit recursion on the satellite to form target latitude and longitude sequence data for each return orbit.

[0060] In one example, the satellite's orbit can be recursively extrapolated based on epochal orbit parameters and the predicted duration to form the initial latitude and longitude sequence data corresponding to the satellite. This initial latitude and longitude sequence data can then be processed by segmenting it according to orbit and filtering it according to its ascent and descent modes to obtain the target latitude and longitude sequence data for each return orbit. Segmentation is used to divide the initial latitude and longitude sequence data into data subsets corresponding to each return orbit, while data filtering is used to select data subsets that satisfy the satellite's current ascent and descent orbit modes, such as selecting data subsets under ascent or descent modes.

[0061] Step S106: Perform land and sea identification processing on the target latitude and longitude sequence data of the satellite under each return orbit to obtain the target imaging arc time sequence map.

[0062] The target imaging arc time series map includes either a land imaging arc time series map or an ocean imaging arc time series map, which is composed of imageable arc segments. The land imaging arc time series map describes the latitude and longitude sequence corresponding to the satellite's passage over land in each orbit, while the ocean imaging arc time series map describes the latitude and longitude sequence corresponding to the satellite's passage over the ocean in each orbit. In one example, the target latitude and longitude sequence data for each orbit is processed in batches to identify whether each data point in the target latitude and longitude sequence data is located on land or in the ocean, thus forming either the land imaging arc time series map or the ocean imaging arc time series map.

[0063] Step S108: Using the imaging duration per orbit of the payload as a constraint, determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time sequence diagram. Use the number of regression cycles as a longitudinal multiple, which is used to calculate the satellite's Earth coverage cycle.

[0064] In one example, taking the land imaging arc time series map as an example, the imageable arcs within the first regression period are examined based on the land imaging arc time series map. Specifically, a portion of a specified cumulative duration is extracted from each row (i.e., each track) of data to serve as the imageable arc for that track under the constraint of the payload's imaging duration per track; this process is repeated until all segments within a regression period are covered, and the number of regression periods experienced at this point is taken as the longitudinal multiple. This longitudinal multiple can meet the coverage period calculation requirements under the "land imaging only" requirement. Similarly, using the same processing method, with the payload's imaging duration per track as a constraint, another longitudinal multiple can be obtained based on the ocean imaging arc time series map. This longitudinal multiple can meet the coverage period calculation requirements under the "ocean imaging only" requirement.

[0065] The longitudinal multiple determination method for calculating Earth coverage period provided in this invention generates target latitude and longitude sequence data of the satellite under each return orbit through orbit recursion. Then, the target latitude and longitude sequence data is processed for land and sea identification to generate land imaging arc time series maps or ocean imaging arc time series maps respectively. Based on this, the longitudinal multiple suitable for land or sea is determined by using the imaging duration of the payload per orbit as a constraint. Compared with the traditional technology that calculates the longitudinal multiple with a simple formula, this invention can distinguish between land and sea, thereby adapting to the coverage period calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improving the adaptability of Earth coverage period calculation and meeting the more refined analysis needs in actual work.

[0066] In one implementation, within the framework of calculating the SAR satellite ground coverage period using a simplified formula, embodiments of the present invention provide a set of longitudinal multiples. This refined design approach can identify land and sea, improving the adaptability of coverage cycle calculation and thus addressing the requirements for "land-only coverage" or "sea-only coverage." The method takes into account the satellite's initial orbital parameters, orbital epoch, orbital return days, and payload-per-orbit imaging duration constraints, calculates and outputs the longitudinal multiple as a necessary calculation parameter for the coverage cycle. This differs from the aforementioned... The calculation method can distinguish between land and ocean, and can adapt to the requirement of covering only the land portion of the globe.

[0067] For example, based on existing simplified formulas for calculating SAR satellite coverage periods, this invention provides a refined calculation method for distinguishing between land and ocean longitudinal multiples, improving the adaptability of coverage period calculations. This invention calculates the longitudinal coefficient in the following simplified formula. :

[0068] ;

[0069] In the formula, For the coverage period, For the regression period, This represents a horizontal multiple. (Symbol) This indicates rounding up to the nearest integer.

[0070] In this embodiment of the invention, considering the coverage requirements of "land imaging only" or "ocean imaging only," different results should be obtained under the same payload operating time. And there must be This improves the accuracy of SAR satellite's ground coverage period calculation while reducing the number of times SAR satellites need to be powered on.

[0071] For ease of understanding, this embodiment of the invention provides a specific implementation of a method for determining the longitudinal multiple for calculating the earth coverage period.

[0072] For the aforementioned step S102, the obtained calculation conditions include information such as orbit epoch, epoch orbit parameters, orbit return days, and payload imaging duration constraints per orbit.

[0073] Regarding the aforementioned step S104, this embodiment of the invention provides an implementation method for recursively extrapolating the satellite's orbit based on satellite parameters to form a sequence of target latitude and longitude data for each return orbit, including the following steps 1.1 to 1.3:

[0074] Step 1.1: Based on the satellite parameters, perform orbital extrapolation on the satellite to form the initial latitude and longitude sequence data corresponding to the satellite.

[0075] First, based on the epoch orbital elements, orbital recursion can be performed according to the predicted duration to generate time series data of the satellite's position and latitude argument in the inertial coordinate system (hereinafter referred to as "inertial frame"). The inertial frame is J2000; the current satellite orbit is input in the form of orbital elements; and the position sequence is represented in rectangular coordinates.

[0076] Optionally, the orbital recursion can be performed according to the standard J2 or J4 recursion method, which will not be elaborated further in this embodiment of the invention.

[0077] Then, for the aforementioned time series data, the satellite's inertial position sequence is sequentially transformed to the Earth-fixed coordinate system (hereinafter referred to as "Earth-fixed system"), and then transformed to latitude and longitude expression to form the initial latitude and longitude sequence data. The Earth-fixed system is WGS84. The coordinate transformation from the inertial system to the Earth-fixed system adopts the standard algorithm defined by the IAU1980 specification, which will not be elaborated further in this embodiment of the invention.

[0078] In practical implementation, for any fixed rectangular coordinate point Convert to latitude and longitude The steps are as follows:

[0079] (1) Constant settings:

[0080] Earth's major radius (equatorial radius): ;

[0081] Earth's short radius (polar radius): ;

[0082] First eccentricity: ;

[0083] (2) Calculation of longitude:

[0084] longitude: ;

[0085] (3) Calculation of geocentric latitude:

[0086] Geocentric latitude: ;

[0087] (4) Calculation of initial values ​​for geographic latitude during iteration:

[0088] Initial value of the radius of curvature of the zonal loop: ;

[0089] Initial height: ;

[0090] Initial value for geographical latitude: ;

[0091] (5) Iterative calculation of geographical latitude:

[0092] For the The nth iteration loop, using the nth The results of the round are calculated as follows.

[0093] ;

[0094] ;

[0095] ;

[0096] Repeat (5) until the residual is correct. This will give you the initial latitude and longitude sequence data corresponding to the satellite.

[0097] Step 1.2: The initial latitude and longitude sequence data is segmented to obtain the intermediate latitude and longitude sequence data of the satellite under each return orbit;

[0098] Step 1.3: Based on the satellite's corresponding ascent and descent orbit pattern, select the target latitude and longitude sequence data for each return orbit from the intermediate latitude and longitude sequence data. In one implementation, firstly, determine the latitude argument segment based on the satellite's corresponding ascent and descent orbit pattern; then, according to the latitude argument segment, select the target latitude and longitude sequence data for each return orbit from the intermediate latitude and longitude sequence data.

[0099] For example, for latitude argument and its corresponding initial latitude and longitude sequence data, the data is segmented by track, and a subset of data is extracted according to the specified latitude argument segment. For instance, for a regression track with 179 regression cycles, it is divided into 179 segments and stored separately, and a subset that matches the latitude argument segment is found in each segment, which is the target latitude and longitude sequence data.

[0100] In one specific implementation, the `find()` function in MATLAB is used to segment the track and extract subsets according to latitude argument segments. Specifically, it finds subsets that satisfy a specified latitude argument. The data sequence number of the interval. The pseudocode is as follows:

[0101] ;

[0102] Taking the regression orbit with 179 regression cycles as an example, It is a cell array of size 179, specifically, denoted as It stores the data sequence numbers of 179 tracks; the latitude argument corresponding to the data sequence numbers in all 179 cell arrays. All satisfy the latitudinal argument range .

[0103] Furthermore, for a specified latitude argument range The following principles are followed: Under the coverage period calculation framework applicable to the embodiments of this invention, the calculation of the longitudinal multiple only considers either ascending or descending orbit. This invention can select either ascending or descending orbit mode. In ascending orbit mode, the latitude argument segment is taken as... In the orbit reduction mode, the latitude argument segment is taken as follows: .

[0104] Regarding the aforementioned step S106, this embodiment of the invention provides a specific implementation method for performing land-sea identification processing on the target latitude and longitude sequence data of the satellite under each return orbit to obtain a target imaging arc time series map, including: for each data point in the target latitude and longitude sequence data of the satellite under each return orbit, identifying whether the data point is located on land or in the ocean; generating a land imaging arc time series map based on the data points located on land, or generating an ocean imaging arc time series map based on the data points located in the ocean.

[0105] For each regression orbit, the target latitude and longitude sequence data is processed in batches to identify whether the latitude and longitude are located on land or sea. The same operation is performed on the target latitude and longitude sequence data for each regression orbit to obtain a time-series map of the target imaging arc segment.

[0106] For example, for each of the 179 tracks mentioned above, land-sea identification is performed sequentially. Data points at each moment in the latitude and longitude sequence within the track are batch-processed and judged to identify whether that latitude and longitude is located on land or sea. Taking the coverage requirement of "land-only imaging" as an example, the land and sea identification results are obtained. The time periods corresponding to the subsets of data identified as land are organized, and all 179 tracks are processed in the same way to form the following... Figure 2 The diagram shows a time series of imaging arcs. The black lines represent the latitude and longitude of the land to be visited, i.e., the uncovered imageable arcs.

[0107] Optionally, the ocean / land coordinate database is provided by Matlab's coast file. Based on this, the ocean-land identification is a simulation method developed by secondary development of the coast database using "Matrix Lab" (MATLAB). It utilizes Matlab's powerful matrix processing capabilities to generate a geographic coordinate finite element mesh and combines it with satellite latitude and longitude sequences for batch processing logic judgment.

[0108] Regarding the aforementioned step S108, this embodiment of the invention provides a specific implementation method for determining the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time series diagram, using the imaging time per orbit of the payload as a constraint, including the following steps 2.1 to 2.3:

[0109] Step 2.1: For each return orbit in the target imaging arc, based on the payload's imaging duration per orbit and the imageable arcs corresponding to the satellite in that return orbit during the previous return cycle, determine the imageable arcs corresponding to the satellite in that return orbit during the current return cycle from the uncovered imageable arcs in the target imaging arc time series diagram. In practical applications, when determining the imageable arcs covered in the current return cycle, the imageable arcs under each return orbit are processed in parallel; that is, within each return cycle, the imageable arcs covered by each return orbit are determined.

[0110] In specific implementation, the following (a) to (c) are included:

[0111] (a) Based on the imageable arc segment corresponding to the satellite in the previous regression cycle under the regression orbit, determine the cumulative starting point corresponding to the current regression cycle from the imageable arc segments not covered under the regression orbit in the target imaging arc segment time sequence diagram.

[0112] In one example, for any imageable arc segment under a regression orbit, if the current regression period is the first regression period, then the starting point of the first imageable arc segment under that regression orbit is directly used as the cumulative starting point corresponding to the first regression period.

[0113] In another example, for any imageable arc segment under a regression orbit, if the current regression period is a subsequent regression period, the next data point corresponding to the end point of the imageable arc segment corresponding to the satellite under that regression orbit in the previous regression period is taken as the cumulative starting point for the current regression period.

[0114] (b) Starting from the cumulative starting point, the cumulative duration of the imageable arc segments not covered under the regression track in the target imaging arc segment time sequence diagram is counted until the imaging duration per track of the payload is reached, so as to obtain the cumulative termination point corresponding to the current regression cycle.

[0115] In one example, the horizontal axis of the target imaging arc time sequence diagram is the latitude angle, and the vertical axis is the track number. The latitude angle is related to time, so the cumulative duration can be calculated based on the latitude angle. When the duration reaches the pre-input payload imaging duration per track, the cumulative duration calculation can be stopped, and the data point at which the cumulative duration calculation stops can be taken as the cumulative termination point.

[0116] (c) The imageable arc segment between the cumulative start point and the cumulative end point is determined as the imageable arc segment corresponding to the satellite in the current regression orbit within the current regression cycle.

[0117] Step 2.2: Mark the imageable arc segments corresponding to the satellite in the current regression cycle under the regression orbit as covered imageable arc segments. This is used to update the uncovered imageable arc segments under the regression orbit in the target imaging arc segment time series diagram. By marking the imageable arc segments corresponding to the satellite in the current regression cycle under the regression orbit as covered imageable arc segments, the same imageable arc segment can be avoided from being counted repeatedly.

[0118] Step 2.3: Using the next regression period as the new current regression period, continue to determine the corresponding imageable arcs of the satellite in the current regression orbit within the updated, uncovered imageable arcs, until every imageable arc under each regression orbit in the target imaging arc time sequence diagram is covered, thus obtaining the number of regression periods. In specific implementation, repeat steps 2.1 to 2.2. Iteration stops when every imageable arc under each regression orbit in the target imaging arc time sequence diagram is covered, and the number of regression periods elapsed at this point is used as the vertical multiple.

[0119] To facilitate understanding, this embodiment of the invention provides an application example for determining the longitudinal multiple. First, based on the aforementioned imageable arc time series diagram, the imageable arcs within the first regression period are examined. Specifically, a portion of a specified cumulative duration is extracted from each row (i.e., each track) of data to serve as a constraint on the imaging duration of each track on the payload. (Taking 8 minutes as an example) This refers to the imageable time period under the current orbit. For example, in the upswing mode, see... Figure 3 The image shows the time series diagram of imageable arcs in the first regression period. Black lines represent the latitude and longitude of land to be visited, i.e., uncovered imageable arcs; dark gray lines represent the latitude and longitude visited in this round, i.e., covered imageable arcs in the current regression period; light gray lines represent visited latitude and longitude, i.e., covered imageable arcs. Imageable segments within this regression period are marked in dark gray, indicating that the imageable segment can be covered within this regression period.

[0120] For subsequent regression periods, the same process is applied. Segments already identified as imageable in historical regression periods are not counted again. This process is repeated until all segments within a regression period are covered (marked in light color). See also Figure 4 The imageable arc time series plot shown in the second regression period, Figure 5 The imageable arc time series plot shown in the third regression period, Figure 6 The imageable arc time series plot is shown in the fourth regression period. At this point, the number of regression periods experienced is the longitudinal multiple, which is 4.

[0121] In this embodiment of the invention, cumulative duration statistics can be performed on each imageable arc segment in each track, and the imageable arc segments of the current regression cycle and the current track can be counted when the imaging duration constraint time for each track expires. Arc segments already identified as imageable in historical regression cycles are not counted repeatedly. This differs from the simplified algorithm's approach of dividing the working duration constraint of each track by an entire continuous arc segment, thus meeting the requirements of refined design.

[0122] In summary, the embodiments of this invention include an input calculation condition stage, an orbit recursion stage, a coordinate transformation stage, an orbit data processing stage, a land-sea identification stage, and a longitudinal multiple calculation stage, thereby calculating and outputting the longitudinal multiple in the simplified coverage period algorithm. Through simulation calculation of the longitudinal coefficient, the embodiments of this invention enable the simplified coverage period algorithm to distinguish between land and sea, adapting to the coverage period calculation requirements under "land-only imaging" or "sea-only imaging" conditions. The embodiments of this invention improve the adaptability of the simplified algorithm, meeting the needs of more refined analysis in practical work.

[0123] Based on the foregoing embodiments, this invention provides a device for determining the longitudinal multiple for calculating the earth coverage period, see [link to previous embodiment]. Figure 7 The diagram shows a structural schematic of a device for determining the longitudinal multiple of earth cover period calculation. The device mainly includes the following parts:

[0124] Data acquisition module 902 is used to acquire the satellite parameters and the imaging duration per orbit of the payload corresponding to the satellite;

[0125] The orbit recursion module 904 is used to recursively predict the orbit of the satellite based on the satellite parameters to form the target latitude and longitude sequence data of the satellite under each return orbit;

[0126] The land-sea identification module 906 is used to perform land-sea identification processing on the target latitude and longitude sequence data of the satellite under each return orbit to obtain the target imaging arc time sequence map, which includes the land imaging arc time sequence map or the ocean imaging arc time sequence map.

[0127] The longitudinal multiple determination module 908 is used to determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time sequence diagram, using the imaging duration of each orbit of the payload as a constraint. The number of regression cycles is used as the longitudinal multiple, which is used to calculate the satellite's Earth coverage cycle.

[0128] The longitudinal multiple determination device for calculating Earth coverage cycle provided in this invention generates target latitude and longitude sequence data of the satellite under each return orbit through orbit recursion. Then, it performs land and sea identification processing on the target latitude and longitude sequence data to generate land imaging arc time series maps or ocean imaging arc time series maps respectively. Based on this, the longitudinal multiple suitable for land or ocean is determined by using the imaging duration of the payload per orbit as a constraint. Compared with the traditional technology that calculates the longitudinal multiple with a simple formula, this invention can distinguish between land and ocean, thereby adapting to the coverage cycle calculation requirements under the requirements of "land imaging only" or "ocean imaging only", improving the adaptability of Earth coverage cycle calculation and meeting the more refined analysis needs in actual work.

[0129] In one implementation, the orbit recursion module 904 is specifically used for:

[0130] The satellite orbit is recursively extrapolated based on satellite parameters to form the initial latitude and longitude sequence data corresponding to the satellite;

[0131] The initial latitude and longitude sequence data is segmented to obtain the intermediate latitude and longitude sequence data of the satellite under each return orbit;

[0132] Based on the satellite's corresponding ascent and descent orbit patterns, target latitude and longitude sequence data for each return orbit of the satellite are selected from the intermediate latitude and longitude sequence data.

[0133] In one implementation, the orbit recursion module 904 is specifically used for:

[0134] The latitude angle range is determined based on the corresponding satellite's ascent and descent orbit pattern;

[0135] Based on the latitude angle segment, target latitude and longitude sequence data of the satellite under each return orbit are selected from the intermediate latitude and longitude sequence data.

[0136] In one implementation, the land-sea identification module 906 is specifically used for:

[0137] For each data point in the target latitude and longitude sequence data of the satellite under each regression orbit, identify whether the data point is located on land or in the ocean;

[0138] A land imaging arc time series map can be generated based on data points located on land, or an ocean imaging arc time series map can be generated based on data points located on the ocean. The land imaging arc time series map is used to describe the latitude and longitude sequence corresponding to the satellite passing over land in each regression orbit, and the ocean imaging arc time series map is used to describe the latitude and longitude sequence corresponding to the satellite passing over the ocean in each regression orbit.

[0139] In one implementation, the longitudinal multiple determination module 908 is specifically used for:

[0140] For each return orbit in the target imaging arc, based on the imaging duration of the payload per orbit and the imageable arc corresponding to the satellite in the return orbit in the previous return cycle, the imageable arc corresponding to the satellite in the return orbit in the current return cycle is determined from the imageable arcs not covered in the return orbit in the target imaging arc time sequence diagram.

[0141] Mark the imageable arcs corresponding to the satellite in the current regression cycle under the regression orbit as covered imageable arcs, in order to update the imageable arcs not covered under the regression orbit in the target imaging arc time series diagram;

[0142] The next regression period is taken as the new current regression period. The imaging arcs corresponding to the satellite in the current regression orbit are determined from the updated uncovered imaging arcs in the new current regression period until every imaging arc under every regression orbit in the target imaging arc time series diagram is covered, and the number of regression periods is obtained.

[0143] In one implementation, the longitudinal multiple determination module 908 is specifically used for:

[0144] Based on the imageable arc segment corresponding to the satellite in the previous regression cycle under the regression orbit, the cumulative starting point corresponding to the current regression cycle is determined from the imageable arc segments not covered under the regression orbit in the target imaging arc segment time sequence diagram.

[0145] Starting from the cumulative starting point, the cumulative duration of the imageable arc segments not covered under the regression track in the target imaging arc segment time sequence diagram is counted until the imaging duration of each track of the payload is reached, so as to obtain the cumulative termination point corresponding to the current regression cycle.

[0146] The imageable arc segment between the cumulative starting point and the cumulative ending point is determined as the imageable arc segment corresponding to the satellite in this regression orbit within the current regression cycle.

[0147] The device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the device embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0148] This invention provides an electronic device, specifically, the electronic device includes a processor and a storage device; the storage device stores a computer program, and the computer program, when run by the processor, executes the method described in any of the above embodiments.

[0149] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. The electronic device 100 includes: a processor 10, a memory 11, a bus 12 and a communication interface 13. The processor 10, the communication interface 13 and the memory 11 are connected through the bus 12. The processor 10 is used to execute executable modules, such as computer programs, stored in the memory 11.

[0150] The memory 11 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 13 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0151] Bus 12 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0152] The memory 11 is used to store programs. After receiving an execution instruction, the processor 10 executes the programs. The method executed by the device for defining the flow process disclosed in any of the foregoing embodiments of the present invention can be applied to the processor 10 or implemented by the processor 10.

[0153] Processor 10 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 10 or by instructions in software form. Processor 10 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 11. The processor 10 reads the information in memory 11 and, in conjunction with its hardware, completes the steps of the above method.

[0154] The computer program product of the readable storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the foregoing method embodiments. For specific implementation, please refer to the foregoing method embodiments, which will not be repeated here.

[0155] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0156] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for determining the longitudinal multiple for calculating the earth cover cycle, characterized in that, include: Obtain the satellite parameters and the imaging duration per orbit of the payload corresponding to the satellite; Based on the satellite parameters, orbit recursion is performed to form the target latitude and longitude sequence data of the satellite under each return orbit; The target latitude and longitude sequence data of the satellite under each of the said return orbits are processed for land and sea identification to obtain a target imaging arc time sequence map, which includes a land imaging arc time sequence map or an ocean imaging arc time sequence map. Using the imaging duration per orbit of the payload as a constraint, the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time sequence diagram is determined. The number of regression cycles is used as a longitudinal multiple, which is used to calculate the satellite's Earth coverage cycle. The target latitude and longitude sequence data of the satellite under each of the said return orbits is processed for land-sea identification to obtain a target imaging arc time series map, including: for each data point in the target latitude and longitude sequence data of the satellite under each of the said return orbits, identifying whether the data point is located on land or in the ocean; generating a land imaging arc time series map based on the data points located on land, or generating an ocean imaging arc time series map based on the data points located in the ocean; wherein, the land imaging arc time series map is used to describe the latitude and longitude sequence corresponding to when the satellite passes over land under each of the said return orbits, and the ocean imaging arc time series map is used to describe the latitude and longitude sequence corresponding to when the satellite passes over the ocean under each of the said return orbits.

2. The method for determining the longitudinal multiple for calculating the earth coverage period according to claim 1, characterized in that, Based on the satellite parameters, orbit recursion is performed to generate target latitude and longitude sequence data for each return orbit of the satellite, including: Based on the satellite parameters, the orbit of the satellite is recursively extrapolated to form the initial latitude and longitude sequence data corresponding to the satellite; The initial latitude and longitude sequence data is segmented to obtain the intermediate latitude and longitude sequence data of the satellite under each return orbit; Based on the satellite's corresponding ascent and descent orbit mode, the target latitude and longitude sequence data of the satellite under each return orbit is selected from the intermediate latitude and longitude sequence data.

3. The method for determining the longitudinal multiple for calculating the earth coverage period according to claim 2, characterized in that, Based on the satellite's corresponding ascent and descent orbit pattern, the target latitude and longitude sequence data of the satellite under each return orbit is selected from the intermediate latitude and longitude sequence data, including: The latitude angle segment is determined based on the satellite's corresponding ascent and descent orbit pattern; According to the latitude angle segment, the target latitude and longitude sequence data of the satellite under each return orbit is selected from the intermediate latitude and longitude sequence data.

4. The method for determining the longitudinal multiple for calculating the earth coverage period according to claim 1, characterized in that, Using the imaging duration per orbit of the payload as a constraint, determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time series diagram, including: For each of the return orbits in the target imaging arc, based on the imaging duration per orbit of the payload and the imageable arcs corresponding to the satellite in the return orbit in the previous return cycle, the imageable arcs corresponding to the satellite in the return orbit in the current return cycle are determined from the imageable arcs not covered in the return orbit in the target imaging arc time sequence diagram. The imageable arc segments corresponding to the satellite in the current regression cycle under the regression orbit are marked as covered imageable arc segments, in order to update the imageable arc segments that are not covered under the regression orbit in the target imaging arc segment time sequence diagram. The next regression period is taken as the new current regression period. From the updated uncovered imageable arcs, the imageable arcs corresponding to the satellite in the current regression period are determined until each imageable arc in each regression orbit in the target imaging arc time sequence diagram is covered, thus obtaining the number of regression periods.

5. The method for determining the longitudinal multiple for calculating the earth coverage period according to claim 4, characterized in that, Based on the imaging duration per orbit of the payload and the imageable arc segment corresponding to the satellite in the previous regression cycle under that regression orbit, the imageable arc segment corresponding to the satellite in the current regression cycle is determined from the imageable arc segments not covered under that regression orbit in the target imaging arc segment time sequence diagram, including: Based on the imageable arc segment corresponding to the satellite in the previous regression cycle under the regression orbit, the cumulative starting point corresponding to the current regression cycle is determined from the imageable arc segments not covered under the regression orbit in the target imaging arc segment time sequence diagram; Starting from the cumulative starting point, the cumulative duration of the imageable arc segments not covered under the regression track in the target imaging arc segment time sequence diagram is counted until the imaging duration per track of the payload is reached, so as to obtain the cumulative termination point corresponding to the current regression cycle. The imageable arc segment between the cumulative starting point and the cumulative ending point is determined as the imageable arc segment corresponding to the satellite in the current regression orbit within the current regression cycle.

6. A device for determining the longitudinal multiple for calculating the earth cover period, characterized in that, include: The data acquisition module is used to acquire the satellite parameters and the imaging duration per orbit of the payload corresponding to the satellite. The orbit recursion module is used to perform orbit recursion on the satellite based on the satellite parameters to form the target latitude and longitude sequence data of the satellite under each return orbit; The land-sea identification module is used to perform land-sea identification processing on the target latitude and longitude sequence data of the satellite under each of the return orbits to obtain a target imaging arc time sequence map, which includes a land imaging arc time sequence map or an ocean imaging arc time sequence map. The longitudinal multiple determination module is used to determine the number of regression cycles required to complete all imageable arcs under each regression orbit in the target imaging arc time sequence diagram, using the imaging duration per orbit of the payload as a constraint, and to use the number of regression cycles as the longitudinal multiple, which is used to calculate the satellite's Earth coverage cycle. The land-sea identification module is specifically used to: identify whether each data point in the target latitude and longitude sequence data of the satellite in each of the return orbits is located on land or in the ocean; generate a land imaging arc time series map based on the data points located on land, or generate an ocean imaging arc time series map based on the data points located in the ocean; wherein, the land imaging arc time series map is used to describe the latitude and longitude sequence corresponding to the satellite passing over land in each of the return orbits, and the ocean imaging arc time series map is used to describe the latitude and longitude sequence corresponding to the satellite passing over the ocean in each of the return orbits.

7. The device for determining the longitudinal multiple for calculating the earth coverage period according to claim 6, characterized in that, The orbit recursion module is specifically used for: Based on the satellite parameters, the orbit of the satellite is recursively extrapolated to form the initial latitude and longitude sequence data corresponding to the satellite; The initial latitude and longitude sequence data is segmented to obtain the intermediate latitude and longitude sequence data of the satellite under each return orbit; Based on the satellite's corresponding ascent and descent orbit mode, the target latitude and longitude sequence data of the satellite under each return orbit is selected from the intermediate latitude and longitude sequence data.

8. An electronic device, characterized in that, The method includes a processor and a memory, the memory storing computer-executable instructions executable by the processor, the processor executing the computer-executable instructions to implement the method of any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when invoked and executed by a processor, cause the processor to perform the method described in any one of claims 1 to 5.

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