Time domain resampling grid clustering multitask implementation method and system

Abstract

The invention provides a time domain resampling grid clustering multi-task implementation method and system, and the method comprises the steps: carrying out the matching according to obtained camera data and imaging sampling moment data added to position and attitude data, calculating the sub-satellite point position of a sampling moment according to the adaptive position and attitude data, and obtaining M rows and N columns of grid angular point data; caching M rows of grid angular point data, and calculating a normal vector of a projection plane; according to the adapted position and attitude data and all camera data, calculating position and attitude data of camera data i pixel scatter points; calculating the coordinates of the pixels in the earth-fixed coordinate system; and constructing an auxiliary plane for clustering according to the M-row N-column grid angular point data and the coordinates of the normal vector and the pixel of the projection plane in the earth-fixed coordinate system. According to the invention, a grid clustering algorithm is realized in a lightweight manner, a data-driven multi-task software architecture is adopted to be deeply coupled with multi-core processor hardware, the method can be conveniently deployed in a line-on-satellite embedded system, and the method has good real-time performance.

Description

Technical Field

[0001] The present invention relates to the field of remote sensing technology, and in particular to a time domain resampling grid clustering multi-task implementation method and system. Background Art

[0002] Satellites are equipped with multiple imaging systems that scan and image the ground in different ways, and then send the pixels together with the imaging time information in the form of data packets to information processing equipment. Clustering pixels imaged at the same location, different imaging devices, different bands, and different times, performing information fusion, and generating different data products is a common application scenario. Generally, the images of each imaging system are transmitted back to the ground, and then pixel clustering and information fusion are performed. With the continuous increase of image data and the limitation of satellite communication bandwidth, clustering and fusion need to be processed in real time on the satellite, and the clustering algorithms of existing ground systems are often not applicable to satellite-borne embedded systems.

[0003] Implementing clustering and fusion algorithms on embedded hardware requires solving two problems: one is how to make the algorithm itself lightweight; the other is how to deeply couple with embedded hardware to fully utilize resources. Summary of the invention

[0004] In view of the defects in the prior art, the object of the present invention is to provide a time domain resampling grid clustering multi-task implementation method and system.

[0005] A time domain resampling grid clustering multi-task implementation method provided by the present invention includes:

[0006] Synchronous adaptation step: matching the acquired camera data and the imaging sampling time data attached to the position and posture data, and eliminating the mismatched data;

[0007] Grid corner point generation step: Calculate the sub-satellite point position at the sampling time according to the adapted position and attitude data, calculate the coordinates of N corner points on the earth's spherical surface according to the set vertical track direction resolution and the vertical track direction field angle of all cameras, and the set vertical track direction resolution, and obtain M rows and N columns of grid corner point data;

[0008] Projection plane calculation steps: cache M rows of grid corner point data and calculate the normal vector of the projection plane;

[0009] The time resampling step of the camera pixel scattered point position and attitude data: according to the adapted position and attitude data and all camera data, the position and attitude data of the camera data i pixel scattered points are calculated before and after the imaging sampling time of the camera data i;

[0010] Camera pixel scattered point positioning and calibration step: calculating the coordinates of the pixel in the ground-fixed coordinate system according to the position and posture data of the pixel scattered point i of the camera data;

[0011] Grid clustering steps: Based on the M rows and N columns of grid corner point data, the projection plane normal vector and the coordinates of the pixel in the ground-fixed coordinate system, an auxiliary plane is constructed for clustering.

[0012] Preferably, the position and attitude data of the pixel scatter points of the camera data i include the position of the pixel scatter points in the imaging plane, the direction of the camera optical axis relative to the satellite body, the attitude of the satellite body, the position of the satellite and the imaging sampling time.

[0013] Preferably, a data-driven multi-task communication scheduling architecture is used to implement the synchronous adaptation step, the grid corner point generation step, the projection plane calculation step, the camera pixel scattered point position posture data time resampling step, the camera pixel scattered point positioning calibration step, and the grid clustering step;

[0014] Among them, the synchronous adaptation step is a task, the grid corner point generation step and the projection plane calculation step are combined into one task implementation, the camera data i pixel scattered point position posture data time resampling step and the camera data i scattered point positioning and calibration step are combined into one task implementation, and the grid clustering step is implemented as one task.

[0015] Preferably, the data-driven multi-task communication scheduling architecture adopts a multi-core CPU deployment method, the task input and output data are one or more data objects, and the buffer structure of the data object is a queue;

[0016] The data communication interface between the tasks is in the form of a queue.

[0017] Preferably, the grid clustering step comprises:

[0018] Project the grid corner points onto the projection plane to form a grid in the projection plane. These grid corner points are called projection grid corner points, these grids are called projection grids, and the area where they are located is the projection grid area; project the pixel scattered points onto the projection plane, and these points are called projection pixel scattered points;

[0019] Establish an affine auxiliary grid, the row length of the auxiliary grid is equal to the row length of the projection grid at the corresponding position near the center line of the projection grid area; the column length of the auxiliary grid is equal to the column length of the projection grid near the center position of the projection grid area or equal to the column length of the projection grid at the corresponding position near the center line of the projection grid area;

[0020] Cluster the projected pixel scatter points onto an auxiliary grid;

[0021] The projected pixel scatter points are corrected to the projected grid, and the corresponding pixel scatter points are clustered to the corresponding spherical grid.

[0022] A time domain resampling grid clustering multi-task implementation system provided by the present invention includes:

[0023] Synchronous adaptation module: matches the acquired camera data and the imaging sampling time data attached to the position and posture data, and removes the mismatched data;

[0024] Grid corner point generation module: Calculate the sub-satellite point position at the sampling time according to the adapted position and attitude data, calculate the coordinates of N corner points on the earth's spherical surface according to the set vertical track resolution and the vertical track field angle of all cameras, and the set vertical track resolution, and obtain M rows and N columns of grid corner point data;

[0025] Projection plane calculation module: cache M rows of grid corner point data and calculate the normal vector of the projection plane;

[0026] Camera pixel scattered point position and attitude data time resampling module: calculates the position and attitude data of the scattered points of camera data i pixel based on the adapted position and attitude data and all camera data before and after the imaging sampling time of camera data i;

[0027] Camera pixel scatter point positioning and calibration module: calculates the coordinates of the pixel in the ground-fixed coordinate system according to the position and posture data of the pixel scatter point i of the camera data;

[0028] Grid clustering module: Construct auxiliary planes for clustering based on M rows and N columns of grid corner point data, projection plane normal vectors, and pixel coordinates in the ground-fixed coordinate system.

[0029] Preferably, the position and attitude data of the pixel scatter points of the camera data i include the position of the pixel scatter points in the imaging plane, the direction of the camera optical axis relative to the satellite body, the attitude of the satellite body, the position of the satellite and the imaging sampling time.

[0030] Preferably, a data-driven multi-task communication scheduling architecture is used to implement the synchronization adaptation module, grid corner point generation module, projection plane calculation module, camera pixel scattered point position posture data time resampling module, camera pixel scattered point positioning calibration module, and grid clustering module;

[0031] Among them, the synchronous adaptation module is a task, the grid corner point generation module and the projection plane calculation module are merged into one task implementation, the camera data i pixel scattered point position posture data time resampling module and the camera data i scattered point positioning and calibration module are merged into one task implementation, and the grid clustering module is implemented as a task.

[0032] Preferably, the data-driven multi-task communication scheduling architecture adopts a system deployed with a multi-core CPU, the task input and output data are one or more data objects, and the buffer structure of the data object is a queue;

[0033] The data communication interface between the tasks is in the form of a queue.

[0034] Preferably, the grid clustering module comprises:

[0035] Project the grid corner points onto the projection plane to form a grid in the projection plane. These grid corner points are called projection grid corner points, these grids are called projection grids, and the area where they are located is the projection grid area; project the pixel scattered points onto the projection plane, and these points are called projection pixel scattered points;

[0036] Establish an affine auxiliary grid, the row length of the auxiliary grid is equal to the row length of the projection grid at the corresponding position near the center line of the projection grid area; the column length of the auxiliary grid is equal to the column length of the projection grid near the center position of the projection grid area or equal to the column length of the projection grid at the corresponding position near the center line of the projection grid area;

[0037] Cluster the projected pixel scatter points onto an auxiliary grid;

[0038] The projected pixel scatter points are corrected to the projected grid, and the corresponding pixel scatter points are clustered to the corresponding spherical grid.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The imaging data of various imaging systems and the satellite position and attitude data often have different sampling frequencies and transmission delays. The present invention adopts a multi-task data-driven architecture to synchronize adaptation, grid angle generation, projection plane calculation, time resampling of position and attitude data, positioning calibration, and grid clustering, and implements the grid clustering algorithm in a lightweight manner. It adopts a data-driven multi-task software architecture and is deeply coupled with multi-core processor hardware. It can be easily deployed in a satellite-borne embedded system and has good real-time performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0042] Figure 1 It is a schematic diagram of the working method of the present invention;

[0043] Figure 2 It is the corner point section relationship diagram;

[0044] Figure 3 It is a top view of the projection plane. DETAILED DESCRIPTION

[0045] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0046] Embodiment 1

[0047] According to a time domain resampling grid clustering multi-task implementation method provided by the present invention, Figures 1 to 3 As shown, including:

[0048] Synchronous adaptation step: Match the acquired camera data and the imaging sampling time data attached to the position and posture data, and remove the unmatched data. For n cameras, camera data 1 to n and the position and posture data are synchronously adapted in sequence according to the sampling time. The imaging sampling time intervals of camera data 1 to n and the position and posture data are different, and the transmission delays are different. Match the imaging sampling time data attached to data 1 to n and the position and posture data, and remove the data whose sampling time is too early to match other data for grid clustering. The adapted position and posture data is used as input for the grid angle generation step, and the adapted position and posture data and camera data 1 are used as input for the camera data 1 pixel scattered position and posture data time resampling step, and the adapted position and posture data and camera data 2 are used as input for the camera data 2 pixel scattered position and posture data time resampling step, and so on, until the adapted position and posture data and camera data n are sent to the camera data n pixel scattered position and posture data time resampling step.

[0049] Grid corner point generation steps: Calculate the subsatellite point position at the sampling time based on the adapted position and attitude data, calculate the coordinates of N corner points on the earth's spherical surface based on the set vertical track resolution and the vertical track field angle of all cameras, and the set vertical track resolution, and obtain M rows and N columns of grid corner point data. The projection plane passes through the center of the sphere and is perpendicular to the line connecting the center of the sphere and the center of the corner point area on the sphere.

[0050] Projection plane calculation steps: cache M rows of grid corner point data and calculate the normal vector of the projection plane;

[0051] The step of time resampling of the camera pixel scatter point position and attitude data: according to the adapted position and attitude data and all camera data, the position and attitude data of the camera data i pixel scatter point are calculated before and after the imaging sampling time of the camera data i; the position and attitude data of the camera data i pixel scatter point include the position of the pixel scatter point in the imaging plane, the direction of the camera optical axis relative to the satellite body, the attitude of the satellite body, the position of the satellite and the imaging sampling time.

[0052] Camera pixel scattered point positioning and calibration step: calculating the coordinates of the pixel in the ground-fixed coordinate system according to the position and posture data of the pixel scattered point i of the camera data;

[0053] Grid clustering step: According to the M rows and N columns of grid corner point data, the projection plane normal vector and the coordinates of the pixel in the ground fixed coordinate system, an auxiliary plane is constructed for clustering. The grid clustering step includes: projecting the grid corner points to the projection plane to form a grid in the projection plane, calling these grid corner points as projection grid corner points, calling these grids as projection grids, and the area where they are located is the projection grid area; projecting pixel scattered points to the projection plane, calling these points as projection pixel scattered points; establishing an affine auxiliary grid, the row length of the auxiliary grid is equal to the row length of the projection grid at the corresponding position near the center line of the projection grid area row direction; the column length of the auxiliary grid is equal to the column length of the projection grid near the center position of the projection grid area or equal to the column length of the projection grid at the corresponding position near the center line of the projection grid area column direction; clustering the projection pixel scattered points to the auxiliary grid; correcting the projection pixel scattered points to the projection grid, and clustering the corresponding pixel scattered points to the corresponding spherical grid.

[0054] Furthermore, the grid clustering step is described in detail as follows:

[0055] First, a two-dimensional coordinate system is established in the projection plane, with the Y axis along the track and the X axis perpendicular to the track. Project the corner points of the M-row and N-column grids onto the projection plane. The M-row grid corner points form (M-1)*(N-1) target grids, and record the coordinates of the grid corner points in the above two-dimensional coordinate system. Project the pixel points onto the projection plane, and record the coordinates of the pixel points in the above two-dimensional coordinate system. The calculation steps for the projection step in the two-dimensional coordinate system are as follows:

[0056] Step 1. In the earth-fixed coordinate system, calculate the coordinates of the unit vectors of the X-axis and Y-axis of the two-dimensional coordinate system, respectively, and record them as (x xn ,y xn ,z xn ) and (x yn ,y yn ,z yn ).

[0057] Step 2. Note that the coordinates of the point to be projected in the ground-fixed coordinate system are (x r0,y r0 ,z r0 ), the projection point coordinates on the projection plane (x p ,y p ) is calculated using the following formula:

[0058] x p =x r0 x xn +y r0 y xn +z r0 z xn

[0059] y p =x r0 x yn +y r0 y yn +z r0 z yn

[0060] Then, an auxiliary grid is established on the projection plane. The auxiliary grid consists of M rows and N columns of grid corner points, which correspond to the target grid one by one and coincide with the center. The time when the first row of grid corner points is generated is t 1 , the generation time of the Mth row grid corner point is t 2 , define t m =(t 1 +t 2 ) / 2. The distance between all adjacent grid corner points in the Y-axis direction of the auxiliary grid is equal to t m -Δt / 2 time and t m +Δt / 2, the distance between the subsatellite point and the projection point on the projection plane is denoted as d. Generate t according to the method in the grid corner point generation step. m The corner point data at each moment is projected onto the auxiliary plane to form a point column L consisting of N points. This point column corresponds one-to-one with the corner point column of each row of the auxiliary grid, and the corresponding points have the same X coordinates.

[0061] Next, by calculating the ratio of the y coordinate in the projection coordinates of the pixel to d, determine which row of the grid the pixel belongs to, and by comparing the x coordinate value in the point column L, determine which column the pixel is in.

[0062] Finally, the projection plane is divided into two parts, upper and lower, with the point sequence L as the boundary. The specific steps are as follows:

[0063] 1. For the pixels in the upper (lower) half, find the corresponding target grid based on the auxiliary grid where it is located, and take out the coordinates of the upper (lower) corner points of the grid;

[0064] 2. Determine whether the pixel point is above (below) the straight line connecting the above corner points. If yes, execute step 3; if not, the correction ends;

[0065] 3. Correct the pixels to the target grid directly above (below) and return to step 1 to execute.

[0066] The step of determining whether a pixel point is above (below) the straight line connecting the corner points is as follows: Calculate A = (x 0 -x 1 )(y 1 -x p )-(y 0 -y 1 )(x 1 -x p ), where (x 0 ,y 0 ), (x 1 ,y 1 ) are pixel scatter points (x p ,y p ) left and right corner points. Judge A. If A is negative, (x p ,y p ) in (x 0 ,y 0 ), (x 1 ,y 1 ) below the line connecting them; if positive, above.

[0067] The present invention adopts a data-driven multi-task communication scheduling architecture to implement the synchronous adaptation step, the grid corner point generation step, the projection plane calculation step, the camera pixel scattered point position posture data time resampling step, the camera pixel scattered point positioning and calibration step, and the grid clustering step; wherein the synchronous adaptation step is a task, the grid corner point generation step and the projection plane calculation step are combined into one task implementation, the camera data i pixel scattered point position posture data time resampling step and the camera data i scattered point positioning and calibration step are combined into one task implementation, and the grid clustering step is one task implementation. The data-driven multi-task communication scheduling architecture adopts a multi-core CPU deployment method, and the task input and output data are all one or more data objects, and the buffer structure of the data object is a queue; the data communication interface between the tasks is in the form of a queue. The multi-task scheduling method includes:

[0068] Step 1: Initialization.

[0069] Step 2: Set the initialization completion flag.

[0070] Step 3: Detect the initialization completion flags of other cores.

[0071] Step 4: Determine whether all other core initializations are completed. If completed, go to step 5. If not, go to step 3.

[0072] Step 5: Query the next task input queue buffer according to the set rules. The querying the next task input queue according to the rules in step 5 includes the following steps: Step 5.1: Increment by 1 with N as modulo n, where N is the number of tasks deployed in the core. Step 5.2: Increment the query counter variable to which task n belongs by 1. Step 5.3: Determine whether the task query counter is equal to the set value. If it is equal to the set value, go to step 4. If it is not equal to the set value, go to step 1. Step 5.4: Clear the counter variable, read the task n queue information, and the querying the next task input queue according to the rules is completed.

[0073] Step 6: Determine whether the conditions for executing the task are met. If so, go to step 7. If not, go to step 5.

[0074] Step 7: Execute the task and go to step 5 after completion.

[0075] The multi-core CPU large-capacity off-chip memory space configuration method includes dividing the large-capacity off-chip memory space into a multi-core shared memory space and a multi-core private memory space. The multi-core multi-task deployment method includes: the average execution time of all tasks in each core is less than the average generation interval of the core source queue data. The source queue data comes from the CPU external interface or other core task output; the queue buffer capacity should be set to be greater than the capacity requirement in the case of data burst; the writing and reading tasks are respectively deployed in the queues of different cores and deployed on the shared memory. Among them, the large-capacity off-chip memory includes DDR and HBM, the queue data is implemented by array, the queue read and write pointers are respectively implemented by array index variables, the queue elements in the queue are aligned with the cache block boundary, the private memory cache is turned on, the shared memory cache is turned off, the queue pointer is deployed on the CPU chip shared memory, and the task reads the data from other cores and copies it to the private memory of the core for processing. The shared memory cache for deploying queue data is turned on, the task does not copy the data from other cores to the private memory, and is directly processed on the shared memory. Before processing, the relevant part of the local cache is invalidated, and the write-back operation is performed after the processing is completed.

[0076] Specifically, the multi-core multi-task deployment method includes a single image loading method, wherein the single image loading method loads the program image into the shared memory, and all cores read the program from the same memory, and the program in the program image contains a judgment statement for comparing the core ID number, and jumps to execute the program matching the ID number. The multi-core multi-task deployment method includes a multi-image loading method, wherein the single image loading method loads the program image into the private memory of each core respectively, and all cores read the program from each sub-private memory.

[0077] Embodiment 2

[0078] The present invention also provides a time domain resampling grid clustering multi-task implementation system, which can be implemented by executing the process steps of the time domain resampling grid clustering multi-task implementation method, that is, those skilled in the art can understand the time domain resampling grid clustering multi-task implementation method as a preferred implementation method of the time domain resampling grid clustering multi-task implementation system.

[0079] A time domain resampling grid clustering multi-task implementation system provided by the present invention includes:

[0080] Synchronous adaptation module: matches the imaging sampling time data attached to the acquired camera data and position and posture data, and removes the mismatched data.

[0081] Grid corner point generation module: Calculate the sub-satellite point position at the sampling time based on the adapted position and attitude data, calculate the coordinates of N corner points on the earth's spherical surface based on the set vertical track resolution and the vertical track field angle of all cameras, and the set vertical track resolution to obtain M rows and N columns of grid corner point data.

[0082] Projection plane calculation module: caches M lines of grid corner point data and calculates the normal vector of the projection plane.

[0083] Camera pixel scatter point position and attitude data time resampling module: Calculate the position and attitude data of the pixel scatter points of camera data i based on the adapted position and attitude data and all camera data before and after the imaging sampling time of camera data i. The position and attitude data of the pixel scatter points of camera data i include the position of the pixel scatter points in the imaging plane, the direction of the camera optical axis relative to the satellite body, the attitude of the satellite body, the position of the satellite and the imaging sampling time.

[0084] Camera pixel scatter point positioning and calibration module: calculates the coordinates of the pixel in the ground-fixed coordinate system based on the position and posture data of the pixel scatter point i of the camera data.

[0085] Grid clustering module: According to the M rows and N columns of grid corner point data, the projection plane normal vector and the coordinates of the pixel in the ground-fixed coordinate system, an auxiliary plane is constructed for clustering. The grid clustering module includes: projecting the grid corner points to the projection plane to form a grid in the projection plane, calling these grid corner points as projection grid corner points, calling these grids as projection grids, and the area where they are located is the projection grid area. Projecting pixel scattered points to the projection plane, calling these points as projection pixel scattered points. Establishing an affine auxiliary grid, the row-direction side length of the auxiliary grid is equal to the row-direction side length of the projection grid at the corresponding position near the row-direction center line of the projection grid area. The column-direction side lengths of the auxiliary grid are all equal to the column-direction side lengths of the projection grid near the center position of the projection grid area or equal to the column-direction side lengths of the projection grid at the corresponding position near the column-direction center line of the projection grid area. Clustering the projected pixel scattered points to the auxiliary grid. Correcting the projected pixel scattered points to the projection grid, and clustering the corresponding pixel scattered points to the corresponding spherical grid.

[0086] The present invention adopts a data-driven multi-task communication scheduling architecture to implement the synchronization adaptation module, grid corner point generation module, projection plane calculation module, camera pixel scattered point position posture data time resampling module, camera pixel scattered point positioning calibration module, and grid clustering module; wherein the synchronization adaptation module is a task, the grid corner point generation module and the projection plane calculation module are combined into a task implementation, the camera data i pixel scattered point position posture data time resampling module and the camera data i scattered point positioning calibration module are combined into a task implementation, and the grid clustering module is a task implementation. The data-driven multi-task communication scheduling architecture adopts a system deployed by multi-core CPUs, the task input and output data are one or more data objects, and the buffer structure of the data object is a queue; the data communication interface between the tasks adopts the queue form.

[0087] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0088] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A time domain resampling grid clustering multi-task implementation method, characterized in that: include: Synchronous adaptation step: matching the acquired camera data and the imaging sampling time data attached to the position and posture data, and eliminating the mismatched data; Grid corner point generation step: Calculate the sub-satellite point position at the sampling time according to the adapted position and attitude data, calculate the coordinates of N corner points on the earth's spherical surface according to the set vertical track direction resolution and the vertical track direction field angle of all cameras, and the set vertical track direction resolution, and obtain M rows and N columns of grid corner point data; Projection plane calculation steps: cache M rows of grid corner point data and calculate the normal vector of the projection plane; The time resampling step of the camera pixel scattered point position and attitude data: according to the adapted position and attitude data and all camera data (a total of n), the position and attitude data before and after the imaging sampling time of camera data i (1≤i≤n) are used to calculate the position and attitude data of the camera data i pixel scattered point; Camera pixel scattered point positioning and calibration step: calculating the coordinates of the pixel in the ground-fixed coordinate system according to the position and posture data of the pixel scattered point i of the camera data; Grid clustering steps: Based on the M rows and N columns of grid corner point data, the projection plane normal vector and the coordinates of the pixel in the ground-fixed coordinate system, an auxiliary plane is constructed for clustering.

2. The time domain resampling grid clustering multi-task implementation method according to claim 1 is characterized in that: The position and attitude data of the pixel scatter points of the camera data i include the position of the pixel scatter points in the imaging plane, the direction of the camera optical axis relative to the satellite body, the attitude of the satellite body, the position of the satellite and the imaging sampling time.

3. The time domain resampling grid clustering multi-task implementation method according to claim 1, characterized in that: A data-driven multi-task communication scheduling architecture is used to implement the synchronous adaptation step, the grid corner point generation step, the projection plane calculation step, the camera pixel scattered point position posture data time resampling step, the camera pixel scattered point positioning calibration step, and the grid clustering step; Among them, the synchronous adaptation step is a task, the grid corner point generation step and the projection plane calculation step are combined into one task implementation, the camera data i pixel scattered point position posture data time resampling step and the camera data i scattered point positioning and calibration step are combined into one task implementation, and the grid clustering step is implemented as one task.

4. The time domain resampling grid clustering multi-task implementation method according to claim 3 is characterized in that: The data-driven multi-task communication scheduling architecture adopts a multi-core CPU deployment method, the task input and output data are one or more data objects, and the buffer structure of the data object is a queue; The data communication interface between the tasks is in the form of a queue.

5. The time domain resampling grid clustering multi-task implementation method according to claim 1, characterized in that: The grid clustering step comprises: Project the grid corner points onto the projection plane to form a grid in the projection plane. These grid corner points are called projection grid corner points, these grids are called projection grids, and the area where they are located is the projection grid area; project the pixel scattered points onto the projection plane, and these points are called projection pixel scattered points; Establish an affine auxiliary grid, the row length of the auxiliary grid is equal to the row length of the projection grid at the corresponding position near the center line of the projection grid area; the column length of the auxiliary grid is equal to the column length of the projection grid near the center position of the projection grid area or equal to the column length of the projection grid at the corresponding position near the center line of the projection grid area; Cluster the projected pixel scatter points onto an auxiliary grid; The projected pixel scatter points are corrected to the projected grid, and the corresponding pixel scatter points are clustered to the corresponding spherical grid.

6. A time domain resampling grid clustering multi-task implementation system, characterized in that: include: Synchronous adaptation module: matches the acquired camera data and the imaging sampling time data attached to the position and posture data, and removes the mismatched data; Grid corner point generation module: Calculate the sub-satellite point position at the sampling time according to the adapted position and attitude data, calculate the coordinates of N corner points on the earth's spherical surface according to the set vertical track resolution and the vertical track field angle of all cameras, and the set vertical track resolution, and obtain M rows and N columns of grid corner point data; Projection plane calculation module: cache M rows of grid corner point data and calculate the normal vector of the projection plane; Camera pixel scattered point position and attitude data time resampling module: calculates the position and attitude data of the scattered points of camera data i pixel based on the adapted position and attitude data and all camera data before and after the imaging sampling time of camera data i; Camera pixel scatter point positioning and calibration module: calculates the coordinates of the pixel in the ground-fixed coordinate system according to the position and posture data of the pixel scatter point i of the camera data; Grid clustering module: Construct auxiliary planes for clustering based on M rows and N columns of grid corner point data, projection plane normal vectors, and pixel coordinates in the ground-fixed coordinate system.

7. The time domain resampling grid clustering multi-task implementation system according to claim 6, characterized in that: The position and attitude data of the pixel scatter points of the camera data i include the position of the pixel scatter points in the imaging plane, the direction of the camera optical axis relative to the satellite body, the attitude of the satellite body, the position of the satellite and the imaging sampling time.

8. The time domain resampling grid clustering multi-task implementation system according to claim 6, characterized in that: The synchronous adaptation module, the grid corner point generation module, the projection plane calculation module, the camera pixel scattered point position and posture data time resampling module, the camera pixel scattered point positioning and calibration module, and the grid clustering module are implemented using a data-driven multi-task communication scheduling architecture; Among them, the synchronous adaptation module is a task, the grid corner point generation module and the projection plane calculation module are merged into one task implementation, the camera data i pixel scattered point position posture data time resampling module and the camera data i scattered point positioning and calibration module are merged into one task implementation, and the grid clustering module is implemented as a task.

9. The time domain resampling grid clustering multi-task implementation system according to claim 7, characterized in that: The data-driven multi-task communication scheduling architecture adopts a system deployed with multi-core CPUs, the task input and output data are one or more data objects, and the buffer structure of the data object is a queue; The data communication interface between the tasks is in the form of a queue.

10. The time domain resampling grid clustering multi-task implementation system according to claim 6, characterized in that: The grid clustering module includes: Project the grid corner points onto the projection plane to form a grid in the projection plane. These grid corner points are called projection grid corner points, these grids are called projection grids, and the area where they are located is the projection grid area; project the pixel scattered points onto the projection plane, and these points are called projection pixel scattered points; Establish an affine auxiliary grid, the row length of the auxiliary grid is equal to the row length of the projection grid at the corresponding position near the center line of the projection grid area; the column length of the auxiliary grid is equal to the column length of the projection grid near the center position of the projection grid area or equal to the column length of the projection grid at the corresponding position near the center line of the projection grid area; Cluster the projected pixel scatter points onto an auxiliary grid; The projected pixel scatter points are corrected to the projected grid, and the corresponding pixel scatter points are clustered to the corresponding spherical grid.

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