CN116205428A - Intelligent planning method and device for global multi-region satellite imaging task - Google Patents
Intelligent planning method and device for global multi-region satellite imaging task Download PDFInfo
- Publication number
- CN116205428A CN116205428A CN202211430903.4A CN202211430903A CN116205428A CN 116205428 A CN116205428 A CN 116205428A CN 202211430903 A CN202211430903 A CN 202211430903A CN 116205428 A CN116205428 A CN 116205428A
- Authority
- CN
- China
- Prior art keywords
- satellite
- imaging
- cloud
- data
- global
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
- G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/587—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
- G06T7/246—Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
- G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
Abstract
The embodiment of the invention discloses an intelligent planning method and device for a global multi-region satellite imaging task. The method comprises the following steps: establishing a satellite parameter library and initializing an observation period; registering global cloud cover data coordinates and counting multi-region cloud cover data; calculating to obtain satellite lower point tracks, and preprocessing the satellite lower point tracks to obtain preprocessed satellite lower point track data; calculating to obtain an effective imaging arc section of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data; based on satellite imaging load use constraint in the satellite parameter library, setting a cloud amount threshold value in a satellite breadth range, and screening and optimizing the effective imaging arc segments of the satellite to obtain optimal imaging arc segments; and generating a global multi-region imaging scheme of the satellite based on the optimal imaging arc section. The embodiment of the invention can reasonably arrange satellite observation resources to effectively observe the region with lower global cloud quantity value.
Description
Technical Field
The invention relates to the technical field of aviation, in particular to an intelligent planning method and device for a global multi-region satellite imaging task.
Background
With the increase of the terrestrial observation satellites in China, the satellite data acquisition capability is greatly improved. Because the optical satellite is easily affected by weather in the data acquisition process, the satellite image product quality is poor due to the fact that the cloud coverage is large. At present, the cloud detection load is not carried on the land observation satellite in China, and the value of the image can be analyzed only after the image data is returned in the current satellite operation mode, so that the cloud quantity distribution of the observation period is combined in the satellite task planning process to avoid the waste of the satellite observation resources.
At present, satellite intelligent task planning is mostly based on single-area grid division and regional observation, and area division optimal solutions are obtained through multiple iterations by utilizing heuristics so as to reduce resource waste, or satellite observation plans are formulated based on single targets of single satellites, so that global multiple observation tasks are comprehensively considered less. In addition, the influence of weather factors on the availability of the acquisition of the satellite data of the land observation is not considered in the above researches, and the related researches are only carried out in aspects of coverage efficiency, demand observation timeliness and the like. With the increase of the number of optical satellites with the same resolution in China, the effective data acquisition capacity in the global scope is greatly improved, but due to the limitation of satellite observation capacity and weather factors, how to realize intelligent task planning of satellites in a plurality of areas in the global scope becomes one of the factors mainly considered in reasonable utilization of satellite resources.
Disclosure of Invention
The invention solves the technical problems that: the intelligent planning method and device for the global multi-region satellite imaging task are provided for overcoming the defects of the prior art.
The technical scheme of the invention is as follows:
in a first aspect, an embodiment of the present invention provides a global multi-region satellite imaging mission intelligent planning method, where the method includes:
establishing a satellite parameter library and initializing an observation period;
registering global cloud cover data coordinates and counting multi-region cloud cover data;
calculating to obtain satellite lower point tracks, and preprocessing the satellite lower point tracks to obtain preprocessed satellite lower point track data;
calculating to obtain an effective imaging arc section of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data;
based on satellite imaging load use constraint in the satellite parameter library, setting a cloud amount threshold value in a satellite breadth range, and screening and optimizing the effective imaging arc segments of the satellite to obtain optimal imaging arc segments;
and generating a global multi-region imaging scheme of the satellite based on the optimal imaging arc section.
Optionally, the establishing a satellite parameter library and initializing an observation period include:
Establishing the satellite parameter library according to the satellite parameters of each satellite and the use constraint of the satellite imaging load;
and initializing an observation period according to the demand observation start time and the demand observation end time.
Optionally, the registering global cloud cover prediction data coordinates and the counting the multi-region cloud cover data includes:
establishing an image coordinate system according to cloud cover prediction data;
according to the corresponding relation between the image coordinates indicated by the cloud amount prediction data image coordinate system and the longitude and latitude coordinates and the pixel resolution, carrying out coordinate conversion on the cloud amount prediction data and establishing a longitude and latitude coordinate system to obtain global cloud amount data with longitude and latitude registration;
and cutting out regional cloud quantity data through the global regional vector file and the registered global cloud quantity data, and obtaining the multi-regional cloud quantity statistic value.
Optionally, the calculating obtains a satellite point track under the satellite, and the preprocessing is performed on the satellite point track under the satellite to obtain preprocessed satellite point track data, including:
acquiring two rows of roots of a satellite according to the satellite number, and preprocessing the two rows of roots of the satellite;
determining satellite point tracks of all satellites based on the preprocessed two satellites;
And performing down-orbit imaging satellite lower point screening on the satellite lower point track to obtain the preprocessed satellite lower point track data.
Optionally, the calculating to obtain the effective imaging arc segment of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data includes:
acquiring the understar points contained in the regional vector file meeting the cloud cover requirement after the global regional data is primarily screened;
taking a satellite transit area satellite under-satellite point initial point as a starting point, taking an end point as a site, calculating the longitude and latitude of the pixel point by a path through a preset algorithm, and calculating the longitude and latitude of all the pixel points in the satellite load imaging breadth;
and counting cloud quantity values corresponding to longitude and latitude positions in satellite load imaging breadth in each global area, and screening the satellite transit arcs by using a cloud quantity threshold value in the set satellite load imaging breadth to obtain the effective satellite transit arcs.
Optionally, the step of setting a cloud amount threshold in a satellite load imaging breadth based on a satellite imaging load usage constraint in the satellite parameter library, and screening and optimizing the effective imaging arc segments of the satellite to obtain an optimal imaging arc segment includes:
sequencing all the effective transit arcs by starting time of the effective transit arcs of the satellite;
According to the sequencing result, satellite imaging load use constraint in the satellite parameter library and cloud amount statistical values in the satellite load imaging breadth, shortening, merging and deleting effective imaging arcs, and counting the total number of imaging arcs and total imaging duration which are screened and optimized from the sequenced satellite effective transit arcs;
and resetting the cloud amount threshold value according to the total number and the total duration of the screened and optimized imaging arcs to screen and optimize again so as to meet the satellite imaging load use constraint in satellite parameters and determine the optimal imaging arcs.
In a second aspect, an embodiment of the present invention provides a global multi-region satellite imaging mission intelligent planning apparatus, where the apparatus includes:
the parameter library establishing module is used for establishing a satellite parameter library and initializing an observation period;
the cloud amount data statistics module is used for registering global cloud amount data coordinates and counting multi-region cloud amount data;
the track data acquisition module is used for calculating to obtain satellite lower point tracks, and preprocessing the satellite lower point tracks to obtain preprocessed satellite lower point track data;
the effective imaging arc segment calculation module is used for calculating and obtaining an effective imaging arc segment of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data;
The optimal imaging arc segment acquisition module is used for screening and optimizing the effective imaging arc segments of the satellite based on satellite imaging load use constraint and cloud amount statistic value in satellite load imaging breadth in the satellite parameter library to obtain optimal imaging arc segments;
and the imaging scheme generating module is used for generating a satellite global multi-region imaging scheme based on the optimal imaging arc section.
Optionally, the parameter library building module includes:
the satellite parameter library establishing unit is used for establishing the satellite parameter library according to the satellite parameters of each satellite and the use constraint of the satellite imaging load;
and the observation period initializing unit is used for initializing the observation period according to the requirement observation starting time and the requirement observation ending time.
Optionally, the cloud amount data statistics module includes:
the coordinate system establishing unit is used for establishing an image coordinate system according to cloud amount prediction data;
the coordinate registration unit is used for carrying out coordinate conversion on the cloud amount prediction data and establishing a longitude and latitude coordinate system according to the corresponding relation between the image coordinates indicated by the cloud amount prediction data image coordinate system and the longitude and latitude coordinates and the pixel resolution, so as to obtain global cloud amount data with longitude and latitude registration;
And the cloud quantity data acquisition unit is used for cutting regional cloud quantity data through the regional vector file and the registered global cloud quantity data to acquire the multi-region cloud quantity statistic value.
Optionally, the track data acquisition module includes:
the preprocessing unit is used for acquiring two-line roots of the satellite according to the satellite number and preprocessing the two-line roots of the satellite;
the satellite point track determining unit is used for determining satellite point tracks of all satellites based on the preprocessed two rows of satellites;
and the track data acquisition unit is used for carrying out down-track imaging satellite lower point screening on the satellite lower point track to obtain the preprocessed satellite lower point track data.
Optionally, the effective imaging arc segment calculation module includes:
the under-satellite point acquisition unit is used for acquiring under-satellite points contained in the area vector file meeting the cloud cover requirement after the global area data is primarily screened;
the longitude and latitude calculating unit is used for calculating the longitude and latitude of the pixel point by a preset algorithm by taking the initial point of the satellite point in the satellite transit area as a starting point and the end point as a site, and calculating the longitude and latitude of all the pixel points in the satellite load imaging breadth;
the satellite transit arc section acquisition unit is used for counting cloud quantity values corresponding to longitude and latitude positions in satellite load imaging breadth in each global area, and satellite transit arc section screening is carried out according to the set cloud quantity threshold value in the satellite load imaging breadth to obtain an effective imaging arc section.
Optionally, the optimal imaging arc segment acquisition module includes:
the arc segment sequencing unit is used for sequencing all the effective transit arc segments at the starting moment of the effective transit arc segments of the satellite;
the arc segment number screening and optimizing unit is used for shortening, merging and deleting effective imaging arc segments according to the sequencing result, satellite imaging load use constraint in the satellite parameter library and cloud amount statistic values in satellite load imaging breadth, and counting the total number of imaging arc segments and imaging total duration of screening and optimizing from the sequenced satellite effective transit arc segments;
and the imaging arc section determining unit is used for resetting the cloud amount threshold value to screen and optimize again according to the total number and total duration of the screened and optimized imaging arc sections and the satellite imaging load using constraint in satellite parameters, and determining the optimal imaging arc section.
Compared with the prior art, the invention has the advantages that: the embodiment of the invention establishes a satellite parameter library with satellite orbit information and imaging load use constraint information; the global cloud amount prediction data distribution map with the geographic position information is obtained by preprocessing the global cloud amount prediction data product. Pre-screening is carried out through preliminary calculation of regional cloud cover, satellite offline in the regional range and cloud cover values in the corresponding breadth range are calculated, and effective imaging arc segments of satellites are determined; and reasonably setting cloud quantity threshold information by combining the satellite imaging load use constraint condition, shortening, merging and deleting the satellite imaging arc segments, and finally generating an optimal satellite global multi-region imaging scheme so as to ensure effective observation of a region with a low global cloud quantity value on the premise of meeting the satellite imaging load use constraint, thereby realizing reasonable distribution of satellite observation resources.
Drawings
FIG. 1 is a flow chart of steps of a method for intelligent planning of global multi-region satellite imaging tasks according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a satellite basic parameter library and an imaging load usage constraint library according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a transformation relationship between cloud cover data image coordinates and longitude and latitude coordinates according to an embodiment of the present invention
FIG. 4 is a schematic diagram of a cloud computing prediction TIFF format image with coordinate transformation and spatial reference writing according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a cloud computing prediction TIFF format image related parameter through coordinate transformation and writing spatial references according to an embodiment of the present invention;
fig. 6 (a) is a schematic diagram of a regional cloud computing theory according to an embodiment of the present invention;
FIG. 6 (b) is a schematic diagram of an example of a clipped global area cloud cover according to an embodiment of the present invention;
fig. 7 is a schematic diagram of an example of a preprocessed satellite orbit reduction point track based on an SGP4 algorithm model according to an embodiment of the present invention;
FIG. 8 is a diagram of satellite passing points and attribute information of each point in the area according to the present invention;
FIG. 9 is a schematic diagram of the invention for calculating cloud cover effectiveness over a wide range of regional satellite ranges;
FIG. 10 is a schematic diagram of a process for calculating longitude and latitude of each pixel within the range of satellite breadth in the area of the present invention;
FIG. 11 is a schematic view of an imaging swath and observation area of a satellite of the present invention;
FIG. 12 (a) is a schematic illustration of an effective imaging session satisfying the satellite monorail maximum imaging time constraint of the present invention;
FIG. 12 (b) is a schematic illustration of effective imaging period reduction without meeting the satellite monorail maximum imaging time constraints of the present invention;
FIG. 12 (c) is a schematic illustration of effective imaging periods of the present invention meeting imaging interval constraints;
FIG. 12 (d) is a schematic diagram of the present invention for efficient imaging period combining without meeting satellite imaging interval constraints, and for meeting satellite monorail longest imaging time constraints after combining;
FIG. 12 (e) is a schematic diagram of the present invention for imaging period combining without satisfying the imaging interval constraint, but for effective imaging period reduction without satisfying the satellite monorail longest imaging time constraint after combining;
fig. 13 is a schematic structural diagram of an intelligent planning device for global multi-region satellite imaging task according to an embodiment of the present invention.
Detailed Description
Example 1
Referring to fig. 1, a step flow chart of a global multi-region satellite imaging task intelligent planning method provided by an embodiment of the present invention is shown, and as shown in fig. 1, the method may include the following steps:
Step 101: and establishing a satellite parameter library and initializing an observation period.
In the embodiment of the invention, when planning a global multi-region satellite imaging task, a satellite parameter library can be established first, and an observation period can be initialized. The implementation may be described in detail in connection with the following specific implementations.
In a specific implementation manner of the embodiment of the present invention, the step 101 may include:
substep A1: and establishing the satellite parameter library according to the satellite parameters of each satellite and the use constraint of the satellite imaging load.
In this embodiment, the satellite parameter library may be established according to the satellite parameters of each satellite and the usage constraint of the satellite imaging load. Specifically, a satellite basic parameter library including a satellite number sat_id, a load number payload_id satellite name sat_name, a nolad satellite number tle_num, a load Resolution, a load imaging Breadth, a satellite view angle FOV, a satellite Orbit height orbit_alt, a satellite basic parameter library and a Payload usage constraint library schematic shown in fig. 2, may be established, which are used to characterize relevant parameters of each satellite, so as to obtain Two lines of Two Line elements issued by a north american air defense command (nolad) through the satellite number, and calculate the cloud amount in the satellite transit and Breadth range.
Meanwhile, a satellite imaging load usage constraint library may be established, and in this example, the main constraints include the imaging load monorail longest imaging duration path_imgmax, the imaging load monorail shortest imaging duration path_imgmin, the imaging load total imaging duration path_imggall, and the satellite imaging load on-off time interval path_imginterval. The parameters are mainly used for all-region imaging task conflict resolution and scheme optimization.
Substep A2: and initializing an observation period according to the demand observation start time and the demand observation end time.
In this example, the satellite observation period may be initialized according to the demand observation start time and the demand observation end time. Specifically, the observation period is the required observation start time T start Time T of end of demand observation end The two moments are taken as the starting moment and the ending moment of the calculation of each satellite orbit, and cloud quantity prediction data between the two moments is taken as global cloud quantity data. In general T 2 -T 1 Less than or equal to 24 hours, and T 1 The time for making the satellite imaging task is less than or equal to 24 hours, so that the accuracy of satellite orbit prediction and cloud cover prediction values is ensured.
After the satellite parameter library is established and the observation period is initialized, step 102 is performed.
Step 102: registering global cloud cover data coordinates and counting multi-region cloud cover data.
After the satellite parameter library is established and the observation period is initialized, global cloud cover data coordinates in the initialization observation period and multi-region cloud cover data can be registered. The implementation may be described in detail in connection with the following specific implementations.
In a specific implementation of the present invention, the step 102 may include:
substep B1: and establishing an image coordinate system according to the global cloud cover prediction data.
In this embodiment, an image coordinate system may be established from the global cloud cover prediction data.
After the image coordinate system is established, sub-step B2 is performed.
Substep B2: and converting the global cloud quantity prediction data image coordinates into longitude and latitude coordinates according to the coordinate corresponding relation indicated by the image coordinate system and the pixel resolution.
After the image coordinate system is established, the cloud quantity prediction data image coordinate can be converted into longitude and latitude coordinates according to the coordinate corresponding relation indicated by the image coordinate system and the pixel resolution, and a geographic coordinate system is established.
And B3, converting the cloud quantity prediction data image coordinates into longitude and latitude coordinates according to the coordinate correspondence and pixel resolution indicated by the image coordinate system, and establishing a geographic coordinate system, and then executing the substep.
Substep B3: and cutting the regional cloud amount data through the obtained global cloud amount prediction data with the geographic coordinate system and the regional vector file to obtain the multi-region cloud amount data.
After the global cloud amount prediction data with the geographic coordinate system are obtained through conversion, the regional cloud amount data can be cut according to the regional vector file, and the multi-regional cloud amount data with the geographic coordinate system are obtained.
The above process may be described in detail in connection with the following procedure.
S2.1: the cloud quantity prediction Data product is in an HDF5 grid format for Data storage, the Data line (Date lines) is 3600, the pixel number (Data Pixels) of each line is 7200, the pixel value is represented by 0-100, the 0 represents area Pixels are all clear sky, the 100 represents area Pixels are all cloud coverage, and the filling value-999 represents that the part of Pixels are not in the prediction range. As shown in fig. 3, the cloud cover data image coordinate and longitude and latitude coordinate conversion flow:
establishing an image coordinate system, and defining a lower left corner point of the image, namely, the coordinates of the lower left corner of the pixel in the 0 th row and the 0 th column can be expressed as follows:
(X (0，0) ，Y (0，0) )＝(0，0)
the corresponding pixel value is K (0,0) 。
The ith row and the jth column of pixels P in the image ij The coordinates can be expressed as:
(X (i，j) ，Y (i，j) )＝(i，j)
wherein i is less than or equal to 3600, j is less than or equal to 7200, and the corresponding pixel value is K (i,j) 。
According to cloud quantity prediction data attribute description, the longitude and latitude coordinates corresponding to the left lower corner point of the known data are:
(Lon (0，0) ，Lat (0，0) )＝(-180.0，-90.0)
the pixel resolution is 0.05 according to Date lines and Data Pixels, the pixel P of the ith row and the jth column (i,j) The longitude and latitude corresponding to the lower left corner are as follows:
Lon (i，j) ＝Lon (0，0) +0.05×i
Lat (i，j) ＝Lat (0，0) +0.05×j
wherein i is less than or equal to 3600, j is less than or equal to 7200, and the corresponding pixel value is K (i,j) 。
S2.2: for counting regional cloud quantity data, converting an HDF5 format cloud quantity predicted image file into a TIF format according to a coordinate corresponding relation and pixel resolution, and establishing a GCS-WGS-1984 geographic coordinate system for the converted TIF file, wherein the graphical coordinate system is shown in a schematic diagram of cloud quantity predicted TIFF format images subjected to coordinate conversion and writing space references, and the graphical coordinate system is shown in a schematic diagram of cloud quantity predicted TIFF format image related parameters subjected to coordinate conversion and writing space references, as shown in fig. 5.
In this embodiment, the global cloud computing prediction data is further cut according to the global area vector space position, and the cut global area cloud computing data is processed, as shown in fig. 6 (a), which is a schematic diagram of an area cloud computing principle.
S2.3: and (3) cutting out regional cloud data through the regional vector file, and as shown in a schematic diagram of a global regional cloud example after cutting out in fig. 6 (b). And acquiring the Area area_1, area_2, …, area_i, … and area_m with cloud amount and coordinate information, traversing the cloud amount data of each Area, and processing the pixel value of the Area cloud amount data by taking the pixel value less than or equal to 20 as a limit value so as to calculate the effective data acquisition rate of the Area and perform Area preliminary screening.
After registering the global cloud cover data geographic coordinates and counting the multi-region cloud cover data, step 103 is performed.
Step 103: and calculating to obtain a satellite lower point track, and preprocessing the satellite lower point track to obtain preprocessed satellite lower point track data.
After registering global cloud cover data coordinate geography and counting multi-region cloud cover data, the satellite under-satellite point track can be calculated, and the satellite under-satellite point track is preprocessed to obtain preprocessed satellite under-satellite point track data. The implementation may be described in detail in connection with the following specific implementations.
In a specific implementation of the present invention, the step 103 may include:
substep C1: and acquiring two lines of roots of the satellite according to the satellite number, and preprocessing the two lines of roots of the satellite.
In this embodiment, two lines of roots issued by the north american air defense command (NORAD) may be acquired according to the satellite number, and the two lines of satellite root data may be preprocessed.
Substep C2: and determining the satellite point track of the satellite based on the preprocessed satellite two-line root data.
After the preprocessed satellite two-line root data is obtained, satellite point track data of the satellite can be determined based on the preprocessed satellite two-line root, wherein the data comprise the point transit time, longitude and latitude data.
After determining the satellite's satellite footprint for each satellite based on the preprocessed satellite's two-line root data, sub-step C3 is performed.
Substep C3: and performing down-orbit imaging satellite lower point screening on the satellite lower point track to obtain the preprocessed satellite lower point track data.
After determining the satellite under-satellite point track of each satellite based on the two preprocessed satellites, the satellite under-satellite point track can be subjected to down-orbit imaging under-satellite point screening to obtain preprocessed satellite under-satellite point track data, as shown in a schematic diagram of an example of the satellite under-satellite down-orbit point track based on the SGP4 algorithm model and preprocessed in FIG. 7.
The above implementation process may be described in detail in connection with the following procedure.
Assume time t 1 Point S of satellite on orbit 1 S is made 1 Connection with the earth center O, the connection intersecting with the surface of the earth surface at S' 1 Point S' 1 Becomes the satellite's point at this time. With the passage of time, the position of the satellite on the orbit changes continuously, the position of the satellite point on the earth also changes continuously, the satellite points are connected, and the track formed on the earth surface is called a satellite point track. Each calculated satellite sub-satellite point contains 3 parameters, t 1 The moment is exemplified by 3 parameters which are respectively the moment t of passing the border of the point under the satellite 1 Longitude of point under satellite lon 1 Latitude lat of point under satellite 1 。
S3.1, acquiring and preprocessing two rows of roots of the satellite. According to the NORAD satellite number TLE_num, two lines of roots of the satellite are acquired, line1 is defined as the 1 st Line of the corresponding two lines of roots of the satellite, and Line2 is defined as the 2 nd Line of the corresponding two lines of roots of the satellite.
S3.2: track forecasting is performed based on the SGP4 model. SGP4 is a model of spatial target forecasting proposed by the united states air force commander, which can more accurately forecast spatial target trajectories with time periods less than 225 min. In the land observation satellites in China, the solar synchronous orbit satellites move around the earth for about 98 minutes, so that the SGP4 model can be adopted for orbit calculation. Performing related algorithm design, setting calculation time period as initialization observation time period and calculation time interval, and outputting initialization time period T start To T end Transit time T of satellite lower point in period and corresponding longitude Lon and latitude Lat.
S3.3: and (5) screening the down-track imaging undersea points. T output as S3.2.2 start To T end The transit time T of the satellite lower point in the period and the corresponding longitude Lon and latitude Lat are used as inputs, and the conditions are set:
if Lat i ＞Lat i+1 =tube as initial condition, the stripThe arrangement of the piece can realize that only the down-orbit imaging of the optical satellite can be analyzed.
After the satellite under-satellite point track is calculated and preprocessed to obtain preprocessed satellite under-satellite point track data, step 104 is performed.
Step 104: and calculating to obtain the effective imaging arc section of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data.
After the satellite lower point track is calculated and preprocessed to obtain preprocessed satellite lower point track data, the satellite effective imaging arc section can be calculated according to the area cloud amount data after the multi-primary screening and the preprocessed satellite lower point track data. The implementation may be described in detail in connection with the following specific implementations.
In a specific implementation of the present invention, the step 104 may include:
substep D1: and acquiring the understar points contained in the regional vector file after the global regional cloud cover primary screening.
In the embodiment of the invention, the undersea points contained in the global prescreened area vector file can be obtained.
In the embodiment of the invention, after acquiring the satellite undersea points included in the vector file, the global preliminary screening is further combined with the satellite load imaging breadth in the satellite parameter library to acquire cloud data in the satellite load imaging breadth, as shown in fig. 9, a schematic representation of cloud efficiency in the regional satellite breadth, and calculate the longitude and latitude of each pixel point in the satellite load imaging breadth, as shown in fig. 10, a schematic representation of the longitude and latitude calculation flow of each pixel in the regional satellite breadth.
And D2, after acquiring the undersea points contained in the vector file of the global prescreened regional cloud cover data, executing the substep.
Substep D2: and calculating the longitude and latitude of the pixel point by a preset algorithm by taking the initial point of the satellite point below the satellite in the satellite transit area as a starting point and the end point as a site, and calculating the longitude and latitude of all the pixel points in the satellite load imaging breadth.
After the undersea points contained in the regional vector file after the global regional cloud cover is obtained, the undersea point initial point of the satellite transit region can be used as a starting point, the ending point is used as a site, the longitude and latitude of the pixel points in the path are calculated through a preset algorithm, and the longitude and latitude of the pixel points in the imaging breadth of the satellite load are calculated.
And D3, after the longitude and latitude of all the pixel points in the satellite load imaging breadth are calculated, executing a substep.
Substep D3: and screening the satellite transit arc sections according to the set cloud cover threshold value to obtain the effective satellite transit arc sections.
After the longitude and latitude of the pixel point in the satellite load imaging breadth are calculated, satellite transit arc section screening can be carried out according to the set cloud amount threshold value, so that an effective satellite transit arc section is obtained.
The above implementation procedure may be described in detail in connection with the following procedure.
Through P Area And (3) determining the starting moment of the global multi-region satellite imaging arc section by combining regional vector space positions which are not less than 60% of the primary screening and satellite lower point space-time information, setting a threshold by combining regional cloud quantity in the imaging breadth of the satellite load, and screening the satellite transit arc section again to determine the effective imaging arc section.
S4.1: and analyzing space-time information of the regional undersea points. The method comprises the steps of obtaining the undersea points contained in the global prescreened region vector file, and obtaining imaging start and end time and imaging duration of a corresponding region according to the quantity of the undersea points contained in the global prescreened region vector file and the undersea point time at the region edge.
S4.2: effective breadth cloud amount value statistics and cloud amount threshold setting. And calculating the longitude and latitude of the pixel point in the path through a Bresenham algorithm by taking the initial point of the satellite point below the satellite in the satellite transit area as a starting point and the end point as a site, and calculating the longitude and latitude of the pixel point in the satellite load imaging breadth. And (3) calculating the effective rate of data acquisition in the satellite load imaging breadth and setting a threshold value for screening the transit arc segments by traversing pixel values corresponding to each longitude and latitude.
S4.3: global multi-region satellite imaging arc information acquisition taking satellite breadth into account. With a set cloud threshold P path Screening the satellite transit arc segments to obtain effective satellite imaging arc segments, wherein the effective imaging arc segment information comprises a starting time T path_start Longitude Lon path_start Latitude Lat path_start End time T path_end Longitude Lon path_end Latitude Lat path_end Imaging duration t.
In the embodiment of the present invention, the imaging arc segments of the adjacent areas refer to the imaging band and the schematic view of the observation area of the satellite shown in fig. 11, where the satellite continuously performs imaging on two or more areas on the same satellite downlink track. Because satellites are actually imaged, the time interval of imaging arcs of adjacent areas, the total duration of two adjacent imaging arcs and the total duration of all imaging arcs of all areas need to meet the satellite imaging load using constraint in a satellite parameter library.
After calculating the satellite effective imaging arc from the multi-region cloud cover data and the pre-processed satellite lower point trajectory data, step 105 is performed.
Step 105: and setting a cloud amount threshold value within a satellite breadth range based on satellite imaging load use constraint in the satellite parameter library, and screening and optimizing the effective imaging arc segments of the satellite to obtain the optimal imaging arc segments.
After satellite effective imaging arcs are obtained through calculation according to the multi-region cloud amount data and the preprocessed satellite lower point track data, the effective observation arcs are ordered according to imaging time, and the ordered satellite effective imaging arcs are screened and optimized according to the ordering result, satellite imaging load use constraint in the satellite parameter library and cloud amount statistic values in satellite load imaging breadth, so that the optimal imaging arcs meeting the satellite load use constraint are obtained. The implementation may be described in detail in connection with the following specific implementations.
In a specific implementation of the present invention, the step 105 may include:
substep E1: and sequencing all the effective transit arcs by starting time of the effective transit arcs of the satellite.
In this embodiment, after obtaining the satellite effective transit imaging arcs, all the effective imaging arcs may be ordered at the starting time of the satellite effective imaging arcs.
After all the valid transit arcs are ordered at the start of the satellite valid transit arcs, a sub-step E2 is performed.
Substep E2: and screening the total effective imaging arc segments from the ordered satellite effective transit arc segments according to the ordering result, the satellite parameters in the satellite parameter library and the initialized observation period.
In this embodiment, the effective imaging period is also required to be selected according to the satellite adjacent gauge imaging interval constraint, the longest imaging time constraint, and the satellite usage constraint, as shown in fig. 12 (a) to 12 (e).
After all the effective imaging arcs are ordered at the starting moment of the effective transit arcs of the satellite, shortening, merging and deleting the effective imaging arcs according to the ordering result, the satellite imaging load use constraint in the satellite parameter library and the cloud amount statistical value in the satellite load imaging breadth, and counting the total number of the imaging arcs and the total imaging duration which are screened and optimized from the ordered effective transit arcs of the satellite.
After counting the total number of effective imaging arcs and the total imaging duration from the ordered satellite effective transit arcs, a sub-step E3 is performed.
Substep E3: and determining an optimal imaging arc section according to the effective imaging arc section information.
After the total number and the total imaging duration are obtained from the ordered satellite effective imaging arcs, screening and optimizing can be conducted again by resetting cloud amount threshold values according to satellite imaging load use constraint and cloud amount statistic values in satellite load imaging breadth in the satellite parameter library, so that the satellite effective imaging arcs meet the satellite load use constraint, and finally the optimal imaging arcs are determined. .
The above-described flow may be described in detail in connection with the following flow.
The imaging load monorail longest formation which is not in accordance with the description of S1.2 exists in the satellite effective transit window obtained in advanceConstraints such as image duration Path_imgmax, total imaging duration of imaging load, satellite imaging load on-off time interval Path_imginterval, etc. By the above parameter Path_imgap and P path And screening the effective imaging arc segments of the satellite so as to select the optimal imaging arc segments.
S5.1: effective imaging arc time setting and optimization. And judging whether the effective imaging duration T meets the imaging load monorail longest imaging duration Path_imgmax.
If the effective imaging duration T is less than or equal to Path_imgmax, then T path_start And tpath_end remains unchanged.
If the effective imaging duration T > Path_imgmax, then T path_end ＝T path_end -(T-Path_imgmax)，T path_start Remain unchanged.
S5.2: and (5) detecting and optimizing the constraint of adjacent effective imaging arc segments. Starting time T of effective transit arc section of satellite path_start Ordering all effective transit arcs, T path_end_i As the ending time of the ith effective transit arc segment, T path_start_i+1 Which is the starting time of the next active transit arc.
If T path_start_i+1 -T path_end_i If the time length of the combined arc segment is less than Path_imginterval, merging the two arc segments into one arc segment, judging whether the time length of the combined arc segment meets T & gtPath_imgmax, if not, canceling the merging, and simultaneously according to the following condition
And
and determining the optimal selection.
S5.3: judging by P path Under the cloud amount threshold condition, whether the total effective imaging arc segment quantity remained after the processing of the steps S5.1 and S5.2 meets the constraint of the total satellite daily imaging arc segment quantity and the total imaging duration or not, and if not, adjusting P path Is re-calculated until the two conditions are met.
After the optimal imaging arc segment is acquired, step 106 is performed.
Step 106: and generating a global multi-region imaging scheme of the satellite based on the optimal imaging arc section.
After the optimal imaging arc is acquired, a satellite global multi-region imaging solution may be generated based on the optimal imaging arc. The implementation may be described in detail in connection with the following specific implementations.
The satellite imaging task finally determined in the step derives the starting time T according to the time sequence path_start Longitude Lon path_start Latitude Lat path_start End time T path_end Longitude Lon path_end Latitude Lat path_end Imaging duration T, and forming a global multi-region multi-star joint task planning scheme.
Step S2.2 is to calculate regional cloud amount data, convert the cloud amount predicted image file in the HDF5 format into the TIF format according to the following steps, and establish a GCS-WGS-1984 coordinate system for the converted TIF file:
s2.2.1: and acquiring a cloud quantity prediction data HDF5 file structure, wherein the file structure comprises a group (group) and a dataset (dataset) of files, and reading the files by taking the group and the dataset of the cloud quantity prediction data as inputs.
S2.2.2: and determining the latitude and longitude range of the image display according to the image coordinates of the image corner points, the latitude and longitude coordinates of the corner points and the resolution ratio, and constructing GCS-WGS-1984 projection information.
S2.2.3: cloud cover prediction data TIF files are generated while the generated geospatial references are written.
S2.2.4: TIF file export.
In the step S2.3, the statistics and the processing of the multi-region cloud amount prediction data are performed to cut region cloud amount data through a region vector file, the cut region cloud amount is counted, and the calculation of the region effective data acquisition rate to perform region screening is performed according to the following process:
S2.3.1: and acquiring an Area vector file (SHP format file), cutting a cloud amount prediction TIF file according to the boundary of the Area vector file, numbering the TIF files cut by m Area vector files, and sequentially obtaining area_1, area_2, …, area_i, … and area_m.
S2.3.2：And judging and preprocessing cloud amount predicted values. In the conventional remote sensing image application, the cloud amount in one remote sensing image is less than or equal to 20%, the remote sensing image is considered to be effective, and if the cloud amount is more than 20%, the whole data of the image with more cloud amount is judged to be ineffective. Sequentially traversing each pixel value of the area_1, area_2, …, area_i, … and area_m files, assigning the pixel value to be 1 if the pixel value K (i, j) is less than or equal to 20, and assigning the pixel value to be 1 if the pixel value K is less than or equal to 20 (i，j) > 20 or pixel value K (i，j) And the pixel value is assigned to be 0 by the= -999, and the processing result is used as the calculation basis of the effective data acquisition rate of the area. A schematic of the principle of regional cloud processing is shown in fig. 6 (a).
Based on the area cloud amount preprocessing result, defining the effective data acquisition rate of the area as follows
Then:
wherein sum_K i sum_P for the total number of pixels with Area area_i cloud cover prediction value less than 20 i The total number of pixels in Area area_i.
S2.3.3: and (5) regional primary screening. At an area effective data acquisition rate P Area And (3) taking more than or equal to 60% as the area effective data to acquire and screen initial screening conditions, and removing areas with more cloud computing so as to improve the subsequent computing efficiency.
In the step S3.2, track prediction is performed based on the SGP4 model, and the sub-satellite point transit time T and the corresponding longitude Lon and latitude Lat are calculated according to the following steps:
s3.2.1: SGP 4-related algorithm programming is performed based on the open source code provided by AIAA 2006-6753.
S3.2.2: with Line1, line2, T start 、T end Substituting the parameters into an SGP4 algorithm model for calculation, setting the calculation interval to be 1s, and outputting T start To T end Transit time T of satellite lower point in period and corresponding longitude Lon and latitude Lat.
S4.1: and analyzing space-time information of the regional undersea points. The method comprises the steps of obtaining the undersea points contained in the global prescreened region vector file, and obtaining imaging start and end time and imaging duration of a corresponding region according to the quantity of the undersea points contained in the global prescreened region vector file and the undersea point time at the region edge.
S4.1.1: traversing the global initially screened area vector file and determining T start To T end Within a period of time satisfy Lat i ＞Lat i+1 Whether the longitude Lon and latitude Lat of the satellite under-satellite point of wire are within the boundary range of the area vector file or not is determined, and the number of points and attribute information of each under-satellite point contained in the single area vector file are determined, as shown in fig. 8.
S4.1.2: since the calculation time interval is 1s in 3.2.2, the number of points contained in the region can be used as the imaging duration of the region. Because the number of the imaging tasks of the satellite is limited every day, the imaging tasks of the region which are too short are removed by screening according to the imaging load of the satellite by using the imaging load monorail shortest imaging duration Path_imgmin in the constraint library.
In the step S4.2, the effective data acquisition rate P is carried out by calculating the cloud quantity value in the effective breadth of the satellite transit area path The imaging task secondary screening by setting the cloud cover threshold is carried out according to the following steps:
s4.2.1: and acquiring longitude and latitude coordinates of a satellite lower point at the starting and ending moments of the primary screening area, wherein the starting point is used as a site, and the ending point is used as a target point. Because of the limitation of the global area and the maximum time length of single imaging of the satellite, the regional satellite downlink is treated as a straight line, and the longitude and latitude of the pixel point are calculated through a Bresenham algorithm.
S4.2.2: and calculating cloud quantity values of satellite coverage areas. The pixels involved in calculation can be determined according to the longitude and latitude of the pixel points of the path of the load imaging Breadth Briadth and S4.2.1. Path pixel longitude and latitude Lon (i,j) 、Lat (i,j) The number of pixels is:
N p ＝(Breadth/2)÷Length lat
wherein Length is lat Indicating the latitude where the approach pixel is located, 0.05 degree corresponding to the ground surface distance N p The value of (2) is rounded up.
Length lat ＝(sin(90-|Lat (i,j) |)×6371×2×π/360)×0.05
Wherein |Lat (i,j) I is pixel P i,j Absolute value of corresponding latitude, 6371 is average radius of earth, length lat The unit is km.
The longitude and latitude coordinates of the pixels in the imaging breadth of all loads corresponding to the same row of the pixel points in the path, namely the same latitude, can be expressed as:
Lon (m,j) ＝Lon (i,j) +(m-i)×0.05
Lat (m,j) ＝Lat (i,j)
simultaneous judgment (Lon) (m,j) ，Lat (m,j) ) Whether within the area, as shown in fig. 10. Wherein the maximum value of m-i is N p 。
Traversing cloud quantity values corresponding to pixels in a satellite load imaging breadth range in an area imaging period, and solving a total value, namely the total number of pixels with the cloud quantity value smaller than 20 in the breadth, wherein the total number of pixels is as follows:
wherein sum_K i Coverage path for effective breadth of area i sum_P of pixels with cloud cover predicted value smaller than 20 i Region(s)
Total number of picture elements->
The effective data acquisition rate in the effective breadth.
S4.2.3: according to the number of global areas,
Distribution of values, dynamic adjustment of cloud threshold P path So that
To screen reasonable effective imaging arcs of satellites.
Example two
Referring to fig. 13, a schematic structural diagram of an intelligent planning apparatus for a global multi-region satellite imaging task according to an embodiment of the present invention is shown, and as shown in fig. 13, the apparatus may include the following modules:
A parameter library establishing module 810, configured to establish a satellite parameter library and initialize an observation period;
the cloud amount data statistics module 820 is used for registering global cloud amount data coordinates and counting multi-region cloud amount data;
the track data acquisition module 830 is configured to calculate a satellite lower point track, and perform preprocessing on the satellite lower point track to obtain preprocessed satellite lower point track data;
the effective imaging arc segment calculation module 840 is configured to calculate an effective imaging arc segment of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data;
the optimal imaging arc segment obtaining module 850 is configured to screen and optimize the effective imaging arc segments of the satellite based on the satellite load usage constraint and the cloud amount statistic value in the satellite load imaging breadth in the satellite parameter library, so as to obtain an optimal imaging arc segment;
the imaging scheme generating module 860 is configured to generate a global multi-region imaging scheme for the satellite based on the optimal imaging arc segment.
Optionally, the parameter library building module includes:
the satellite parameter library establishing unit is used for establishing the satellite parameter library according to the satellite parameters of each satellite and the use constraint of the satellite imaging load;
And the observation period initializing unit is used for initializing the observation period according to the requirement observation starting time and the requirement observation ending time.
Optionally, the cloud amount data statistics module includes:
the coordinate system establishing unit is used for establishing an image coordinate system according to cloud amount prediction data;
the coordinate registration unit is used for carrying out coordinate conversion on the cloud amount prediction data and establishing a longitude and latitude coordinate system according to the corresponding relation between the image coordinates indicated by the cloud amount prediction data image coordinate system and the longitude and latitude coordinates and the pixel resolution, so as to obtain global cloud amount data with longitude and latitude registration;
and the cloud quantity data acquisition unit is used for cutting regional cloud quantity data through the regional vector file and the registered global cloud quantity data to acquire the multi-region cloud quantity statistic value.
Optionally, the track data acquisition module includes:
the preprocessing unit is used for acquiring two-line roots of the satellite according to the satellite number and preprocessing the two-line roots of the satellite;
the satellite point track determining unit is used for determining satellite point tracks of all satellites based on the preprocessed two rows of satellites;
and the track data acquisition unit is used for carrying out down-track imaging satellite lower point screening on the satellite lower point track to obtain the preprocessed satellite lower point track data.
Optionally, the effective imaging arc segment calculation module includes:
the under-satellite point acquisition unit is used for acquiring the under-satellite points contained in the global prescreened area vector file;
the longitude and latitude calculating unit is used for calculating the longitude and latitude of the pixel point in the path through a preset algorithm by taking the initial point of the satellite point in the satellite transit area as a starting point and the end point as a site, and calculating the longitude and latitude of the pixel point in the satellite load imaging breadth;
the satellite transit arc section acquisition unit is used for screening the satellite transit arc sections according to the set cloud amount threshold value to obtain effective satellite transit arc sections.
Optionally, the optimal imaging arc segment acquisition module includes:
the arc segment sequencing unit is used for sequencing all the effective transit arc segments at the starting moment of the effective transit arc segments of the satellite;
the arc segment number screening and optimizing unit is used for shortening, merging and deleting effective imaging arc segments according to the sequencing result, satellite imaging load use constraint in the satellite parameter library and cloud amount statistic values in satellite load imaging breadth, and counting the total number of imaging arc segments and imaging total duration of screening and optimizing from the sequenced satellite effective transit arc segments;
and the imaging arc section determining unit is used for resetting the cloud amount threshold value to screen and optimize again according to the total number and total duration of the screened and optimized imaging arc sections and the satellite imaging load using constraint in satellite parameters, and determining the optimal imaging arc section.
The specific embodiments described herein will be described in order to provide a more thorough understanding of the present application to those skilled in the art, and are not intended to limit the present application in any way. Accordingly, it will be understood by those skilled in the art that the present application is still modified or equivalently substituted; all technical solutions and modifications thereof that do not depart from the spirit and technical essence of the present application are intended to be included in the protection scope of the present application.
What is not described in detail in the present specification is a well known technology to those skilled in the art.
Claims (12)
1. An intelligent planning method for a global multi-region satellite imaging mission, which is characterized by comprising the following steps:
establishing a satellite parameter library and initializing an observation period;
registering global cloud cover data coordinates and counting multi-region cloud cover data;
calculating to obtain satellite lower point tracks, and preprocessing the satellite lower point tracks to obtain preprocessed satellite lower point track data;
calculating to obtain an effective imaging arc section of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data;
based on satellite imaging load use constraint in the satellite parameter library, setting a cloud amount threshold value in a satellite breadth range, and screening and optimizing the effective imaging arc segments of the satellite to obtain optimal imaging arc segments;
And generating a global multi-region imaging scheme of the satellite based on the optimal imaging arc section.
2. The method of claim 1, wherein the establishing a satellite parameter library and initializing an observation period comprises:
establishing the satellite parameter library according to the satellite parameters of each satellite and the use constraint of the satellite imaging load;
and initializing an observation period according to the demand observation start time and the demand observation end time.
3. The method of claim 1, wherein said registering global cloud cover prediction data coordinates and counting multi-region cloud cover data comprises:
establishing an image coordinate system according to cloud cover prediction data;
according to the corresponding relation between the image coordinates indicated by the cloud amount prediction data image coordinate system and the longitude and latitude coordinates and the pixel resolution, carrying out coordinate conversion on the cloud amount prediction data and establishing a longitude and latitude coordinate system to obtain global cloud amount data with longitude and latitude registration;
and cutting out regional cloud quantity data through the global regional vector file and the registered global cloud quantity data, and obtaining the multi-regional cloud quantity statistic value.
4. The method of claim 1, wherein the calculating obtains a satellite lower point track and the preprocessing the satellite lower point track to obtain preprocessed satellite lower point track data comprises:
Acquiring two rows of roots of a satellite according to the satellite number, and preprocessing the two rows of roots of the satellite;
determining satellite point tracks of all satellites based on the preprocessed two satellites;
and performing down-orbit imaging satellite lower point screening on the satellite lower point track to obtain the preprocessed satellite lower point track data.
5. The method of claim 1, wherein said calculating a satellite effective imaging arc from said multi-region cloud cover data and said pre-processed satellite lower point trajectory data comprises:
acquiring the understar points contained in the regional vector file meeting the cloud cover requirement after the global regional data is primarily screened;
taking a satellite transit area satellite under-satellite point initial point as a starting point, taking an end point as a site, calculating the longitude and latitude of the pixel point by a path through a preset algorithm, and calculating the longitude and latitude of all the pixel points in the satellite load imaging breadth;
and counting cloud quantity values corresponding to longitude and latitude positions in satellite load imaging breadth in each global area, and screening the satellite transit arcs by using a cloud quantity threshold value in the set satellite load imaging breadth to obtain the effective satellite transit arcs.
6. The method of claim 1, wherein the setting a cloud cover threshold within a satellite payload imaging breadth based on satellite imaging payload usage constraints in the satellite parameter library, and the screening and optimizing the satellite effective imaging arcs to obtain optimal imaging arcs, comprises:
Sequencing all the effective transit arcs by starting time of the effective transit arcs of the satellite;
according to the sequencing result, satellite imaging load use constraint in the satellite parameter library and cloud amount statistical values in the satellite load imaging breadth, shortening, merging and deleting effective imaging arcs, and counting the total number of imaging arcs and total imaging duration which are screened and optimized from the sequenced satellite effective transit arcs;
and resetting the cloud amount threshold value according to the total number and the total duration of the screened and optimized imaging arcs to screen and optimize again so as to meet the satellite imaging load use constraint in satellite parameters and determine the optimal imaging arcs.
7. A global multi-region satellite imaging mission intelligent planning apparatus, the apparatus comprising:
the parameter library establishing module is used for establishing a satellite parameter library and initializing an observation period;
the cloud amount data statistics module is used for registering global cloud amount data coordinates and counting multi-region cloud amount data;
the track data acquisition module is used for calculating to obtain satellite lower point tracks, and preprocessing the satellite lower point tracks to obtain preprocessed satellite lower point track data;
The effective imaging arc segment calculation module is used for calculating and obtaining an effective imaging arc segment of the satellite according to the multi-region cloud cover data and the preprocessed satellite lower point track data;
the optimal imaging arc segment acquisition module is used for screening and optimizing the effective imaging arc segments of the satellite based on satellite imaging load use constraint and cloud amount statistic value in satellite load imaging breadth in the satellite parameter library to obtain optimal imaging arc segments;
and the imaging scheme generating module is used for generating a satellite global multi-region imaging scheme based on the optimal imaging arc section.
8. The apparatus of claim 7, wherein the parameter library creation module comprises:
the satellite parameter library establishing unit is used for establishing the satellite parameter library according to the satellite parameters of each satellite and the use constraint of the satellite imaging load;
and the observation period initializing unit is used for initializing the observation period according to the requirement observation starting time and the requirement observation ending time.
9. The apparatus of claim 7, wherein the cloud data statistics module comprises:
the coordinate system establishing unit is used for establishing an image coordinate system according to cloud amount prediction data;
The coordinate registration unit is used for carrying out coordinate conversion on the cloud amount prediction data and establishing a longitude and latitude coordinate system according to the corresponding relation between the image coordinates indicated by the cloud amount prediction data image coordinate system and the longitude and latitude coordinates and the pixel resolution, so as to obtain global cloud amount data with longitude and latitude registration;
and the cloud quantity data acquisition unit is used for cutting regional cloud quantity data through the regional vector file and the registered global cloud quantity data to acquire the multi-region cloud quantity statistic value.
10. The apparatus of claim 7, wherein the trajectory data acquisition module comprises:
the preprocessing unit is used for acquiring two-line roots of the satellite according to the satellite number and preprocessing the two-line roots of the satellite;
the satellite point track determining unit is used for determining satellite point tracks of all satellites based on the preprocessed two rows of satellites;
and the track data acquisition unit is used for carrying out down-track imaging satellite lower point screening on the satellite lower point track to obtain the preprocessed satellite lower point track data.
11. The apparatus of claim 7, wherein the effective imaging arc segment calculation module comprises:
the under-satellite point acquisition unit is used for acquiring under-satellite points contained in the area vector file meeting the cloud cover requirement after the global area data is primarily screened;
The longitude and latitude calculating unit is used for calculating the longitude and latitude of the pixel point by a preset algorithm by taking the initial point of the satellite point in the satellite transit area as a starting point and the end point as a site, and calculating the longitude and latitude of all the pixel points in the satellite load imaging breadth;
the satellite transit arc section acquisition unit is used for counting cloud quantity values corresponding to longitude and latitude positions in satellite load imaging breadth in each global area, and satellite transit arc section screening is carried out according to the set cloud quantity threshold value in the satellite load imaging breadth to obtain an effective imaging arc section.
12. The apparatus of claim 7, wherein the optimal imaging arc acquisition module comprises:
the arc segment sequencing unit is used for sequencing all the effective transit arc segments at the starting moment of the effective transit arc segments of the satellite;
the arc segment number screening and optimizing unit is used for shortening, merging and deleting effective imaging arc segments according to the sequencing result, satellite imaging load use constraint in the satellite parameter library and cloud amount statistic values in satellite load imaging breadth, and counting the total number of imaging arc segments and imaging total duration of screening and optimizing from the sequenced satellite effective transit arc segments;
and the imaging arc section determining unit is used for resetting the cloud amount threshold value to screen and optimize again according to the total number and total duration of the screened and optimized imaging arc sections and the satellite imaging load using constraint in satellite parameters, and determining the optimal imaging arc section.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211430903.4A CN116205428A (en) | 2022-11-15 | 2022-11-15 | Intelligent planning method and device for global multi-region satellite imaging task |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211430903.4A CN116205428A (en) | 2022-11-15 | 2022-11-15 | Intelligent planning method and device for global multi-region satellite imaging task |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116205428A true CN116205428A (en) | 2023-06-02 |
Family
ID=86510204
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211430903.4A Pending CN116205428A (en) | 2022-11-15 | 2022-11-15 | Intelligent planning method and device for global multi-region satellite imaging task |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116205428A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116957272A (en) * | 2023-07-27 | 2023-10-27 | 北京和德宇航技术有限公司 | Satellite task planning method and device, electronic equipment and storage medium |
-
2022
- 2022-11-15 CN CN202211430903.4A patent/CN116205428A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116957272A (en) * | 2023-07-27 | 2023-10-27 | 北京和德宇航技术有限公司 | Satellite task planning method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109165858B (en) | Multi-satellite scheduling method for large-area target observation | |
| CN101604018B (en) | Method and system for processing high-definition remote sensing image data | |
| CN108256822A (en) | One kind is suitable for weather information secondary satellite imaging task planning system and method | |
| US20230213337A1 (en) | Large-scale forest height remote sensing retrieval method considering ecological zoning | |
| CN104063748B (en) | A kind of algorithm for being used to solve towards the time-dependent scheduling problem of imaging satellite | |
| CN114612439A (en) | Planet scale analysis system | |
| US7616828B2 (en) | Geospatial modeling system providing geospatial model data target point filtering based upon radial line segments and related methods | |
| CN107527328B (en) | Unmanned aerial vehicle image geometric processing method considering precision and speed | |
| CN111091502A (en) | Remote sensing image color homogenizing method and system, storage medium and electronic equipment | |
| CN113935227A (en) | Optical satellite intelligent task planning method based on real-time meteorological cloud picture | |
| CN113343777B (en) | Satellite rapid planning method and system for natural disaster emergency response | |
| CN110008297B (en) | Optimal coverage satellite image screening method based on geographic grids | |
| CN113744249B (en) | Marine ecological environment damage investigation method | |
| US7778808B2 (en) | Geospatial modeling system providing data thinning of geospatial data points and related methods | |
| CN116205428A (en) | Intelligent planning method and device for global multi-region satellite imaging task | |
| CN114092835A (en) | Normalized vegetation index data space-time fusion method based on different space-time resolutions | |
| CN116153140A (en) | River basin emergency unmanned aerial vehicle management method and platform | |
| CN112180987B (en) | Collaborative operation method, collaborative operation system, collaborative operation device, collaborative operation computer equipment and collaborative operation storage medium | |
| CN110807579A (en) | Satellite task planning method for minimum completion time under sufficient resource condition | |
| CN111967734B (en) | Method for planning earth observation requirement of micro-nano remote sensing network | |
| CN114118511A (en) | Large-area multi-satellite combined coverage effectiveness evaluation method based on cloud amount prediction information | |
| CN114639023A (en) | Roof photovoltaic potential evaluation method based on machine learning | |
| CN114758087B (en) | Method and device for constructing urban information model | |
| CN116305487A (en) | Unmanned aerial vehicle-based roof photovoltaic power station design method and system | |
| CN113758467B (en) | Remote sensing satellite region observation method based on region division and local grid nesting |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |