Multi-satellite global remote sensing and data transmission combined scheduling method and system based on regional division and multi-dimensional scoring mechanism

Abstract

The invention provides a multi-satellite global remote sensing and data transmission combined scheduling method and system based on region division and a multi-dimensional scoring mechanism. The method comprises the following steps: acquiring orbit data of a plurality of remote sensing satellites and preprocessing the orbit data; dividing the earth surface area into a plurality of grids, and dividing all the grids into a plurality of task areas; according to the positions of the satellite and the ground station, establishing a transmission link between the satellite and the ground station, downloading storage data in the satellite, and updating a satellite storage space; for a satellite which is not in a downloading window and has a residual storage space, obtaining a task area where a sub-satellite point of the satellite is located and an adjacent task area as candidate areas; traversing grids in the candidate area, scoring, and carrying out observation task distribution on the satellites according to scores; and finally obtaining a multi-star observation task execution and data downloading scheme. The method realizes integrated coordination of remote sensing tasks and data transmission scheduling, and can be widely applied to satellite remote sensing task management, satellite-ground integrated network scheduling and efficient remote sensing data return systems.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite observation data transmission mission planning, and relates to a multi-satellite global remote sensing and data transmission joint scheduling method and system based on regional division and multi-dimensional scoring mechanism. Background Art

[0002] With the growing demand for Earth observation, remote sensing satellites are playing an increasingly important role in areas such as agricultural monitoring, disaster assessment, environmental protection, and urban planning. Traditional remote sensing systems rely on a single satellite or a small number of satellites to complete observation missions. Their limited coverage and long response times make them unable to meet the real-world demand for large-scale, high-frequency observations.

[0003] In recent years, with the decreasing costs of satellite manufacturing and launch, and the trend toward satellite miniaturization and mass production, building large-scale remote sensing constellations has become a crucial means of enhancing remote sensing capabilities. Pushbroom imaging, a highly efficient imaging method widely used in remote sensing satellite systems, continuously acquires ground images along its trajectory during flight, offering advantages such as high precision and wide coverage. Combining pushbroom imaging with multiple satellites can effectively enhance the spatial and temporal coverage of Earth observation.

[0004] However, in practical applications, remote sensing observation mission planning involves not only calculating orbital coverage and imaging conditions, but also comprehensively considering satellite resource constraints (such as observation capabilities and data storage capacity) and ground resource constraints (such as ground station availability and bandwidth limitations). Traditional scheduling methods often separate observation scheduling from data downlink scheduling, failing to fully coordinate resource competition and priority between the two, which can easily lead to mission delays, data backlogs, or downlink congestion.

[0005] Especially in the context of collaborative observations and global data downlink from multiple remote sensing satellites, efficiently scheduling each satellite's observation activities and data transmission processes within the mission cycle has become a key issue in improving overall system utilization efficiency and mission completion rate. Therefore, a new joint scheduling method and system that can integrate the characteristics of push-broom imaging, multi-satellite orbit coverage, and ground data transmission window resources is urgently needed to achieve integrated and optimized scheduling of the entire remote sensing mission process. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a multi-satellite global remote sensing and data transmission joint scheduling method and system based on regional division and multi-dimensional scoring mechanism.

[0007] The technical solution adopted in the present invention is as follows:

[0008] A multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism includes the following steps:

[0009] Obtain orbital data of multiple remote sensing satellites and perform preprocessing;

[0010] The surface area is divided into several grids based on the satellite's push-broom imaging capabilities, and all grids are then divided into several mission areas;

[0011] According to the position of the satellite and the ground station, a transmission link between the satellite and the ground station is established to download the data stored in the satellite and update the satellite storage space;

[0012] For satellites that are not in the downlink window and have remaining storage space, the mission area where the satellite's sub-satellite point is located and its adjacent mission areas are obtained as candidate areas;

[0013] Traverse the grids in the candidate area, score each grid, and assign observation tasks to satellites based on the scores; ultimately, obtain a multi-satellite observation mission execution and data downlink plan.

[0014] Furthermore, the preprocessing is: based on the two lines of satellite orbit data, a simplified conventional perturbation model is used to predict the satellite orbit to obtain the latitude, longitude and altitude of each satellite at each moment.

[0015] Furthermore, dividing the surface area into a plurality of grids according to the push-broom imaging capability of the satellite includes:

[0016] Calculate the satellite's imaging swath width at any time based on the satellite's orbital altitude and imaging viewing angle;

[0017] The step length in the latitudinal direction is calculated according to the imaging swath width or the specified target spatial resolution, and the surface area is divided into several latitudinal bands according to the step length in the latitudinal direction;

[0018] For each latitude band, the step length in the longitude direction is calculated according to the imaging sweep width, and the surface area is divided into several grids according to the step length in the longitude direction.

[0019] Furthermore, the method of dividing all grids into a number of task areas is as follows: dividing the surface area along the latitude and longitude directions at a certain step length to obtain a number of task areas, each of which includes a number of grids.

[0020] Furthermore, the steps of establishing a transmission link between the satellite and the ground station based on the positions of the satellite and the ground station, downloading the data stored in the satellite, and updating the satellite storage space include:

[0021] The time window between the satellite and each ground station is obtained based on the position relationship. For each satellite position at each timestamp, the elevation angle between the satellite and each ground station is calculated, and the ground station corresponding to the current maximum elevation angle of the satellite is selected to establish a transmission link.

[0022] Evaluate the amount of data that can be downloaded by the satellite within the time window, download the data to the ground station, and update the satellite storage space.

[0023] Furthermore, the step of obtaining the mission area where the satellite sub-satellite point is located and its adjacent mission areas as candidate areas specifically includes:

[0024] Calculate the distance between the current satellite's sub-satellite point's geographical location and the four boundaries of the mission area where the sub-satellite point is located. Select the mission area in the direction of the boundary with the shortest distance and its two adjacent boundaries as candidate areas together with the mission area where the sub-satellite point is located.

[0025] Furthermore, the scoring of each grid specifically includes: calculating the comprehensive observation score of each grid based on the observability reference value, the spatial distance from the sub-satellite point, and the number of scheduled observations; the observability reference value is the maximum number of observable times of the grid point in an unscheduled state; the spatial distance from the sub-satellite point is the Euclidean distance between the longitude and latitude coordinates of the sub-satellite point and the center of the grid; the number of scheduled observations is the frequency with which the grid point is observed in the current scheduling.

[0026] Furthermore, the allocation of observation tasks to satellites according to scores specifically includes: selecting the grid point with the highest score, judging whether the grid point meets the constraints of field of view angle and pitch angle, and if so, selecting the grid point as the observation target of the current satellite.

[0027] Furthermore, after obtaining the multi-satellite observation mission execution and data downlink plan, the plan was simulated and verified, including coverage analysis and the formulation and verification of the high-latitude shutdown plan.

[0028] A multi-satellite global remote sensing and data transmission joint scheduling system based on regional division and multi-dimensional scoring mechanism, including:

[0029] Data processing module: used to obtain orbital data of multiple remote sensing satellites and perform preprocessing;

[0030] Region division module: used to divide the surface area into several grids based on the satellite's push-broom imaging capabilities, and then divide all the grids into several mission areas;

[0031] Data download module: used to establish a transmission link between the satellite and the ground station according to the position of the satellite and the ground station, download the data stored in the satellite, and update the satellite storage space;

[0032] Region selection module: for satellites with remaining storage space, it is used to obtain the mission area where the satellite sub-satellite point is located and its adjacent mission areas as candidate areas;

[0033] Task assignment module: used to traverse the grids in the candidate area, score each grid, and assign observation tasks to satellites based on the scores; ultimately, a multi-satellite observation task execution and data downlink plan is obtained.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] The present invention proposes a multi-satellite global remote sensing and data transmission joint scheduling method and system based on regional division and multi-dimensional scoring mechanism, which realizes the integrated coordinated scheduling of observation and data transmission, breaks through the separation scheduling bottleneck of "observation priority and data transmission lag" in traditional methods, and significantly improves the overall efficiency of remote sensing task execution and system resource utilization. In addition, the method also has a relatively efficient and simple solution process, which significantly improves the efficiency and operability of task planning, and at the same time demonstrates strong practical application capabilities.

[0036] The present invention constructs a multi-satellite multi-task candidate set and a dynamic scoring mechanism, and realizes priority scheduling and fairness control of hot spots and high-value areas based on multi-dimensional indicators such as regional priority, number of observations and distance from sub-satellite points.

[0037] The present invention can also provide simulation verification and statistical analysis capabilities, output partition coverage statistics and task allocation details, and provide users with visual feedback on task execution, facilitating strategy iteration and system evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The figure is a schematic diagram of the process design of the method in the embodiment of the present invention.

[0039] Figure 2 This is a flowchart of the task preprocessing part in an embodiment of the present invention.

[0040] Figure 3 The present invention is a flowchart of a method for screening candidate areas for remote sensing satellite observation and joint scheduling of data transmission based on area division and multi-dimensional scoring mechanism in an embodiment of the present invention.

[0041] Figure 4 Schematic diagram of the sub-satellite point distance of each partition in an embodiment of the present invention.

[0042] Figure 5 This is a statistical diagram of partition coverage in an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The technical solution of the invention will be further explained in detail below with reference to the accompanying drawings and specific examples.

[0044] The present invention provides a multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism, comprising the following steps:

[0045] The orbital data of multiple remote sensing satellites are obtained and preprocessed. The preprocessing includes: based on the two lines of orbital data of the satellite, using a simplified conventional perturbation model to predict the satellite orbit and obtain the latitude, longitude and altitude of each satellite at each moment.

[0046] Based on the satellite's push-broom imaging capabilities, field of view, orbital altitude, imaging resolution, and other parameters, the surface area is divided into several regular grids to ensure the rationality of the grid division. All grids are then divided into several mission areas. Specifically, this includes: calculating the satellite's imaging swath width at any time based on the satellite's orbital altitude and imaging angle of view; calculating the latitudinal step length based on the imaging swath width or the specified target spatial resolution, and dividing the surface area into several latitude bands in the latitudinal direction according to the step length; for each latitude band, calculating the longitudinal step length based on the imaging swath width, and dividing the longitude step length in the longitude direction to divide the surface area into several grids. The surface area is then divided along the latitude and longitude directions at a certain step length to obtain several mission areas, each of which includes several grids.

[0047] Based on the positions of the satellite and the ground station, a transmission link is established between the satellite and the ground station, the data stored in the satellite is downloaded, and the satellite storage space is updated. Specifically, this includes: obtaining the time window between the satellite and each ground station based on the position relationship; for each satellite position at each timestamp, calculating the elevation angle between the satellite and each ground station; selecting the ground station corresponding to the current maximum elevation angle of the satellite to establish a transmission link; evaluating the amount of data that can be downloaded from the satellite within the time window, downloading the data to the ground station, and updating the satellite storage space.

[0048] For satellites that are not in the downlink window and have available storage space, the mission area where the satellite's subsatellite point is located and its adjacent mission areas are obtained as candidate areas. This involves calculating the distance between the current satellite's subsatellite point's geographic location and the four boundaries of the mission area where the subsatellite point is located, and then selecting the mission area in the direction of the boundary with the shortest distance and its two adjacent boundaries as the candidate areas along with the mission area where the subsatellite point is located.

[0049] The grids within the candidate area are traversed, each scored, and observation tasks assigned to satellites based on the scores. Ultimately, a plan for executing multi-satellite observation tasks and downlinking data is obtained. Specifically, this includes calculating a comprehensive observation score for each grid based on its observability reference value, spatial distance from the sub-satellite point, and the number of scheduled observations. The observability reference value is the maximum number of times the grid point can be observed in an unscheduled state; the spatial distance from the sub-satellite point is the Euclidean distance between the sub-satellite point's longitude and latitude coordinates and the grid center; and the number of scheduled observations is the frequency with which the grid point is observed during the current schedule.

[0050] The grid point with the highest score is selected to determine whether it meets the field of view and elevation constraints. If it does, it becomes the observation target for the current satellite. After obtaining a multi-satellite observation mission execution and data downlink plan, the plan is simulated and verified, including coverage analysis and the development and verification of a high-latitude shutdown plan.

[0051] In a specific embodiment of the present invention, the multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism can be divided into three stages: task preprocessing, algorithm solution, and simulation verification. Figure 1 shown.

[0052] Mission preprocessing phase: used to obtain orbital data of multiple remote sensing satellites, build a global ground grid model, and generate feasible windows for observation and data transmission based on push-broom imaging constraints to complete mission initialization preparations.

[0053] Algorithm solving module: used to execute joint scheduling strategies under multi-objective optimization constraints, integrate regional priorities, observation constraints and resource status, and generate remote sensing task allocation plans and data downlink plans.

[0054] Simulation verification module: used to output remote sensing mission execution results and system performance indicators, statistically analyze global observation coverage, storage and data transmission utilization, and verify and evaluate the joint scheduling effect.

[0055] The following is an introduction to the specific steps of this implementation:

[0056] The specific process of task preprocessing is as follows Figure 2 shown.

[0057] First, according to the demand, the task is initiated from t s The initial 300 satellites S = {s1,s2,...,s b} Where b represents the number of satellites. The current design is 300, and thousands of ground station antennas U = {u1,u2,...,u n}Global satellite observation data transmission integrated mission planning with a cycle of two hours.

[0058] Among them, S represents the set of all satellites in orbit. The attributes of a single satellite are as follows: s1 = {SatId, SatTLE, used, capacity, rate, downQueue}, where SatId represents the satellite number, SatTLE represents the number of satellite roots, used represents the amount of storage data currently used, capacity represents the total storage space of the satellite, rate represents the downlink rate, and downQueue stores the grid observed by the current satellite. U represents the set of all available ground stations. The current attributes of a single ground station are the following 3-tuple: in Indicates the status of all antennas of the current ground station at the current moment, P u Indicates the current geographical location of the ground station, represents the number of idle antennas of the ground station at the current moment, u∈U and s∈S represent a ground station and satellite respectively.

[0059] Then, based on the SGP4 model orbit prediction algorithm of the satellite TLE, by inputting two lines of orbit data for each satellite, the SGP4 model is used to predict the satellite orbit and obtain the latitude, longitude and altitude of each satellite at each moment:

[0060]

[0061] in They represent the longitude, latitude and altitude of the current satellite at time t respectively.

[0062] The orbit prediction algorithm, based on the Simplified General Perturbations 4 (SGP4) model using two-line orbital element (TLE) data, can accurately predict the orbits of low-orbiting satellites. Developed by NORAD, this model is particularly suitable for near-Earth objects with orbital periods less than 225 minutes. SGP4 accounts for perturbations such as the Earth's non-spherical gravity, the gravitational pull of the sun and moon, solar radiation pressure, and atmospheric drag. By inputting the satellite's TLE data, the SGP4 model can efficiently calculate the satellite's latitude, longitude, and altitude at each moment, significantly improving the accuracy of orbit predictions.

[0063] Through the conversion of orbital parameters, the basic information of the satellite is: s1 = {SatId, P1, used, capacity, rate, downQueue}, where P1 represents the longitude, latitude and altitude of the satellite at each moment in the planning period.

[0064] Dividing the ground into grid areas according to the input satellite orbit altitude and camera imaging angle is helpful for push-broom observations.

[0065] This method first calculates the imaging swath width of the satellite at any point in time based on the satellite's orbital altitude and imaging field of view. The calculation formula for the imaging width W is as follows:

[0066]

[0067] Where H is the satellite orbit altitude (in kilometers), θ is the imaging field of view angle (in degrees), and tan(·) represents the tangent function. The angle must be converted to radians.

[0068] In order to divide the earth's surface into a regular grid suitable for remote sensing capabilities, the system divides the earth's surface in the latitudinal direction according to a step size △φ, which is calculated as follows:

[0069] If the user specifies the target spatial resolution R (unit: km), then:

[0070]

[0071] Where K = 111, is the surface distance corresponding to a unit of latitude (km / °). If R is not specified, the system automatically determines it based on the sweep width W:

[0072]

[0073] For the longitude direction, since the higher the latitude on the earth's surface, the denser the meridians, the system independently calculates the longitude step length △λ(φ) in each latitude band. Its expression is:

[0074]

[0075] Where: φ is the current latitude, cos(φ) is the cosine term of the latitude, which is used to correct the actual reduction in surface distance caused by the contraction of longitudes at high latitudes.

[0076] Based on the above division method, within the longitude interval [-180°, 180°] and the latitude interval [-90°, 90°], a grid rectangular area is generated with a step size of (△φ, △λ(φ)). Each grid is represented by its lower left corner coordinate (φ i ,λ j ) and step size define its rectangular vertex set g i,j for:

[0077] g i,j ={(φ i ,λ j ),(φ i ,λ j +△λ),(φ i +△φ,λ j +△λ),(φ i +△φ,λ j )}

[0078] The global ground grid generated by this method can adaptively adjust the resolution according to different orbital altitudes and imaging capabilities, and supports regional visibility analysis and task allocation on a grid basis. Each grid area has a fixed spatial coverage range, which facilitates regional assignment and resource planning in remote sensing task scheduling.

[0079] Then, the time window between the satellite and each ground station is obtained based on the position of the satellite and the ground station. For each satellite position at each timestamp, its position vector relative to each ground station is calculated, and the elevation angle is calculated using the following formula

[0080]

[0081] Among them, P s represents the satellite position vector, P u Represents the ground station position vector. Each satellite selects the ground station antenna with the largest elevation angle to establish a link with the current satellite every second.

[0082] Then came the algorithm solution phase, and proposed a remote sensing satellite observation candidate area screening and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism. The process is as follows Figure 3 shown.

[0083] The system allocates an independent storage resource management module to each satellite and continuously tracks its storage usage status during its on-orbit operation.

[0084] Each time a satellite performs a remote sensing observation, the system estimates the data volume generated by the observation based on the duration of the imaging mission and remote sensing parameters (such as bandwidth and resolution), denoted as ΔV. This data volume is immediately added to the satellite's corresponding "used" storage field, enabling dynamic measurement and updating of satellite storage space.

[0085] Before each task scheduling, the system compares the current used storage with the capacity limit. If used ≥ capacity, the satellite storage is considered full and no new remote sensing imaging tasks will be accepted until observation data is successfully transmitted through the downlink channel and storage space is released.

[0086] The system continuously monitors whether the downlink window is open (i.e., the satellite is within the ground station's visual range). Once a matching downlink slot exists, the system automatically estimates the amount of data that can be downlinked within this window (max_downlink = rate × duration) and selects a corresponding number of observation tasks from the downlink queue to perform the downlink operation. All downlink tasks will record the observation area to which the data belongs, mark the start and end times, and be removed from the downlink queue upon completion, with the storage usage amount used updated accordingly.

[0087] After each successful observation, the observation record (including the observation time and region identifier) ​​is written to the satellite's downQueue, forming a pool of tasks to be downloaded. This pool has a first-in-first-out (FIFO) queue feature, ensuring that the download order is consistent with the observation order, preventing data from being invalidated due to being held for too long.

[0088] Through the above-mentioned dynamic storage tracking and management mechanism, the system can prevent data from being lost due to storage overflow. At the same time, the satellite will not be scheduled to perform tasks that exceed its current storage capacity. The cache of data transmission tasks will be cleared in time to create conditions for subsequent tasks, thereby improving the continuity and stability of task scheduling.

[0089] In order to improve the accuracy and fairness of satellite Earth observation scheduling and reduce the search time of satellite visible areas, this paper proposes a method for screening candidate observation areas based on spatial region division. This method first divides the earth's surface into regular latitude and longitude mission areas, and uses a latitude step size to select candidate observation areas. The task area is divided into 18×18 task areas, each containing several grids. Each area can be uniquely identified by its area code (m,n), where:

[0090]

[0091] During the scheduling process, the current geographical location of the satellite's sub-satellite point is calculated in real time And locate the distance between the subsatellite point position and the four boundaries (upper, lower, left, and right) of the mission area to which it belongs, which are recorded as:

[0092]

[0093] d left =l s -l min , d right =l max -l s

[0094] in Indicates the maximum latitude of the current area, Indicates the minimum latitude of the current area, l min Indicates the minimum longitude of the current area, l max , indicating the maximum longitude of the current area.

[0095] According to the relative size of these distances, it is determined which boundary the sub-satellite point is closer to, and candidate areas are selected from that direction and two adjacent directions. Together with the mission area where the sub-satellite point is located, there are a total of 4 candidate areas, such as Figure 4 As shown, this method can cope with the visibility loss caused by boundary effects.

[0096] Then, within these candidate areas, the grids contained therein are traversed, and the comprehensive observation score (Score) of each grid is calculated based on the observability reference value, the spatial distance from the sub-satellite point, and the number of scheduled observations: the observability reference value refers to the theoretical maximum number of observable times of the grid in the unscheduled state. The lower the value, the less favorable its geographical location is for observation, and the higher the priority should be for scheduling; the spatial distance from the sub-satellite point refers to the Euclidean distance between the latitude and longitude coordinates of the sub-satellite point and the center of the grid; the number of scheduled observations refers to the frequency of observation of the grid in the current scheduling. The higher the frequency, the lower its priority should be.

[0097] In order to ensure that all hot spots are covered except for areas that cannot be reached by orbit such as the South Pole and the North Pole, a balanced optimization of the three factors of sub-satellite point distance, regional priority and number of real-time observations is achieved. At the same time, after selecting the optimal target area, the satellite observation field of view angle and the ground station pitch angle are constrained and judged to select the optimal observation network grid.

[0098] The score for each grid is calculated as follows:

[0099]

[0100] Among them, Score is the grid score, priority is the static observability of the grid, dist represents the geographical distance between the current sub-satellite point and the grid center, obs is the number of times the grid has been observed during the scheduling process, A, B, and C are the weight coefficients of the above three factors respectively, and ε is a small constant to avoid division by zero errors.

[0101] Finally, the grid with the highest score is selected to determine whether it meets the satellite field of view angle and pitch angle constraints. The calculation formula is as follows:

[0102]

[0103] Where ψ is the off-axis angle, and calculate whether it meets the satellite field of view angle constraint. FOV stands for field of view, P g Represents the coordinates of the grid center point,

[0104]

[0105] in Is the off-axis angle, determine whether the pitch angle constraint is met This embodiment sets

[0106] If the conditions are met, they are used as the observation targets at the current moment. This method not only ensures that boundary areas are not missed in the visibility space, but also improves the scheduling balance and priority recognition capabilities through the dynamic integration of the three-dimensional scoring system, effectively improving the utilization efficiency of Earth observation resources.

[0107] Next, the constellation ground station feeder link allocation scheme is simulated and verified, including coverage analysis and the formulation and verification of high-latitude shutdown plans.

[0108] The specific steps for global coverage analysis are:

[0109] The global coverage ratio CoverageRatio is calculated based on the actual number of observed grids Covered and the total number of grids Total, which is defined as:

[0110]

[0111] At the same time, we analyzed the coverage of each area, such as Figure 5 As shown, the steps are as follows:

[0112] For the input geographical area, 13 regions A a The boundary data (represented by polygons or composite polygons) is uniformly converted to the [0°, 360°] coordinate system to avoid spatial calculation errors caused by crossing longitude ±180°. The conversion function is:

[0113]

[0114] Traverse all ground grids g i,j , for each region A a , use spatial intersection operation to identify the grid set G that intersects with the area a ,Right now:

[0115] G a ={g|geometry(g)∩geometry(A a )}

[0116] Then, according to the planning results, the current a All grids in , the set of grids observed in planning C a .

[0117] For each area A a , calculate its observation coverage R a , defined as follows:

[0118]

[0119] This method can quickly complete the fusion analysis of spatial matching and scheduling results for any designated geographical area. On the basis of ensuring geometric accuracy, it supports parallel analysis of multiple regions around the world, providing data support for remote sensing mission effect evaluation and hot spot area coverage balance analysis.

[0120] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0122] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0123] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0124] The above description is only a preferred embodiment of the present invention. Although the present invention has been disclosed as a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can use the above disclosed methods and technical contents to make many possible changes and modifications to the technical solution of the present invention without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment with equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of protection of the technical solution of the present invention.

Claims

1. A multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism, characterized in that: The following steps are involved: Obtain orbital data of multiple remote sensing satellites and perform preprocessing; The surface area is divided into several grids based on the satellite's push-broom imaging capabilities, and all grids are then divided into several mission areas; According to the position of the satellite and the ground station, a transmission link between the satellite and the ground station is established to download the data stored in the satellite and update the satellite storage space; For satellites that are not in the downlink window and have remaining storage space, the mission area where the satellite's sub-satellite point is located and its adjacent mission areas are obtained as candidate areas; Traverse the grids in the candidate area, score each grid, and assign observation tasks to satellites based on the scores; Finally, a plan for executing multi-satellite observation missions and transmitting data was obtained.

2. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: The preprocessing is: based on two lines of satellite orbit data, using a simplified conventional perturbation model to predict the satellite orbit, and obtain the latitude, longitude and altitude of each satellite at each moment.

3. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: The method of dividing the surface area into a plurality of grids according to the push-broom imaging capability of the satellite includes: Calculate the satellite's imaging swath width at any time based on the satellite's orbital altitude and imaging viewing angle; The step length in the latitudinal direction is calculated according to the imaging swath width or the specified target spatial resolution, and the surface area is divided into several latitudinal bands according to the step length in the latitudinal direction; For each latitude band, the step length in the longitude direction is calculated according to the imaging sweep width, and the surface area is divided into several grids according to the step length in the longitude direction.

4. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: The method of dividing all grids into a number of task areas is as follows: dividing the surface area along the latitude and longitude directions at a certain step length to obtain a number of task areas, each of which includes a number of grids.

5. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: The method of establishing a transmission link between the satellite and the ground station according to the positions of the satellite and the ground station, downloading the data stored in the satellite, and updating the satellite storage space includes the following specific steps: The time window between the satellite and each ground station is obtained based on the position relationship. For each satellite position at each timestamp, the elevation angle between the satellite and each ground station is calculated, and the ground station corresponding to the current maximum elevation angle of the satellite is selected to establish a transmission link. Evaluate the amount of data that can be downloaded by the satellite within the time window, download the data to the ground station, and update the satellite storage space.

6. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: The specific steps of obtaining the mission area where the satellite sub-satellite point is located and its adjacent mission areas as candidate areas include: Calculate the distance between the current satellite's sub-satellite point's geographical location and the four boundaries of the mission area where the sub-satellite point is located. Select the mission area in the direction of the boundary with the shortest distance and its two adjacent boundaries as candidate areas together with the mission area where the sub-satellite point is located.

7. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: The scoring of each grid specifically includes: calculating the comprehensive observation score of each grid based on the observability reference value, the spatial distance from the sub-satellite point, and the number of scheduled observations; the observability reference value is the maximum number of observable times of the grid point in the unscheduled state; the spatial distance from the sub-satellite point is the Euclidean distance between the longitude and latitude coordinates of the sub-satellite point and the center of the grid; the number of scheduled observations is the frequency with which the grid point is observed in the current scheduling.

8. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: The allocation of observation tasks to satellites according to scores specifically includes: selecting the grid point with the highest score, judging whether the grid point meets the constraints of the field of view angle and the pitch angle, and if so, taking the grid point as the observation target of the current satellite.

9. The multi-satellite global remote sensing and data transmission joint scheduling method based on regional division and multi-dimensional scoring mechanism according to claim 1 is characterized in that: After obtaining the multi-satellite observation mission execution and data downlink plan, the plan was simulated and verified, including coverage analysis and the formulation and verification of the high-latitude shutdown plan.

10. A multi-satellite global remote sensing and data transmission joint scheduling system based on regional division and multi-dimensional scoring mechanism, characterized by: include: Data processing module: used to obtain orbital data of multiple remote sensing satellites and perform preprocessing; Region division module: used to divide the surface area into several grids based on the satellite's push-broom imaging capabilities, and then divide all the grids into several mission areas; Data download module: used to establish a transmission link between the satellite and the ground station according to the position of the satellite and the ground station, download the data stored in the satellite, and update the satellite storage space; Region selection module: for satellites with remaining storage space, it is used to obtain the mission area where the satellite sub-satellite point is located and its adjacent mission areas as candidate areas; Task assignment module: used to traverse the grids in the candidate area, score each grid, and assign observation tasks to satellites based on the scores; Finally, a plan for executing multi-satellite observation missions and transmitting data was obtained.

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