A remote sensing method and system based on a low earth orbit satellite network

Abstract

The application provides a remote sensing method and system based on a low-orbit satellite network, wherein the low-orbit satellite network system is composed of a remote sensing satellite, a communication satellite and a computing satellite; the remote sensing satellite and the computing satellite perform relay communication through the communication satellite, and the remote sensing data is calculated by the computing satellite; constraints are established based on the data acquisition capability of the remote sensing satellite to ensure that the remote sensing task is effectively executed; constraints are established in the data transmission process to ensure that the transmission rate occupied by the remote sensing data transmission does not exceed the maximum transmission rate of the inter-satellite link; constraints are established in the data calculation to ensure that the cumulative calculation resources occupied by the remote sensing data processing do not exceed the maximum calculation capability of the computing satellite; under each constraint condition, a maximum remote sensing task execution quantity problem model is established, the problem model is decomposed into a flow scheduling sub-problem and a remote sensing scheduling sub-problem, the complexity of problem solving is reduced, and the task scheduling efficiency and completion rate are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of remote sensing methods, and in particular to a remote sensing method and system based on a low-orbit satellite network. BACKGROUND

[0002] In recent years, remote sensing satellites have been widely used in environmental monitoring, emergency detection and rescue, such as monitoring forest fires, earthquakes, floods and other natural disasters, and a large amount of collected data needs to be transmitted back to the ground to provide real-time information and reduce life and property losses. However, due to the limited visibility time between the satellite and the ground station, there are serious challenges in transmitting a large amount of remote sensing data to the ground.

[0003] Existing remote sensing data transmission methods are mainly divided into three categories: transmission through ground stations, relay through geosynchronous orbit satellites (GEO), and relay through low-orbit satellites (LEO). Most remote sensing scheduling schemes consider remote sensing task scheduling and data transmission separately, without coordinating remote sensing, transmission, and computing in the network, resulting in insufficient utilization of network resources and limited performance improvement. Existing remote sensing data computing offloading schemes mainly focus on the feasibility analysis of remote sensing data on-board offloading, system architecture design, and target-based optimal offloading strategy. Remote sensing data on-board offloading system architecture design aims to provide a multi-layer computing offloading framework, in which satellites can participate in data offloading as relays or computing nodes, but lacks optimization and scheduling of various resources in the satellite network. Target-based optimal offloading strategy mainly optimizes data offloading strategy for single or multiple targets such as delay, energy consumption, resource utilization, and offloading efficiency to achieve optimal scheduling. However, these works mainly focus on computing offloading without considering available remote sensing and transmission resources, and do not achieve overall optimal configuration. SUMMARY

[0004] In view of this, the embodiments of the present application provide a remote sensing method and system based on a low-orbit satellite network to eliminate or improve one or more defects in the prior art, solving the problem that the low-orbit satellite network in the prior art cannot achieve task number maximization and optimal resource allocation when allocating remote sensing, communication, and computing resources to differentiated remote sensing tasks.

[0005] One aspect of the present application provides a remote sensing method based on a low-orbit satellite network, which is used to run on a low-orbit satellite network, the low-orbit satellite network comprising one or more remote sensing satellites, communication satellites, and computing satellites, the topological relationship between the remote sensing satellites, the communication satellites, and the computing satellites remaining unchanged within a single time slot, the method comprising the following steps:

[0006] Construct a remote sensing model and establish constraints so that after each remote sensing task is assigned to a designated remote sensing satellite, each remote sensing satellite can complete data acquisition of the target area of ​​one or more assigned remote sensing tasks within its data sampling capability range;

[0007] A transmission model is constructed, and constraints are established to ensure that during the relay communication between the remote sensing satellite and the computing satellite through the communication satellite, the transmission rate occupied by the inter-satellite link does not exceed its maximum transmission rate.

[0008] A computational model is constructed, and constraints are established to ensure that the cumulative computational resources used by the computational satellite when performing the assigned remote sensing tasks do not exceed its computational capacity;

[0009] Construct a problem model, and based on the remote sensing model, the transmission model, and the computation model, maximize the total number of remote sensing tasks performed by the low-Earth orbit satellite network;

[0010] The problem model is decomposed into a traffic scheduling subproblem and a remote sensing scheduling subproblem. The traffic scheduling subproblem is used to determine the maximum amount of data that each remote sensing satellite in the low-orbit satellite network can transmit. The traffic scheduling subproblem is solved based on a first predetermined scheme. The remote sensing scheduling subproblem is solved based on the solution of the traffic scheduling subproblem and the available remote sensing resources of the remote sensing satellites to schedule the remote sensing mission. The remote sensing scheduling subproblem is solved based on a second predetermined scheme.

[0011] In some embodiments, each remote sensing satellite is capable of acquiring data from the target area of ​​one or more assigned remote sensing missions within its data sampling capability range, including:

[0012] A constraint is established to make the trajectory of the remote sensing satellite projected onto the Earth's surface approach or cover the target area, expressed as:

[0013] ;

[0014] ;

[0015] in, Indicates satellite In the time slot latitude at that location Indicates satellite In the time slot Longitude of the location Indicates the latitude of the target area. Indicates the longitude of the target area. This indicates the maximum rotation angle of the satellite remote sensing camera. Represents the Earth's radius. Indicates the satellite's orbital altitude, [ , [Indicates from time slot] Time slot A continuous period of time;

[0016] And establish constraints to ensure that the cumulative data collection rate of the remote sensing mission allocated to each remote sensing satellite does not exceed the maximum data acquisition rate of the corresponding remote sensing satellite, expressed as:

[0017] ;

[0018] in, This indicates the variables that identify remote sensing scheduling decisions. A value of 1 indicates the remote sensing task. In the time slot The remote sensing satellite Observation, A value of 0 indicates a remote sensing task. In the time slot Not by the aforementioned remote sensing satellite Observation, This indicates the data acquisition rate required to observe remotely sensed targets. Indicates remote sensing satellite In the time slot Available data collection rate This represents the set of remote sensing satellites. Represents the set of all time slots. This indicates the number of real-time remote sensing tasks that the low-orbit satellite network system needs to perform.

[0019] In some embodiments, establishing constraints to ensure that the inter-satellite link transmission rate used during relay communication between the remote sensing satellite and the computing satellite via the communication satellite does not exceed its maximum transmission rate includes:

[0020] Calculate the maximum transmission rate of inter-satellite links using Shannon's theorem. The expression is:

[0021] ;

[0022] ;

[0023] in, This indicates the available bandwidth of the inter-satellite link. Indicates the satellite Transmission power, Indicates the satellite The transmit antenna gain at that location, Indicates the satellite The receiving antenna gain at that location, Indicates noise power. Indicates free space loss. Represents the speed of light. Indicates the center frequency of the carrier. Indicates the satellite and The distance between them.

[0024] And the transmission rate used by each task transmitted from the communication satellite to the computing satellite within each time slot. It should not exceed the maximum transmission rate of the corresponding inter-satellite link, expressed as:

[0025] ;

[0026] in, This indicates the number of real-time remote sensing tasks that the low-Earth orbit satellite network system needs to perform. Indicates all inter-satellite links. Represents the set of all time slots. Indicates satellite and Inter-satellite links between them This indicates the maximum transmission rate of the inter-satellite link.

[0027] In some embodiments, a constraint is established to ensure that the computing satellite, when performing its assigned remote sensing tasks, does not accumulate computing resources exceeding its computing capacity, as expressed by:

[0028] ;

[0029] in, Indicates in time slot Computing satellites Available computing resources This represents the computing resources required by the computing satellite s to compute the remote sensing mission m, where T is the set of all time slots. This indicates the number of real-time remote sensing tasks that the low-orbit satellite network system needs to perform.

[0030] In some embodiments, considering the constraints of the remote sensing model, the transmission model, and the computational model, the expression of the problem model is:

[0031]

[0032] in, To define variables, where A value of 1 indicates a remote sensing satellite. Assigned to task , A value of 0 indicates a remote sensing satellite. Not assigned to a task , This indicates the number of real-time remote sensing tasks that the low-Earth orbit satellite network system needs to perform. Represents a collection of remote sensing satellites. This represents the set of satellites being computed. This indicates the transmission rate used by each task. This indicates the variables that identify remote sensing scheduling decisions. A value of 1 indicates the remote sensing task. In the time slot Remote sensing satellite Observation, A value of 0 indicates the remote sensing task. In the time slot Not detected by remote sensing satellites Observation, Indicates all inter-satellite links. Indicates satellite and Inter-satellite links between them It is the data acquisition rate required for observing remotely sensed targets. Indicates calculation satellite Calculate the computational resources required for remote sensing task m. This indicates the CPU cycles required to process one unit of remote sensing data.

[0033] In some embodiments, the traffic scheduling subproblem is used to determine the maximum amount of data that each remote sensing satellite in the low-Earth orbit satellite network can transmit, and the expression is:

[0034]

[0035] in, Indicates satellite In the time slot Towards The amount of data transmitted Indicates all inter-satellite links. Represents the set of all time slots. The remote sensing satellite In the time slot Available data collection rate Indicates the length of each time slot. Represents a collection of remote sensing satellites. This represents the set of satellites being computed. Indicates satellite and Inter-satellite links between them This indicates the maximum transmission rate of the inter-satellite link. Indicates in time slot Computing satellites Available computing resources This indicates the CPU cycles required to process one unit of remote sensing data.

[0036] In some embodiments, the method transforms the flow scheduling subproblem into a maximum flow problem and solves it using the standard maximum flow algorithm. The specific steps are as follows:

[0037] Construct a system topology diagram based on a low-Earth orbit satellite network model;

[0038] Initialize the residual network in each time slot, use the breadth-first search algorithm to search for augmenting paths from the virtual source node to the virtual destination node, and calculate the feasible flow of the augmenting path;

[0039] The remaining network is updated based on the feasible flow until no available augmentation path can be found, thus obtaining the maximum transmissible flow of the entire network in that time slot.

[0040] In some embodiments, the expression for the remote sensing scheduling subproblem is:

[0041]

[0042] in, To define variables, where A value of 1 indicates a remote sensing satellite. Assigned to task ,in A value of 0 indicates a remote sensing satellite. Not assigned to a task , Represents a remote sensing sequence, where Indicates task Requires time slots To observe the remote sensing targets specified in the remote sensing mission, and Indicates task No time slot required To observe the remote sensing targets specified in the remote sensing mission. Indicates remote sensing satellite For the task The provided remote sensing window, Represents a collection of remote sensing satellites. Represents the set of all time slots. This indicates the number of real-time remote sensing tasks that the low-Earth orbit satellite network system needs to perform. Indicates task In the time slot The amount of data generated Indicates remote sensing satellite In the time slot The amount of remote sensing data that can be transmitted.

[0043] In some embodiments, the remote sensing scheduling subproblem is solved using the CPLEX solver.

[0044] On the other hand, the present invention also provides a remote sensing low-Earth orbit satellite system, the low-Earth orbit satellite system comprising: one or more remote sensing satellites, communication satellites and computing satellites, the low-Earth orbit satellite system performing the steps of the remote sensing method based on the low-Earth orbit satellite network described above.

[0045] The beneficial effects of the present invention are at least as follows:

[0046] The remote sensing method and system based on a low-Earth orbit (LEO) satellite network described in this invention comprises remote sensing satellites, communication satellites, and computing satellites. The remote sensing satellites collect data on the target areas of their assigned remote sensing tasks. The remote sensing satellites and computing satellites communicate via relay satellites. The computing satellites perform calculations on the allocated remote sensing data. Constraints are established based on the data acquisition capabilities of the remote sensing satellites to ensure the effective execution of remote sensing tasks. Constraints are also established during data transmission to ensure that the transmission rate occupied by inter-satellite links does not exceed their maximum transmission rate. Constraints are established during data calculation to ensure that the cumulative computing resources occupied by the computing satellites do not exceed their computing capacity. This invention comprehensively considers the remote sensing, communication, and computing resources in the satellite network, achieving optimal resource allocation when allocating remote sensing, communication, and computing resources to differentiated remote sensing tasks in the LEO satellite network, thereby maximizing the number of remote sensing tasks executed.

[0047] Furthermore, this invention designs a resource-aware scheduling algorithm that decomposes the joint scheduling problem of remote sensing, communication, and computing resources into a flow scheduling subproblem and a remote sensing scheduling subproblem. The flow scheduling subproblem determines the maximum amount of data that the remote sensing satellite can transmit based on the data acquisition rate of the remote sensing satellite, the transmission rate of the communication satellite, and the computing power of the computing satellite. The remote sensing scheduling subproblem optimizes the allocation of remote sensing tasks based on the maximum amount of data that the remote sensing satellite can transmit and the remote sensing resources, thereby maximizing the number of remote sensing tasks executed. This algorithm reduces the complexity of solving the original problem. The flow scheduling subproblem and the remote sensing scheduling subproblem are solved using the maximum flow algorithm and an integer programming solver, respectively.

[0048] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the specification and drawings.

[0049] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0050] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. In the drawings:

[0051] Figure 1 This is a flowchart illustrating a remote sensing method for a low-Earth orbit satellite network according to an embodiment of the present invention.

[0052] Figure 2 This is a schematic diagram of the process of solving the flow scheduling subproblem using the standard maximum flow algorithm according to an embodiment of the present invention.

[0053] Figure 3 This is a remote sensing system architecture diagram of a low-orbit satellite network according to an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0055] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0056] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0057] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0058] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0059] In existing technologies, low-Earth orbit (LEO) satellite communication systems can provide low-latency and high-throughput data transmission by relaying data to ground stations via multi-hop inter-satellite links. However, the transmission capacity of this communication system is still limited by the availability and capacity of the satellite-to-ground links. Most remote sensing scheduling schemes do not coordinate remote sensing, transmission, and computing resources in the network, resulting in insufficient utilization of network resources. This invention proposes a multi-dimensional resource scheduling method for LEO satellite networks by jointly scheduling remote sensing, transmission, and computing resources. At the same time, this invention also designs a resource-aware scheduling algorithm, which decomposes the joint scheduling problem of remote sensing, communication, and computing resources into a flow scheduling subproblem and a remote sensing scheduling subproblem, and solves them using the maximum flow algorithm and an integer programming solver, which greatly reduces the complexity of solving the original problem.

[0060] Figure 1 This is a flowchart illustrating a remote sensing method using a low-Earth orbit (LEO) satellite network according to an embodiment of the present invention. Specifically, this application provides a remote sensing method based on a LEO satellite network. The method is used to operate on a LEO satellite network, which includes one or more remote sensing satellites, communication satellites, and computing satellites. The method includes the following steps S101-S105:

[0061] Step S101: Construct a remote sensing model and establish constraints so that after each remote sensing task is assigned to a designated remote sensing satellite, each remote sensing satellite can complete data acquisition of the target area of ​​one or more assigned remote sensing tasks within its data sampling capability range.

[0062] Step S102: Construct a transmission model and establish constraints to ensure that the inter-satellite link transmission rate occupied during the relay communication between remote sensing satellites and computing satellites via communication satellites does not exceed their maximum transmission rate.

[0063] Step S103: Construct a computational model and establish constraints to ensure that the cumulative computational resources used by the computing satellite when performing the assigned remote sensing tasks do not exceed its computational capacity.

[0064] Step S104: Construct a problem model based on the remote sensing model, transmission model, and computation model to maximize the total number of remote sensing missions performed by the low-Earth orbit satellite network.

[0065] Step S105: Decompose the problem model into a traffic scheduling subproblem and a remote sensing scheduling subproblem. The traffic scheduling subproblem is solved based on the first setting scheme. The remote sensing scheduling subproblem is solved based on the solution of the traffic scheduling subproblem and the available remote sensing resources of the remote sensing satellite to schedule remote sensing missions. The remote sensing scheduling subproblem is solved based on the second setting scheme.

[0066] In step S101, the remote sensing satellite's ability to observe the mission depends on the time it can observe the remote sensing target specified by the mission and its data acquisition rate. The time period during which the remote sensing satellite can observe the remote sensing target specified by the mission is called the remote sensing window. When the trajectory of the satellite projected onto the Earth's surface is close to the remote sensing target specified by the mission, the satellite has an available remote sensing window for the remote sensing mission.

[0067] Each remote sensing satellite requires a continuous remote sensing period to conduct ongoing observations for the mission, that is...

[0068] ;

[0069] ;

[0070] in This indicates the variables that identify remote sensing scheduling decisions. A value of 1 indicates a remote sensing task. In the time slot Remote sensing satellite Observation, A value of 0 indicates a remote sensing task. In the time slot Not detected by remote sensing satellites Observation, As a remote sensing window, it represents the time slot Time slot A continuous period of time, Indicates the time when the observation began. Indicates the duration of the observation.

[0071] In step S102, relay communication extends the communication distance and maintains good communication quality by setting up one or more relay stations between two terminal stations to amplify, shape, and convert the signal carrier frequency; inter-satellite link refers to a data transmission and communication link between satellites, which can realize information transmission and exchange between satellites. Through inter-satellite link, multiple satellites can be interconnected to form a satellite communication network, thereby improving communication efficiency and coverage.

[0072] In step S104, a problem model is constructed based on the remote sensing model, transmission model, and computation model. To maximize the number of remote sensing tasks executed by the low-Earth orbit satellite network under the constraints of differentiated remote sensing task requirements, satellite remote sensing capabilities, transmission capabilities, and computation capabilities, it is necessary to jointly schedule remote sensing satellites, communication satellites, and computing satellites. The remote sensing satellites allocate sufficient data transmission rates to each remote sensing task in each time slot to ensure the normal transmission of remote sensing data, and the computing satellites allocate sufficient computing resources to process the received remote sensing task data, ensuring that all remote sensing tasks observed by the remote sensing satellites can be successfully processed.

[0073] In step S105, the flow scheduling subproblem is solved using the maximum flow algorithm, which includes: Edmunds Kapp's algorithm, push-relabeling algorithm, and electric flow algorithm;

[0074] The remote sensing scheduling subproblem is solved using integer linear programming, which is an extension of linear programming. Under the premise of satisfying the constraints, it seeks integer solutions to make the objective function optimal.

[0075] In some embodiments, each remote sensing satellite is capable of acquiring data from the target area of ​​one or more assigned remote sensing missions within its data sampling capability range, including:

[0076] To establish constraints so that the trajectory of the remote sensing satellite projected onto the Earth's surface approaches or covers the target area, the expression is:

[0077] ;

[0078] ;

[0079] in, Indicates satellite In the time slot latitude at that location Indicates satellite In the time slot Longitude of the location Indicates the latitude of the target area. Indicates the longitude of the target area. This indicates the maximum rotation angle of the satellite remote sensing camera. Represents the Earth's radius. Indicates the satellite's orbital altitude, [ , [Indicates from time slot] Time slot A continuous period of time;

[0080] And establish constraints to ensure that the cumulative data collection rate of the remote sensing missions allocated to each remote sensing satellite does not exceed the maximum data acquisition rate of the corresponding remote sensing satellite, expressed as:

[0081] ;

[0082] in, This indicates the variables that identify remote sensing scheduling decisions. A value of 1 indicates a remote sensing task. In the time slot Remote sensing satellite Observation, A value of 0 indicates a remote sensing task. In the time slot Not detected by remote sensing satellites Observation, This indicates the data acquisition rate required to observe remotely sensed targets. Indicates remote sensing satellite In the time slot Available data collection rate Represents a collection of remote sensing satellites. Represents the set of all time slots. This indicates the number of real-time remote sensing tasks that a low-Earth orbit satellite network system needs to perform.

[0083] In some embodiments, constraints are established to ensure that during relay communication between remote sensing satellites and computing satellites via communication satellites, the transmission rate occupied by the inter-satellite link does not exceed its maximum transmission rate, including:

[0084] Calculate the maximum transmission rate of inter-satellite links using Shannon's theorem. The expression is:

[0085] ;

[0086] ;

[0087] in, This indicates the available bandwidth of the inter-satellite link. Indicates satellite Transmission power, Indicates satellite The transmit antenna gain at that location, Indicates satellite The receiving antenna gain at that location, Indicates noise power. Indicates free space loss. Represents the speed of light. Indicates the center frequency of the carrier. Indicates satellite and The distance between them.

[0088] And within each time slot, the transmission rate used by each task transmitted from the communication satellite to the computing satellite. It should not exceed the maximum transmission rate of the corresponding inter-satellite link, expressed as:

[0089] ;

[0090] in, This indicates the number of real-time remote sensing tasks that a low-Earth orbit satellite network system needs to perform. Indicates all inter-satellite links. Represents the set of all time slots. Indicates satellite and Inter-satellite links between them This indicates the maximum transmission rate of the inter-satellite link.

[0091] In some embodiments, a constraint is established to ensure that the cumulative computing resources used by a computing satellite in performing its assigned remote sensing tasks do not exceed its computing capacity, expressed as:

[0092] ;

[0093] in, Indicates in time slot Computing satellites Available computing resources Let T represent the computational resources required to compute remote sensing mission m from satellite s, where T is the set of all time slots. This indicates the number of real-time remote sensing tasks that a low-Earth orbit satellite network system needs to perform.

[0094] In some embodiments, considering the constraints of the remote sensing model, the transmission model, and the computational model, the expression of the problem model is as follows:

[0095]

[0096] in, To define variables, where A value of 1 indicates a remote sensing satellite. Assigned to task , A value of 0 indicates a remote sensing satellite. Not assigned to a task , This indicates the number of real-time remote sensing tasks that a low-Earth orbit satellite network system needs to perform. Represents a collection of remote sensing satellites. This represents the set of satellites being computed. This indicates the transmission rate used by each task. This indicates the variables that identify remote sensing scheduling decisions. A value of 1 indicates a remote sensing task. In the time slot Remote sensing satellite Observation, A value of 0 indicates a remote sensing task. In the time slot Not detected by remote sensing satellites Observation, Indicates all inter-satellite links. Indicates satellite and Inter-satellite links between them It is the data acquisition rate required for observing remotely sensed targets. Indicates calculation satellite Calculate the computational resources required for remote sensing task m. This indicates the CPU cycles required to process one unit of remote sensing data.

[0097] In some embodiments, the traffic scheduling subproblem is used to determine the maximum amount of data that each remote sensing satellite in a low-Earth orbit satellite network can transmit, and the expression is:

[0098]

[0099] in, Indicates satellite In the time slot Towards The amount of data transmitted Indicates all inter-satellite links. Represents the set of all time slots. Indicates remote sensing satellite In the time slot Available data collection rate Indicates the length of each time slot. Represents a collection of remote sensing satellites. This represents the set of satellites being computed. Indicates satellite and Inter-satellite links between them This indicates the maximum transmission rate of the inter-satellite link. Indicates in time slot Computing satellites Available computing resources This indicates the CPU cycles required to process one unit of remote sensing data.

[0100] Figure 2 This is a flowchart illustrating the standard maximum flow algorithm for solving a traffic scheduling subproblem according to an embodiment of the present invention. In some embodiments, the method transforms the traffic scheduling subproblem into a maximum flow problem and solves it using the standard maximum flow algorithm, including the following steps S201-S203:

[0101] Step S201: Construct a system topology diagram based on the low-Earth orbit satellite network model.

[0102] Step S202: Initialize the residual network in each time slot, use the breadth-first search algorithm to search for augmenting paths from the virtual source node to the virtual destination node, and calculate the feasible flow of the augmenting path.

[0103] Step S203: Update the residual network according to the feasible flow until no available augmentation path can be found, and obtain the maximum transmittable flow of the entire network in this time slot.

[0104] In some embodiments, the expression for the remote sensing scheduling subproblem is:

[0105]

[0106] in, To define variables, where A value of 1 indicates a remote sensing satellite. Assigned to task ,in A value of 0 indicates a remote sensing satellite. Not assigned to a task , Represents a remote sensing sequence, where Indicates task Requires time slots To observe the remote sensing targets specified in the remote sensing mission, and Indicates task No time slot required To observe the remote sensing targets specified in the remote sensing mission. Indicates remote sensing satellite For the task The provided remote sensing window, Represents a collection of remote sensing satellites. Represents the set of all time slots. This indicates the number of real-time remote sensing tasks that a low-Earth orbit satellite network system needs to perform. Indicates task In the time slot The amount of data generated Indicates remote sensing satellite In the time slot The amount of remote sensing data that can be transmitted.

[0107] In some embodiments, the remote sensing scheduling subproblem is solved using an integer programming solver. The remote sensing scheduling subproblem is an integer linear programming problem, and the integer programming solver employs various methods to solve linear programming, mixed integer programming, and quadratic programming problems, including branch and bound, cutting plane, and heuristic solution methods.

[0108] On the other hand, the present invention also provides a low-Earth orbit satellite system for remote sensing, the low-Earth orbit satellite system comprising: one or more remote sensing satellites, communication satellites and computing satellites, the low-Earth orbit satellite system performing the steps of the remote sensing method based on the low-Earth orbit satellite network described above.

[0109] The present invention will now be described with reference to a specific embodiment:

[0110] Figure 3This is a diagram illustrating the remote sensing system architecture of a low-Earth orbit (LEO) satellite network according to an embodiment of the present invention. The present invention addresses the diverse remote sensing mission requirements and the limited resources available for remote sensing, transmission, and computing by proposing a multi-dimensional resource scheduling method for LEO satellite networks.

[0111] 1. System Architecture:

[0112] The entire low-Earth orbit satellite network system consists of three types of satellites: remote sensing satellites (using a collection of...) (representation), calculation of satellites (using sets) (representation) and communication satellites (using sets) (This indicates that) inter-satellite links can be established between satellites, and data can be transmitted via multi-hop relay through multiple inter-satellite links. Remote sensing satellites are equipped with remote sensing payloads such as cameras to observe ground targets and transmit the generated remote sensing data. Computing satellites process the received remote sensing data through their equipped computing units, and communication satellites are mainly responsible for relaying remote sensing data to computing satellites.

[0113] This low-Earth orbit satellite network can be modeled as a time-varying graph. ,in It represents the collection of all remote sensing, communication, and computing satellites. Including all inter-satellite links, of which Indicates satellite and Inter-satellite links between them, T = {1,…,t,…,N} T} is the set of all time slots, where each time slot has a length of . Within each time slot, the topology of the satellite network remains unchanged.

[0114] This low-Earth orbit satellite network system needs to perform Each real-time remote sensing task represents a target located in a specific region of the Earth's surface, where task m ∈ M = {1, 2, ..., M}. The remote sensing satellite needs to observe this target at a specific time and transmit the resulting remote sensing data to a computing satellite for processing. Each remote sensing task uses a triplet. It means that, among them It is the data acquisition rate required for observing remotely sensed targets. Indicates the time when the observation began. Indicates the duration of the observation.

[0115] 1.1 Constructing a remote sensing model:

[0116] The observation capability of a remote sensing satellite for a mission depends on the time during which it can observe the mission-specified remote sensing targets and the data acquisition rate it supports. The time period during which the satellite can observe the mission-specified remote sensing targets is defined as the remote sensing window. Within a given time period... Within a given remote sensing satellite, there may be multiple remote sensing windows for each remote sensing mission. Using ensembles... Indicates satellite For the task All available remote sensing windows, each element As a remote sensing window, it represents the time slot Time slot A continuous time period. A satellite may only have a usable remote sensing window for a mission if its trajectory projected onto the Earth's surface is close to the remote sensing target specified in the mission. Given a satellite... In the time slot latitude ,longitude and the latitude of the target area ,longitude Remote sensing window Eligible conditions:

[0117] (1)

[0118] in , , , These are the maximum rotation angle of the satellite remote sensing camera, the Earth's radius, and the satellite's orbital altitude, respectively.

[0119] Considering that satellites move periodically along their orbits, the remote sensing window for each satellite can be predetermined based on the satellite trajectory and the remote sensing target specified by the mission. In this embodiment, a 0-1 variable is defined. Identifying remote sensing scheduling decisions, among which Indicates task In the time slot by satellite For observation, each satellite needs to be allocated a continuous remote sensing time period to ensure continuous and uninterrupted observation of the mission, expressed as:

[0120] (2)

[0121] (3)

[0122] The satellite's data collection rate is highly limited by the remote sensing payload it carries. Indicates remote sensing satellite In the time slot The available data collection rate, all allocated to remote sensing satellites The cumulative data collection rate for the mission should not exceed the maximum data acquisition rate that the satellite can provide, as expressed by:

[0123] (4)

[0124] In addition, to avoid duplicate observations, each task can only be assigned to one remote sensing satellite, as expressed in the following expression:

[0125] (5)

[0126] 1.2 Constructing the transmission model:

[0127] Remote sensing data collected by remote sensing satellites is transmitted to computing satellites via inter-satellite links through relay satellites for further processing. The data transmission rate can be expressed according to Shannon's theorem as:

[0128] (6)

[0129] in Inter-satellite links Available bandwidth, It is a satellite Transmission power, It is a satellite The transmit antenna gain at that location, It is a satellite The receiving antenna gain at that location, It is noise power. It is free space loss, which can be achieved through... The calculation yielded, where and These represent the speed of light and the center frequency of the carrier wave, respectively. Indicates satellite and The distance between them. The transmission rate used by each task within each time slot. The maximum transmission rate of the link should not be exceeded, as expressed in the following formula:

[0130] (7)

[0131] 1.3 Constructing the computational model:

[0132] The computing satellite is responsible for performing calculations on the received remote sensing data. To ensure real-time processing and smooth execution of the mission, sufficient computing resources need to be allocated. Indicates in time slot Computing satellites Available CPU frequency, all tasks are computing satellite The cumulative computing resources used should not exceed those of the satellite. Its computational power is expressed as:

[0133] (8)

[0134] 2. Constructing a problem model:

[0135] The goal of the multidimensional resource scheduling method is to maximize the number of achievable remote sensing tasks under constraints of differentiated remote sensing mission requirements, satellite remote sensing capabilities, transmission capabilities, and computing capabilities. Achieving this goal requires joint scheduling of remote sensing satellites, relay satellites, and computing satellites. A 0-1 variable is defined. ,in Indicates remote sensing satellite Assigned to task Problem modeling:

[0136]

[0137] st (1)-(8)

[0138] (9)

[0139] (10)

[0140] (11)

[0141] (12)

[0142] in This represents the CPU cycles required to process one unit of remote sensing data. Constraint (9) shows the variables. and The relationship between the data and the data is as follows. Constraint (10) indicates that the remote sensing satellite needs to allocate sufficient data transmission rate to each remote sensing task in each time slot to ensure the normal transmission of remote sensing data. Constraint (11) ensures that the computing satellite allocates sufficient computing resources to process the received remote sensing task data. Finally, constraint (12) ensures that the computing satellite can successfully process all remote sensing tasks observed by the remote sensing satellite. It can be seen that this problem is a nonlinear mixed integer programming problem. The non-convexity of constraint (9) and the coordinated scheduling of remote sensing, transmission and computing resources increase the difficulty of solving the problem.

[0143] 3. Algorithm Design:

[0144] To address the aforementioned issues, this embodiment proposes a multidimensional resource scheduling algorithm, ORCA. ORCA decomposes the original problem into two sub-problems: a flow scheduling sub-problem and a remote sensing scheduling sub-problem. The flow scheduling sub-problem primarily determines the maximum amount of data that each remote sensing satellite in the satellite network can transmit, and is solved by transforming it into a single-source, single-sink, multi-commodity flow problem. The remote sensing scheduling sub-problem is then solved using an integer linear programming method, based on the solution to the flow scheduling sub-problem and the available remote sensing resources of the remote sensing satellites, to schedule remote sensing tasks.

[0145] 3.1 Traffic Scheduling Subproblem:

[0146] The traffic scheduling subproblem combines the data acquisition rate of remote sensing satellites, the transmission rate of communication satellites, and the computational frequency constraints of computing satellites to determine the maximum amount of data that remote sensing satellites can transmit. Indicates satellite In the time slot Towards The amount of data transmitted. This sub-problem indicates:

[0147]

[0148] (P1-1) Restricted to each time slot The amount of data transmitted from the internal remote sensing satellite to the communication satellite shall not exceed the total amount of remote sensing data collected, (P1-2) to ensure that each time slot... The amount of data transmitted on the inter-satellite link does not exceed the link capacity (P1-3), ensuring that the computing satellite provides sufficient computing resources to process the received remote sensing data in real time.

[0149] P1 can be transformed into a maximum flow problem, which can be solved using the standard maximum flow algorithm. The transformation process is shown below:

[0150] Satellite network topology diagram Extended to ,in Inter-satellite links The capacity sequence, Indicates from satellite arrive Inter-satellite links in time slots The maximum data transfer capacity within, i.e. Since the traffic through the link should not exceed the link capacity, the expression is:

[0151] (13)

[0152] This constraint is equivalent to (P1-2). According to flow conservation, the transmission communication satellite... The amount of data sent must equal the amount of data transmitted. The expression is:

[0153] (14)

[0154] Constraints (P1-1) and (P1-3) can be obtained from the constructed time-varying diagram. This problem is solved by introducing virtual nodes and edges. The specific solution is described below:

[0155] (1) Remote sensing constraints: Introduce virtual source nodes Build from virtual source node To all remote sensing satellites Virtual links, configuring each link in time slots capacity For satellites within this time slot The maximum amount of data that can be collected is expressed as:

[0156] (15)

[0157] Based on flow conservation and link capacity constraints, the expression is:

[0158] (16)

[0159] (2) Computational constraints: Introduce virtual destination nodes Build from all computing satellites to virtual target nodes The virtual link. To ensure that the computing satellite can process received data in real time, the computing satellite... To virtual destination node Virtual links in time slots The capacity is set to the maximum amount of data that the satellite can process within this time slot, expressed as:

[0160] (17)

[0161] Based on flow conservation and link capacity constraints, the expression is:

[0162] (18)

[0163] After transforming the problem into a standard maximum flow problem, the Edmunds-Kapp algorithm, a maximum flow algorithm, can be used to obtain remote sensing satellite data. In the time slot The amount of remote sensing data that can be transmitted .

[0164] 3.2 Remote Sensing Scheduling Subproblem:

[0165] The objective of the remote sensing scheduling subproblem is to optimize the allocation of remote sensing tasks based on the maximum transmittable data volume of remote sensing satellites and limited remote sensing resources, in order to maximize the number of remote sensing tasks completed. Each remote sensing task... The remote sensing requirements are transformed into remote sensing sequences, using It means that, among them Indicates task Requires time slots To observe the remote sensing targets specified in the remote sensing mission, and This indicates that no time slot is required. This embodiment has Therefore, the task In the time slot The amount of data generated can be expressed as Similarly, each remote sensing satellite For the task The provided remote sensing window can be represented as This embodiment has The remote sensing scheduling subproblem is formulated as follows:

[0166]

[0167] P2 is an integer linear programming problem that can be solved using readily available solvers based on branch and bound, cutting planes, and heuristics.

[0168] Algorithm 1 summarizes the entire algorithm flow, mainly including two parts: solving the maximum flow calculation of the traffic scheduling subproblem and solving the remote sensing satellite selection of the remote sensing scheduling subproblem. In solving the traffic scheduling subproblem, this embodiment first constructs a system topology based on the low-Earth orbit satellite network model. Within each time slot, the residual network is initialized first, and then a breadth-first search algorithm is used to search for augmenting paths from virtual source nodes to virtual destination nodes. The feasible flow of the augmenting paths is calculated, and the residual network is updated based on the feasible flow until no usable augmenting path can be found, thus obtaining the maximum transmittable traffic of the entire network within that time slot. Using the obtained maximum transmittable traffic as a constraint, a remote sensing scheduling subproblem is constructed, and then solved using an integer programming solver to allocate optimal remote sensing satellites, transmission paths, transmission rates, and computation frequencies for each task.

[0169] In summary, this invention provides a remote sensing method and system based on a low-Earth orbit (LEO) satellite network. The LEO satellite network system comprises remote sensing satellites, communication satellites, and computing satellites. The remote sensing satellites collect data on the target areas of their assigned remote sensing tasks. The remote sensing satellites and computing satellites communicate via relay satellites. The computing satellites perform calculations on the assigned remote sensing data. Constraints are established based on the data acquisition capabilities of the remote sensing satellites to ensure the effective execution of remote sensing tasks. Constraints are also established during data transmission to ensure that the transmission rate occupied by inter-satellite links does not exceed their maximum transmission rate. Constraints are established during data computation to ensure that the cumulative computing resources occupied by the computing satellites do not exceed their computing capacity. This invention comprehensively considers the remote sensing, communication, and computing resources in the satellite network, achieving optimal resource allocation when allocating remote sensing, communication, and computing resources to differentiated remote sensing tasks in the LEO satellite network, thereby maximizing the number of remote sensing tasks executed.

[0170] Furthermore, this invention designs a resource-aware scheduling algorithm that divides the joint scheduling problem of remote sensing, communication, and computing resources into a flow scheduling subproblem and a remote sensing scheduling subproblem. The flow scheduling subproblem determines the maximum amount of data that the remote sensing satellite can transmit based on the data acquisition rate of the remote sensing satellite, the transmission rate of the communication satellite, and the computing power of the computing satellite. The remote sensing scheduling subproblem optimizes the allocation of remote sensing tasks based on the maximum amount of data that the remote sensing satellite can transmit and the remote sensing resources, thereby maximizing the number of remote sensing tasks completed. This algorithm reduces the complexity of solving the original problem. The flow scheduling subproblem and the remote sensing scheduling subproblem are solved using the maximum flow algorithm and an integer programming solver, respectively.

[0171] This invention also provides a computer device, which may include a processor and a memory, wherein the processor and the memory may be connected via a bus or other means.

[0172] The processor can be a central processing unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips.

[0173] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the button blocking method of the vehicle display device in this embodiment of the invention. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory.

[0174] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0175] The one or more modules are stored in the memory, and when executed by the processor, they perform the method described in this embodiment.

[0176] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned edge computing server deployment method. The computer-readable storage medium can be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, floppy disks, hard disks, removable storage disks, CD-ROMs, or any other form of storage medium known in the art.

[0177] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0178] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0179] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0180] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote sensing method based on low-Earth orbit satellite networks, characterized in that, The method is used to operate on a low-Earth orbit (LEO) satellite network, which includes one or more remote sensing satellites, communication satellites, and computing satellites. The method includes the following steps: Construct a remote sensing model and establish constraints so that after each remote sensing task is assigned to a designated remote sensing satellite, each remote sensing satellite can complete data acquisition of the target area of ​​one or more assigned remote sensing tasks within its data sampling capability range; A transmission model is constructed, and constraints are established to ensure that the inter-satellite link transmission rate occupied during the relay communication between the remote sensing satellite and the computing satellite through the communication satellite does not exceed its maximum transmission rate. A computational model is constructed, and constraints are established to ensure that the cumulative computational resources used by the computational satellite when performing the assigned remote sensing tasks do not exceed its computational capacity; Construct a problem model, and based on the remote sensing model, the transmission model, and the computation model, maximize the total number of remote sensing tasks performed by the low-Earth orbit satellite network; The problem model is decomposed into a traffic scheduling subproblem and a remote sensing scheduling subproblem. The traffic scheduling subproblem is used to determine the maximum amount of data that each remote sensing satellite in the low-Earth orbit satellite network can transmit. The traffic scheduling subproblem is solved based on a first predetermined scheme. The remote sensing scheduling subproblem is solved based on the solution of the traffic scheduling subproblem and the available remote sensing resources of the remote sensing satellites to schedule the remote sensing tasks. The remote sensing scheduling subproblem is solved based on a second predetermined scheme. The traffic scheduling subproblem is used to determine the maximum amount of data that each remote sensing satellite in the low-Earth orbit satellite network can transmit, and its expression is: in, Indicates satellite In the time slot Towards The amount of data transmitted Indicates all inter-satellite links. Represents the set of all time slots. Indicates remote sensing satellite In the time slot Available data collection rate Indicates the length of each time slot. Represents a collection of remote sensing satellites. This represents the set of satellites being computed. Indicates satellite and Inter-satellite links between them This indicates the maximum transmission rate of the inter-satellite link. Indicates in time slot Computing satellites Available computing resources This represents the CPU cycles required to process one unit of remote sensing data. The method transforms the traffic scheduling subproblem into a maximum flow problem and solves it using the standard maximum flow algorithm. The specific steps are as follows: Construct a system topology based on the low-Earth orbit satellite network model; initialize the residual network in each time slot, use the breadth-first search algorithm to search for augmenting paths from virtual source nodes to virtual destination nodes, and calculate the feasible flow of the augmenting paths; update the residual network according to the feasible flow until no available augmenting path can be found, and obtain the maximum transmittable traffic of the entire network in that time slot. The expression for the remote sensing scheduling subproblem is: in, To define variables, where A value of 1 indicates a remote sensing satellite. Assigned to task ,in A value of 0 indicates a remote sensing satellite. Not assigned to a task , Represents a remote sensing sequence, where Indicates task Requires time slots To observe the remote sensing targets specified in the remote sensing mission, and Indicates task No time slot required To observe the remote sensing targets specified in the remote sensing mission. Indicates remote sensing satellite For the task The provided remote sensing window, Represents a collection of remote sensing satellites. Represents the set of all time slots. This indicates the number of real-time remote sensing tasks that the low-Earth orbit satellite network system needs to perform. Indicates task In the time slot The amount of data generated Indicates remote sensing satellite In the time slot The amount of remote sensing data that can be transmitted; the remote sensing scheduling subproblem is solved based on an integer programming solver.

2. The remote sensing method based on low-Earth orbit satellite networks according to claim 1, characterized in that, Constraints are established to enable each remote sensing satellite to complete data acquisition of the target area of ​​one or more assigned remote sensing missions within its data sampling capabilities, including: A constraint is established to make the trajectory of the remote sensing satellite projected onto the Earth's surface approach or cover the target area, expressed as: ； ； in, Indicates satellite In the time slot latitude at that location Indicates satellite In the time slot Longitude of the location Indicates the latitude of the target area. Indicates the longitude of the target area. This indicates the maximum rotation angle of the satellite remote sensing camera. Represents the Earth's radius. Indicates the satellite's orbital altitude, [ , [Indicates from time slot] Time slot A continuous period of time; And establish constraints to ensure that the cumulative data collection rate of the remote sensing mission allocated to each remote sensing satellite does not exceed the maximum data acquisition rate of the corresponding remote sensing satellite, expressed as: ； in, This indicates the variables that identify remote sensing scheduling decisions. A value of 1 indicates a remote sensing task. In the time slot The remote sensing satellite Observation, A value of 0 indicates a remote sensing task. In the time slot Not by the aforementioned remote sensing satellite Observation, This indicates the data acquisition rate required to observe remotely sensed targets. Indicates remote sensing satellite In the time slot Available data collection rate This represents the set of remote sensing satellites. Represents the set of all time slots. This indicates the number of real-time remote sensing tasks that the low-orbit satellite network system needs to perform.

3. The remote sensing method based on low-Earth orbit satellite networks according to claim 2, characterized in that, Constraints are established to ensure that the inter-satellite link transmission rate used during relay communication between the remote sensing satellite and the computing satellite via the communication satellite does not exceed its maximum transmission rate, including: Calculate the maximum transmission rate of inter-satellite links using Shannon's theorem. The expression is: ； ； in, This indicates the available bandwidth of the inter-satellite link. Indicates the satellite Transmission power, Indicates the satellite The transmit antenna gain at that location Indicates the satellite The receiving antenna gain at that location, Indicates noise power. Indicates free space loss. Represents the speed of light. Indicates the center frequency of the carrier. Indicates the satellite and The distance between them; And the transmission rate used by each task transmitted from the communication satellite to the computing satellite within each time slot. It should not exceed the maximum transmission rate of the corresponding inter-satellite link, expressed as: ； in, This indicates the number of real-time remote sensing tasks that the low-Earth orbit satellite network system needs to perform. Indicates all inter-satellite links. Represents the set of all time slots. Indicates satellite and Inter-satellite links between them This indicates the maximum transmission rate of the inter-satellite link.

4. The remote sensing method based on low-Earth orbit satellite networks according to claim 3, characterized in that, A constraint is established to ensure that the cumulative computing resources used by the computing satellite when performing its assigned remote sensing tasks do not exceed its computing capacity, expressed as: ； in, Indicates in time slot Computing satellites Available computing resources This represents the computing resources required by the computing satellite s to compute the remote sensing mission m, where T is the set of all time slots. This indicates the number of real-time remote sensing tasks that the low-orbit satellite network system needs to perform.

5. The remote sensing method based on low-Earth orbit satellite networks according to claim 4, characterized in that, Based on the constraints of the remote sensing model, the transmission model, and the computational model, the expression of the problem model is as follows: in, To define variables, where A value of 1 indicates a remote sensing satellite. Assigned to task , A value of 0 indicates a remote sensing satellite. Not assigned to a task , This indicates the number of real-time remote sensing tasks that the low-Earth orbit satellite network system needs to perform. Represents a collection of remote sensing satellites. This represents the set of satellites being computed. This indicates the transmission rate used by each task. This indicates the variables that identify remote sensing scheduling decisions. A value of 1 indicates the remote sensing task. In the time slot Remote sensing satellite Observation, A value of 0 indicates the remote sensing task. In the time slot Not detected by remote sensing satellites Observation, Indicates all inter-satellite links. Indicates satellite and Inter-satellite links between them It is the data acquisition rate required for observing remotely sensed targets. Indicates calculation satellite Calculate the computational resources required for remote sensing task m. This indicates the CPU cycles required to process one unit of remote sensing data.

6. A low-Earth orbit satellite system for remote sensing, characterized in that, The low-Earth orbit satellite system includes one or more remote sensing satellites, communication satellites, and computing satellites, and the low-Earth orbit satellite system performs the steps of the method as described in any one of claims 1 to 5.

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