CN115759598A - Remote sensing satellite task planning method based on multi-source cloud amount - Google Patents
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Abstract
The application relates to a remote sensing satellite task planning method based on multi-source cloud amount, which comprises the following steps: acquiring multi-source historical cloud amount data and current day cloud mask data; determining the weight of each source historical cloud amount data based on a weight index based on the multi-source historical cloud amount data and the current day cloud mask data, wherein the weight index comprises resolution, updating frequency, prediction days and minimum difference times; calculating the cloud quantity value of the minimum grid based on the weight of the weight index according to historical cloud quantity data of each source; and performing task planning on the remote sensing satellite based on the cloud volume value of the minimum grid. According to the cloud amount prediction method and device, satellite resources can be effectively utilized, and cloud amount prediction can be made more accurately.
Description
Technical Field
The invention relates to the field of remote sensing satellite task planning, in particular to a remote sensing satellite task planning method based on multi-source cloud amount.
Background
The optical remote sensing technology is a technology for detecting a target on the ground from high altitude with a distance of more than 100 kilometers to obtain related information, a used space carrier is a remote sensing satellite, a space station or a space shuttle and the like, used optical remote sensing equipment is a payload of the optical remote sensing equipment, and the optical remote sensing equipment is a space camera, a scanner or an imaging spectrometer and the like which detect by means of visible light, ultraviolet rays or infrared rays.
The optical remote sensing satellite adopts an optical remote sensing technology, and a remote sensing data shooting plan is arranged through mission planning. The task planning of the optical remote sensing satellite needs to accurately calculate a model to forecast the operation orbit of the satellite and comprehensively consider the constraint of a satellite platform and a load, the requirement characteristics of a user, meteorological conditions and other factors to arrange a satellite shooting plan.
In satellite mission planning, in order to improve the satellite observation accuracy and improve the satellite data utilization rate, a satellite mission planning scheme needs to be adjusted in time to increase the judgment on a dynamic environment, particularly the real-time viewing and prediction of cloud cover. However, the task planning of the optical remote sensing satellite generally adopts the artificial observation of the real-time meteorological nephogram to carry out the feasibility analysis of the satellite shooting cloud cover satisfaction degree, and the mode is influenced by the data source of the satellite nephogram, so that the satellite resources cannot be effectively utilized to make accurate cloud cover prediction.
Disclosure of Invention
In order to solve at least one technical problem, an embodiment of the application provides a remote sensing satellite mission planning method based on multi-source cloud amount.
The embodiment of the application provides a remote sensing satellite task planning method based on multi-source cloud amount, which comprises the following steps:
obtaining multi-source historical cloud amount data and current day cloud mask data;
determining the weight of each source historical cloud amount data based on a weight index based on the multi-source historical cloud amount data and the current-day cloud mask data, wherein the weight index comprises resolution, updating frequency, prediction days and the minimum difference number;
calculating the cloud quantity value of the minimum grid based on the weight of the weight index according to historical cloud quantity data of each source;
and performing task planning on the remote sensing satellite based on the cloud volume value of the minimum grid.
In one possible implementation manner, the determining, based on the multi-source historical cloud amount data and the current-day cloud mask data, a weight of each source historical cloud amount data based on a weight index includes:
calculating the difference value between the average cloud cover of the cloud cover data of all sources and the cloud cover data of the current day in an image time phase range, and obtaining the times of the minimum difference value in the preset time;
obtaining the value of historical cloud data of each source relative to the weight index;
based on a preset judgment scale and the value of the historical cloud amount data of each source relative to the weight index, calculating the weight of the weight index based on the historical cloud amount data of each source and the weight of the historical cloud amount data of each source relative to the weight index;
and determining the weight of each source historical cloud amount data based on the weight index based on the weight of each source historical cloud amount data and the weight result of each source historical cloud amount data relative to the weight index.
In a possible implementation manner, the calculating the cloud volume value of the minimum grid based on the weight of the weight index according to the historical cloud volume data of each source comprises:
acquiring cloud cover values of historical cloud cover data of each source corresponding to the minimum grid;
and taking the sum of the cloud volume value of the historical cloud volume data of each source corresponding to the minimum grid and the weight product of the historical cloud volume data of the corresponding source based on the weight index as the cloud volume value of the minimum grid.
In a possible implementation manner, before the calculating a difference between an average cloud amount of cloud amount data of each source in an image time phase range and cloud mask data of the current day, and obtaining a minimum number of times of the difference in a preset time, the method further includes:
and carrying out normalization processing on the multi-source cloud amount data and the current day cloud mask data.
In a possible implementation manner, after the normalizing the multi-source cloud amount data and the current-day cloud mask data, the normalizing further includes:
defining a uniform grid;
calculating the cloud cover value of each unit area grid based on the grid size of the source cloud cover data;
and calculating the area average value of each grid of the unified grids based on the cloud amount value of the unit area, namely, the area average value is the initial cloud amount value of the source cloud amount data corresponding to each unified grid.
In summary, the present application includes at least one of the following beneficial technical effects:
1. the weight-based calculation method contrasts and analyzes the cloud mask data and the multi-source cloud amount data in long-term task planning, so that the weight of the cloud amount data is dynamically corrected, a satellite task planning scheme is adjusted conveniently in time, more accurate cloud amount prediction data is obtained, and the utilization efficiency of satellite resources is improved.
2. The automatic planning of the tasks is completed based on the corrected cloud cover data weight, manual planning in the prior art is replaced, the time required for operation in task planning is saved, the labor cost is reduced, and a planning worker can conveniently adjust a satellite task planning scheme in time.
It should be understood that what is described in this summary section is not intended to limit key or critical features of the embodiments of the application, nor is it intended to limit the scope of the application. Other features of the present application will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present application will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, the same or similar reference numerals denote the same or similar elements.
Fig. 1 shows a flowchart of a remote sensing satellite mission planning method based on multi-source cloud cover according to an embodiment of the application.
Fig. 2 is a schematic diagram illustrating a calculation result of a grid of the unified grid according to the embodiment of the present application, corresponding to cloud values of any source.
Fig. 3 is a schematic diagram of a hierarchical structure model according to an embodiment of the present application.
Fig. 4 is a schematic diagram illustrating a maximum shot area region within a maximum yaw range of a planning task according to an embodiment of the present application.
Detailed Description
Fig. 1 shows a flowchart of a remote sensing satellite mission planning method based on multi-source cloud amount according to an embodiment of the present application, and referring to fig. 1, the method includes the following steps:
step 101, obtaining multi-source historical cloud amount data and current day cloud mask data.
In the embodiment of the application, the multi-source cloud amount data files provided by each large-cloud amount data platform can be automatically obtained by compiling codes, and the cloud mask data can be obtained by calculating the remote sensing image data shot by the satellite.
In the embodiment of the application, after the multi-source cloud amount data is obtained, data analysis is carried out on the multi-source cloud amount data.
Where data parsing is the process of converting one data format to another more readable or operational data format.
Illustratively, when the readable file format is a file in a NetCDF data format, in an implementation manner, one cloud data source is GFS model cloud data of the National Oceanic and Atmospheric Administration (NOAA), and the acquired data file in the grib1 format is converted into a file in a NetCDF data format; in another implementation, one cloud volume data source is ICON model cloud volume data of germany meteorological office (DWD), the acquired file format is bz2 file, the decompressed file format is grib2 file, and the grib2 format data file is converted into a NetCDF data format file.
Further, in order to continuously update the historical cloud data and further dynamically adjust the task planning, the analyzed cloud data needs to be stored and updated.
Specifically, the cloud cover data file is read, corresponding cloud cover values are stored according to the grid range of the cloud cover data of each source, the latest cloud cover data are obtained according to the updating frequency, and the cloud cover data at the same time every day are subjected to coverage updating and stored.
Furthermore, normalization processing is carried out on the obtained multi-source cloud amount data and the cloud mask data.
Specifically, data is mapped between [0,1] using a range transform method based on the following formula, and normalized values are stored corresponding to the grid ranges:
x ’ ＝(x-x min )/(x max -x min )
wherein, the x ’ Is a normalized value of the data;
the x is the actual value of the data;
said x max The maximum value of the interval in which the data is positioned;
said x min Is the minimum value of the interval in which the data is located.
For example, the range of the cloud amount value of the cloud amount data source 1 is 0 to 1, the range of the cloud amount value of the cloud amount data source 2 is 0 to 10, the range of the cloud amount value of the cloud amount data source 3 is 0 to 100, and the conversion result is shown in table 1.
| Source 1 | Source 2 | Source 3 | |
| Actual value | 0.5 | 2.3 | 72 |
| Normalized value | 0.5 | 0.23 | 0.72 |
TABLE 1
In the embodiment of the present application, the range transform method may make data with different dimensions between [0,1] to facilitate the subsequent weighted average comprehensive calculation, and other normalization methods may also be used, such as: vector normalization, linear scaling, etc.
Further, grid unification processing is carried out on the multi-source cloud amount data.
In the embodiment of the present application, the cloud data grids from different sources have different sizes, so that grid unification processing needs to be performed on the cloud data grids, which is convenient for subsequent unification of data weighting processing.
Specifically, a unified mesh is first defined.
In the embodiment of the present application, since the earth is an ellipse, the unified grid needs to be divided according to the degrees of longitude and latitude to uniformly cover the shooting range of the satellite.
In some optional embodiments, the grid is a grid graticule, and the distance between the grid graticules is smaller than the width of the intersatellite point, which is the shooting width of the satellite when the satellite is at the intersatellite point shooting, and the intersatellite point is the intersection point of the earth center and the connecting line of the satellite on the earth surface.
The actual arc length of 1 degree of latitude on any warp is 111km, the actual arc length of 1 degree of longitude on any weft is 111km × cos alpha, the alpha is the latitude of the weft, and when the width of the satellite at the sub-satellite point is less than 20km, a 0.1x0.1 grid-shaped graticule is adopted.
It should be noted that the grid selection grid is merely exemplary, and other grids may be used, such as: arc-shaped graticules, graticules and the like, and the selection of the graticules does not influence the mission planning, and the grids convenient to calculate can be freely selected.
Further, a cloud value for each of its unit area grids is calculated based on the grid size of the source cloud data.
Further, the cloud values of all unit area grids included in each grid of the unified grid are added based on the following formula and then divided by the total area to obtain an area average value of each grid of the unified grid, namely the cloud value of each grid corresponding to the source:
x”＝(x 1 *a 1 +x 2 *a 2 +...+x n *a n )/(a 1 +a 2 +...+a n )
wherein, x' is the cloud amount value of each grid corresponding to any source cloud amount data;
said x 1 ，x 2 ，...，x n Cloud cover values for unit area grids included in each grid of the unified grid;
a is a mentioned 1 ，a 2 ，...，a n The area value of the unit area mesh included for each mesh of the unified mesh.
For example, the calculation result of the cloud value of one grid of the unified grid corresponding to any source is shown in fig. 2.
Step 302, determining the weight of each source historical cloud amount data based on a weight index based on the multi-source historical cloud amount data and the current-day cloud mask data, wherein the weight index comprises resolution, updating frequency, prediction days and the minimum difference times.
In the embodiment of the application, a difference value between the average cloud amount of the cloud amount data of each source and the cloud mask data of the current day in an image time phase range is calculated, and the number of times of the minimum difference value in a preset time is obtained.
The time phase is a time period of remote sensing satellite images taken according to a time sequence, and exemplarily, one time phase range is a time when a satellite takes a group of images.
Specifically, an average cloud value of the normalized cloud amount data of each source in a time phase range is calculated, and the average cloud value is compared with the cloud mask data value to obtain a difference value, as shown in table 2.
| Cloud mask data values | Mean cloud volume | Difference value | |
| Source 1 | 0.2 | 0.18 | 0.02 |
| Source 2 | 0.18 | 0.18 | 0 |
| Source 3 | 0.15 | 0.18 | 0.0.3 |
TABLE 2
And adding the normalized cloud amount data values of all the sources and dividing the normalized cloud amount data values by the number of the sources to perform average calculation to obtain the average cloud amount.
Further, the number of times of the minimum difference value in the preset time phase range is calculated, and normalization processing is performed on the number of times of the minimum difference value.
For example, in a preferred embodiment, the number of times of the year that the difference is the smallest is selected and normalized as shown in table 3.
| Minimum number of differences | Normalized value | |
| Source 1 | 1000 | 1 |
| Source 2 | 3000 | 0 |
| Source 3 | 1500 | 0.75 |
TABLE 3
Further, the value of the historical cloud amount data of each source relative to the weight index is obtained.
For example, there are three sources of cloud data versus four weight index data: the values of resolution, update frequency, prediction days, and minimum number of differences are shown in table 4 for each source.
| Resolution (Km) | Update frequency | Predicted days | Minimum number of differences | |
| Source 1 | 13 | 3 | 10 | 1 |
| Source 2 | 22 | 6 | 10 | 1 |
| Source 3 | 25 | 12 | 1 | 0.75 |
TABLE 4
Further, based on a preset judgment scale and the value of the historical cloud amount data of each source relative to the weight index, calculating the weight of the weight index based on the historical cloud amount data of each source and the weight of the historical cloud amount data of each source relative to the weight index.
Illustratively, calculating the weight index using AHP analytic hierarchy process is based on a weight of each source historical cloud data and a weight of each source historical cloud data relative to the weight index.
In the embodiment of the application, the AHP analytic hierarchy process is a systematic method in which a complex target decision problem is used as a system, a target is decomposed into a plurality of targets or criteria, and correlation calculation is performed through qualitative indexes to be used as a basis for multi-scheme optimization decision, and a hierarchical structure model of the AHP analytic hierarchy process includes a target layer, a criteria layer and a scheme layer. The weight index data includes, but is not limited to, resolution, update frequency, number of predicted days, and minimum number of differences.
Specifically, first, a hierarchical model is built, as shown in fig. 3.
Wherein the target of the target layer is to select a cloud source; the indexes of the criterion layer comprise resolution, updating frequency, prediction days and minimum difference times; the scheme of the scheme layer comprises a source 1, a source 2 and a source 3.
Further, a judgment matrix is constructed, the values of the judgment matrix are filled according to the data in table 4 according to the judgment scale, and the specific meaning of the preset judgment scale is shown in table 5.
TABLE 5
Specifically, the judgment matrix of the criterion layer is shown in table 6, and if the matrix is denoted as Z, then:
| resolution ratio | Update frequency | Predicted days | |
| Resolution ratio | 1 | 0.333 | 0.2 |
| Update frequency | 3 | 1 | 2 |
| Predicted days | 5 | 0.5 | 1 |
TABLE 6
As shown in table 7, when the determination matrix of the recipe layer is F1, the determination matrix of the update frequency is F2, and the determination matrix of the predicted number of days is F3, then:
| resolution ratio | Source 1 | Source 2 | Source 3 |
| Source 1 | 1 | 2 | 3 |
| Source 2 | 0.5 | 1 | 0.5 |
| Source 3 | 0.33 | 2 | 1 |
TABLE 7
The judgment matrix and the matrix value calculation method of F2 and F3 are the same as FI, and are not described herein again.
The weight matrix of the criterion layer and the scheme layer obtained from the matrix Z, the matrix F1, the matrix F2, and the matrix F3 is shown in table 8.
| Criterion layer weights | Source 1 | Source 2 | Source 3 | |
| Resolution ratio | 0.11326863 | 0.54721643 | 0.18970934 | 0.26307422 |
| Update frequency | 0.50760259 | 0.54721643 | 0.18970934 | 0.26307422 |
| Predicted days | 0.37912878 | 0.22112479 | 0.31891713 | 0.45995809 |
TABLE 8
Further, the weight of each source historical cloud amount data based on the weight index is determined based on the weight of each source historical cloud amount data and the weight result of each source historical cloud amount data relative to the weight index.
Illustratively, each source cloud data versus initial weight value of the target tier, as shown in table 9.
| Source | Initial weight | Sorting |
| Source 1 | 0.42 | 1 |
| Source 2 | 0.28 | 3 |
| Source 3 | 0.30 | 2 |
TABLE 9
It should be noted that, in addition to the AHP analytic hierarchy process adopted in the embodiment of the present application to calculate the weight of the historical cloud data of each source based on the weight index, other weight calculation methods may also be adopted to calculate the weight, such as TOPSIS, etc., and the specific method and the AHP analytic hierarchy process to calculate the weight of the historical cloud data of each source based on the weight index belong to the same concept, and the specific implementation process is detailed in the embodiment of the present application and will not be described herein again.
And 103, calculating the cloud quantity value of the minimum grid based on the weight of the weight index according to historical cloud quantity data of each source.
Specifically, firstly, cloud cover values of historical cloud cover data of all sources corresponding to the minimum grids are obtained; and then taking the sum of the cloud volume value of the historical cloud volume data of each source corresponding to the minimum grid and the weight product of the historical cloud volume data of the corresponding source based on the weight index as the cloud volume value of the minimum grid.
For example, when the source cloud values of a grid are shown in table 10, the initial cloud values of the grid are: 0.3 + 0.42+0.4 + 0.28+0.2 + 0.30=0.298
| Source | Cloud amount | Initial weight |
| Source 1 | 0.3 | 0.42 |
| Source 2 | 0.4 | 0.28 |
| Source 3 | 0.2 | 0.30 |
Watch 10
And 104, performing task planning on the remote sensing satellite based on the cloud volume value of the minimum grid.
In the embodiment of the application, on one hand, the mission planning of the optical remote sensing satellite needs to forecast the running orbit of the satellite through an accurate calculation model, wherein the running orbit comprises the space position, the flight speed and the flight direction of the satellite at the instant moment; on the other hand, the satellite shooting plan is arranged by comprehensively considering the constraint of the satellite platform and the load, the characteristics of user requirements, meteorological conditions and other factors.
Specifically, the orbit visibility range calculation is first performed based on the two-line root of the satellite orbit.
In the embodiment of the application, a satellite motion track mode model is constructed based on two rows of roots of a satellite orbit, the satellite motion track mode model is used for determining the orbital roots of a space target moving around the earth at a given epoch, and the position and the speed of the target at any point on the orbit can be estimated with certain precision by using a proper prediction model to obtain a satellite orbit calculation result.
In a preferred embodiment, an orbit calculation method of the SGP4 model is utilized to calculate the coordinate and velocity of the satellite-dependent sub-satellite point in the earth-fixed system (ECEF), after the earth-fixed coordinate, the velocity is normalized to obtain the limit range (axis) of the coordinate axis of the earth-fixed system, obtain the real-time height h of the satellite, calculate the angle ha of the left and right range of the sub-satellite point of the 0 side pendulum based on the width w of the satellite, and if the maximum side pendulum angle is maxa, the calculation formula of the maximum swing angle ta is as follows:
ta＝maxa+ha
the calculation formula of the included angle theta between the geocenter and the maximum side sway position is as follows:
θ＝(h+tan(ta*π/180))/6378137
and axis and theta are subjected to quaternion conversion to calculate the offset position of the earth-fixed system, and the coordinates of the earth-fixed system are converted into longitude and latitude coordinates of WGS84 coordinates.
And performing over-orbit calculation of the mission to be planned according to the maximum sidesway and the satellite orbit calculation result, wherein the calculation can be performed by algorithms such as a propagation algorithm, a greedy algorithm or a genetic algorithm.
Further, the shooting time is divided so that the region object is included in the visible range.
In a preferred embodiment, the default step time is equal to the minimum capture unit, and the capture time is determined according to the default step time. Illustratively, the minimum shooting unit of a certain satellite is 3s, and the default step time is 3s.
Further, shooting range comparison is carried out based on automatic movement of the satellite, and a maximum shooting area region in a maximum sidesway range of the planning task is obtained.
Specifically, based on the automatic movement of the satellite, the shooting range is compared by adjusting the satellite yaw and the shooting time, so as to obtain a maximum shooting area region within the maximum yaw range of the planning task, that is, an exemplary example of the optimal shooting range of the planning task is shown in fig. 4.
And further, calculating the average value of the grid cloud amount in the optimal shooting range of the planning task based on the cloud amount value of the minimum grid.
Specifically, cloud values of grids intersected with the maximum shooting area of the planning task are obtained, and the average value of the cloud amount is obtained by adding the cloud amount value of each grid and dividing the sum by the number of the intersected grids.
And further, judging whether the average value of the grid cloud cover exceeds the preset cloud cover requirement of the task, if so, adjusting the satellite side pendulum to enable the average value of the grid cloud cover to meet the preset cloud cover requirement of the task, and otherwise, finishing the planning.
According to the embodiment of the disclosure, the following technical effects are achieved:
the cloud mask data and the multi-source cloud amount data are contrastively analyzed in long-term task planning by the weight-based calculation method, so that the cloud amount data weight is dynamically corrected, more accurate cloud amount prediction data is obtained, manual planning in the prior art is replaced by automatic task planning, the time required for operation in many task planning is saved, the labor cost is reduced, and the utilization efficiency of satellite resources is improved.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
It is to be understood that reference herein to "at least one" means one or more and "a plurality" means two or more. In the description of the embodiments of the present application, "/" indicates an alternative meaning, for example, a/B may indicate a or B; "and/or" herein is merely an association relationship describing an associated object, and means that there may be three relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, in order to facilitate clear description of technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish identical items or similar items with substantially identical functions and actions. Those skilled in the art will appreciate that the terms "first," "second," etc. do not denote any order or quantity, nor do the terms "first," "second," etc. denote any order or importance.
The above-mentioned exemplary embodiments are not intended to limit the embodiments of the present application, and any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the embodiments of the present application should be included in the protection scope of the present application.
Claims (5)
1. A remote sensing satellite mission planning method based on multi-source cloud cover is characterized by comprising the following steps:
acquiring multi-source historical cloud amount data and current day cloud mask data;
determining the weight of each source historical cloud amount data based on a weight index based on the multi-source historical cloud amount data and the current day cloud mask data, wherein the weight index comprises resolution, updating frequency, prediction days and minimum difference times;
calculating the cloud quantity value of the minimum grid based on the weight of the weight index according to historical cloud quantity data of each source;
and performing task planning on the remote sensing satellite based on the cloud volume value of the minimum grid.
2. The method of claim 1, wherein determining a weight of each source historical cloud data based on a weight index based on the multi-source historical cloud data and the current-day cloud mask data comprises:
calculating the difference value between the average cloud amount of the cloud amount data of each source and the cloud mask data of the current day in an image time phase range, and acquiring the times of minimum difference value in preset time;
obtaining the value of historical cloud data of each source relative to the weight index;
calculating the weight of the weight index based on the historical cloud amount data of each source and the weight of the historical cloud amount data of each source relative to the weight index based on a preset judgment scale and the value of the historical cloud amount data of each source relative to the weight index;
and determining the weight of the source historical cloud amount data based on the weight index based on the weight of the source historical cloud amount data and the weight result of the source historical cloud amount data relative to the weight index.
3. The method of claim 1, wherein calculating the cloud cover value of the minimum grid based on the weight of the weight index according to the historical cloud cover data of each source comprises:
acquiring cloud cover values of historical cloud cover data of each source corresponding to the minimum grid;
and taking the sum of the cloud volume value of the historical cloud volume data of each source corresponding to the minimum grid and the weight product of the historical cloud volume data of the corresponding source based on the weight index as the cloud volume value of the minimum grid.
4. The method of claim 1, wherein before calculating a difference between an average cloud amount of cloud amount data of each source in an image phase range and a current day cloud mask data to obtain a minimum difference within a predetermined time, the method further comprises:
and carrying out normalization processing on the multi-source cloud amount data and the current day cloud mask data.
5. The method of claim 4, wherein after normalizing the multi-source cloud amount data and the current day cloud mask data, further comprising:
defining a uniform grid;
calculating the cloud cover value of each unit area grid based on the grid size of the source cloud cover data;
and calculating the area average value of each grid of the unified grids based on the cloud amount value of the unit area, namely, the area average value is the initial cloud amount value of the source cloud amount data corresponding to each unified grid.
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