A satellite network connection plan design method based on shadow perception topology control

Abstract

The application claims a satellite network connection plan design method based on shadow perception topology control, belonging to the technical field of wireless communication. In view of the limited battery capacity of small satellites and the time-varying characteristics of power, considering the demand of different observation data on time delay and throughput, a connection plan design method based on shadow perception topology control is proposed. Considering that the periodic appearance of small satellites on the earth's sun and moon surface will lead to the time-varying of battery energy, the small satellite network topology control is carried out according to the position information of the satellite sun and moon surface, the energy model is established by using the constructed shadow perception space-time diagram, and the lowest energy consumption path is found for all node pairs, and all connections in each time slot are traversed to screen the connections meeting the residual energy constraint. Considering the limited number of repeaters, limited satellite storage capacity and limited backhaul opportunity, the snake optimization algorithm is used to design the connection plan for different demand observation data, which minimizes the real-time data delay and maximizes the non-real-time data throughput.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology. Specifically, it relates to the design of a satellite network connection planning method based on shadow-aware topology control. Background Technology

[0002] As space missions become increasingly complex and diverse, satellites need to perform more and more functions. Traditional solutions relying on single, high-capacity, high-efficiency, and long-life satellites are costly to develop and difficult to maintain. Furthermore, the increased complexity of the entire system significantly reduces its reliability and security. Using multiple small satellites working collaboratively to handle complex tasks instead of a single large satellite has become a promising solution. On traditional platforms, geostationary orbit relay satellites are typically used to transmit observation data from low-Earth orbit satellites to ground stations to improve coverage and link availability. However, with the increase in observation missions, geostationary orbit satellites may become overloaded. To address this trend, deploying small satellites with low development costs and energy consumption in low Earth orbit is considered a promising approach. Small satellites offer advantages such as low mass (typically less than 500 kg), small size, low cost, short development cycle, and high performance. Small satellite technology is already used in various fields, including micro-propulsion, CubeSats, and long-distance communication. New and emerging applications include scientific missions, Earth remote sensing, space exploration, radar sensing, remote command and control, surveillance, hardware testing, educational operations and training, land deformation detection, weather forecasting, and defense, making them an important part of the aerospace industry.

[0003] However, due to limitations in size and mass, small satellites can only be equipped with solar panels of limited capacity, resulting in a very limited energy budget. Furthermore, due to their orbital motion, small satellites periodically appear on the Earth's dark and sunny sides. Satellites on the dark side rely entirely on battery power to establish connections, while those exposed to sunlight have more connection options and longer distances. Therefore, reasonable topology control of the small satellite network is necessary to plan battery energy usage, ensuring network connectivity while preventing transmission link establishment from being affected by battery levels below the maximum discharge depth, thus avoiding increased network latency. In addition, Earth observation plays an irreplaceable role in environmental monitoring, disaster relief, and other fields. For these diverse application scenarios, small satellites acquire a large amount of data daily, such as non-real-time data in environmental monitoring and real-time data in disaster relief. This leads to different latency and throughput requirements for different observation data. Therefore, arranging reasonable transmission paths for observation data with varying needs is particularly important for improving network operating efficiency. On the other hand, with the high-speed movement of satellites, the relative positions between satellites change frequently, leading to the sporadic nature of connections established between node pairs, and the constant evolution of inter-satellite links and topology. To seek stable connection topologies that meet optimization objectives, the design of connection plans for intermittent connections has attracted extensive research. However, due to resource constraints such as limited transponder and memory capacity, not all "connections," i.e., potentially available links, are feasible during data transmission. In summary, although there may be more connections, satellites typically can only select one connection at a time due to the aforementioned limitations. Therefore, in resource-constrained small satellite networks, efficient connection planning plays a crucial role in improving network performance.

[0004] Existing research on small satellite network topology control and connectivity planning focuses on saving network costs and balancing inter-satellite link reliability, redundancy, and robustness, neglecting the limited battery capacity of small satellites. Current connectivity planning research emphasizes improving fairness in node connection acquisition, dynamic connectivity, and enhancing network data backhaul performance, but different mission requirements are still overlooked. Therefore, this invention proposes a satellite network connectivity planning method based on shadow-aware topology control. This method rationally plans the use of small satellite energy and, within limited on-board resources, designs connectivity plans for observation data with varying needs.

[0005] This invention proposes a satellite network connection planning design method based on shadow-aware topology control. Addressing the limited battery capacity and time-varying power characteristics of small satellites, and considering the latency and throughput requirements of different observation tasks, the method performs small satellite network topology control based on the differences in the sun-facing and shadow-facing positions of the small satellites. A constructed shadow-aware spatiotemporal graph is used to find the lowest-energy-consumption path for all node pairs. Then, all connections in each time slot are traversed, and connections that meet the remaining energy constraints are selected. Considering the limited number of transponders, constrained satellite memory capacity, and limited backhaul opportunities, a snake optimization algorithm is used to design connection plans for observation data with different requirements, minimizing real-time data latency while maximizing non-real-time data throughput. Summary of the Invention

[0006] The present invention aims to solve the problems of the prior art mentioned above. A method is proposed. The technical solution of the present invention is as follows:

[0007] A satellite network connectivity planning design method based on shadow-aware topology control includes the following steps:

[0008] S1: Obtain the satellite visibility matrix and satellite eclipse position information within a period.

[0009] S2: Construct a shadow-aware spatiotemporal graph G = (V, E), where V represents all nodes in G and E represents all connections in G.

[0010] S3: Establish a small satellite energy model based on the satellite energy harvesting and consumption process.

[0011] S4: Calculate the lowest energy-consuming path between all node pairs. The connected subgraph G' = (V, E') based on the lowest energy consumption path is obtained.

[0012] S5: Iterate through all connections in each time slot of E, and determine whether establishing a connection satisfies the remaining energy constraint. Add the connection that satisfies the remaining energy constraint to the connection subgraph G'. Otherwise, continue iterating through all connections in the current time slot until all connections in all time slots have been traversed. The resulting connection subgraph G” = (V, E”) is obtained based on the lowest energy consumption path and the remaining energy constraint.

[0013] S6: Establish constraints on the number of transponders, flow conservation, satellite memory capacity, and link capacity.

[0014] S7: Construct a connection plan design model and use the snake optimization algorithm to design connection plans for observation data with different requirements, generating connection plans that minimize real-time data latency while maximizing non-real-time data throughput.

[0015] The advantages and beneficial effects of this invention are as follows:

[0016] This invention analyzes the motion patterns of small satellite networks and, considering the limited energy budget and energy harvesting process of small satellites, limited network resources, and varying observation data requirements, proposes a satellite network connection planning design method based on shadow-aware topology control. The main innovation of this invention lies in using shadow-aware spatiotemporal maps to rationally plan the use of small satellite battery energy and design connection plans for observation data with different needs, minimizing real-time data latency while maximizing non-real-time data throughput. Existing topology control research focuses on reducing network costs and balancing inter-satellite link reliability with link redundancy and robustness, without much attention to the limited onboard payload that small satellites can carry. Existing connection plan designs often focus on connection fairness, dynamic connectivity, and single data return performance or latency, ignoring the varying observation data requirements and typically assuming all connections are available. Therefore, the satellite network connection planning design based on shadow-aware topology control proposed in this invention is something that those skilled in the art would not easily conceive of. Furthermore, this invention constructs a shadow-aware spatiotemporal map based on satellite visibility matrix and satellite eclipse position information to describe the transmission of any data in the network and the eclipse position information of small satellites. Using this shadow-aware spatiotemporal map, two shadow-aware topology control algorithms are proposed to filter connections that satisfy the remaining energy constraint. Simultaneously, this invention uses the difference between the weighted storage amount of real-time data from small satellites within a period and the storage amount of non-real-time data from the ground station in the last time slot as the objective function, minimizing real-time data latency while maximizing non-real-time data throughput. Therefore, this invention possesses uniqueness and inventiveness. This invention fully analyzes the characteristics of small satellite networks in Earth observation missions, combining topology control and connection planning design. In existing research, the research models set by researchers are too idealistic. Due to the limited battery capacity that small satellites can carry, it is not possible to assume that all connections are stable and available, and the observation data requirements are different, so it is not possible to focus on only one type of requirement. Therefore, this invention has inventiveness and ease of implementation in its solution. By combining topology control and connection planning, this invention rationally plans power usage while ensuring network connectivity, designing connections for observation data with different needs to meet the latency and throughput requirements of various observation tasks. It also considers practical network scenarios, demonstrating the rationality and practicality of this invention. Attached Figure Description

[0017] Figure 1 This is an overall flowchart of the satellite network connectivity planning design method based on shadow-aware topology control, according to a preferred embodiment of the present invention.

[0018] Figure 2 It is a spatiotemporal map based on shadow perception. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0020] The technical solution of the present invention to solve the above-mentioned technical problems is:

[0021] The model designed according to the present invention is as follows:

[0022] Network Model

[0023] The primary scenario of this invention is small satellite Earth observation. The network model consists of small satellites, mission observation points, and ground stations. Small satellites are used for acquiring and transmitting mission data, mission observation points represent the areas to be collected, and ground stations receive the mission data transmitted back by the small satellites. In this network, small satellites perform Earth observation missions and transmit the data collected from the observation points back to the ground stations. Small satellites can only collect data from observation points within their observation area. If a ground station exists that can directly establish a satellite-to-ground communication connection, the acquired data is directly transmitted back to the ground station. If no such ground station exists, but there are small satellites that can establish inter-satellite connections, the small satellite can forward the data to other small satellites or store it in the next time slot for a more appropriate time to forward it.

[0024] This invention proposes a satellite network connection planning design method based on shadow-aware topology control. A shadow-aware spatiotemporal graph G is constructed based on the visibility matrix and satellite position information (both on the sunlit and shadowed sides of the Earth). Considering that the periodic appearance of small satellites on the sunlit and shadowed sides of the Earth causes time-varying battery energy, small satellite network topology control is performed based on the differences in satellite position information. The constructed shadow-aware spatiotemporal graph is used to find the lowest energy consumption path for all node pairs. Then, all connections in each time slot are traversed, and connections that satisfy the remaining energy constraint are selected, resulting in a connection subgraph G", which serves as the input to the connection planning design problem. Then, constraints on the number of transponders, flow conservation, small satellite memory capacity, and link capacity are established. A snake optimization algorithm is used to design connection plans for observation data with different requirements, minimizing real-time data latency while maximizing non-real-time data throughput. The specific steps are as follows:

[0025] Step 1: Use STK software to obtain the satellite visibility matrix and satellite eclipse position information within a period.

[0026] Step 2: Based on the visibility matrix and satellite eclipse location information, construct a shadow-aware spatiotemporal graph G = (V, E), where V represents all nodes in G and E represents all connections in G. For example... Figure 2As shown, graph G is divided into K discrete and equal time slots, transforming the dynamically time-varying network into a series of continuous static graph sequences, where nodes located on the sunlit side of the earth are represented in white and nodes located on the shadowed side of the earth are represented in black.

[0027] Step 3: Establish a small satellite energy model based on the satellite energy harvesting and consumption process.

[0028] The energy consumption of small satellites mainly consists of two parts: static energy consumption and dynamic energy consumption. Static energy consumption for:

[0029]

[0030] Where P o This refers to static power. Dynamic energy consumption. for:

[0031]

[0032] in Let $\mathbf$ and $\mathbf$ represent the energy consumed by satellite node $i$ in receiving information and the energy consumed in transmitting information, respectively, in time slot $t$. The energy consumed in transmitting information and the energy consumed in receiving information are expressed as follows:

[0033]

[0034]

[0035] Among them, Boolean variables are introduced. If the link If activated, then on the contrary P sg This represents the transmit power of the satellite-to-ground link. P re and P tr These represent the receive power and transmit power of the inter-satellite link, respectively. In space, laser communication links are used between satellites. re and P tr The relationship between them is:

[0036]

[0037] Among them G t G r ,λ,η ot η or G(θ) off ), where d represents the transmit antenna gain, receive antenna gain, signal wavelength, transmit optical component benefit, receive optical component benefit, off-axis gain, and distance between transponders, respectively.

[0038] In summary, satellite node v iThe energy consumed in time slot t is:

[0039]

[0040] Satellite v i The energy absorbed and stored in time slot t is

[0041]

[0042] Where P c It is the absorption rate of solar energy by the satellite's solar panels. Time represents the satellite node v in the t-th time slot. i Located on the sunlit side of the Earth, when Time represents the satellite node v in the t-th time slot. i Located on the dark side of the Earth.

[0043] Based on the model of energy loss and absorption processes of the aforementioned satellite nodes, satellite v i Remaining energy at the end of time slot t for:

[0044]

[0045] in For satellite v i The remaining energy at the end of the previous time slot. Due to the limited capacity of the satellite's batteries, the remaining energy of the satellite node must satisfy the following constraints:

[0046]

[0047] Among them EB imax It is satellite v i The battery capacity is θ, and the maximum depth of discharge of the satellite battery is θ.

[0048] Finally, let's assume that the ground station continues to operate normally.

[0049] Step 4: Calculate the lowest energy consumption path for all node pairs to obtain the connected subgraph G' = (V, E') based on the lowest energy consumption path.

[0050] Definition 1: In a graph G, if G contains a path from node u to v, then the two nodes u and v are connected. A graph G is connected if every pair of distinct nodes is connected.

[0051] To maintain overall network connectivity and meet satellite energy requirements, it is necessary to find any node pair. The lowest energy-consuming path between [i,j], where i,j∈[1,N] s A topology control algorithm based on shadow awareness is proposed, with the following basic steps:

[0052] Step 1: Based on the small satellite energy model, according to the distance matrix The energy consumption of all potential inter-satellite connections is preprocessed.

[0053] Step 2: Traverse all node pairs, and in each round, select V. s2e Any pair of nodes in Using Dijkstra's algorithm to find the lowest energy path for a pair of nodes in a shadow-aware spatiotemporal graph G.

[0054]

[0055] Step 3: Determine and establish the lowest energy consumption path We determine whether the connections in the graph satisfy the residual energy constraint. If they do, we add them to the connected subgraph G' = (V, E'). If they do not, there is no solution and this is not within the scope of our discussion. We also introduce two two-dimensional matrices. and Record the small satellite node v respectively i The maximum and minimum energy consumption for establishing an inter-satellite connection in the t-th time slot (only when satellite node v...). i The corresponding updates are only made when the link is established. When no link is established and (Default is 0).

[0056] Step 4: Repeat steps 2 and 3, and execute... After round 1, all the lowest energy-consuming paths are added to the connection subgraph G', resulting in a connection subgraph G' based on the lowest energy-consuming paths.

[0057] Step 5: Add new connections to the connection subgraph G', resulting in a connection subgraph G” = (V, E”) based on the minimum energy consumption path and the remaining energy constraint.

[0058] When link transceiver node v x and v y A connection can be added if the following residual energy constraints are met. To connect the subgraph G'.

[0059]

[0060] If this connection satellite node v is added x The energy consumed is greater than Then update the remaining energy of the satellite node:

[0061]

[0062] In summary, the basic steps of the shadow-aware topology control algorithm 2 are as follows:

[0063] Step 1: Obtain the connected subgraph G' based on the shadow-aware topology control algorithm 1.

[0064] Step 2: Starting from the first time slot, iterate through all time slots within a cycle, and determine if a connection needs to be established in that time slot. node v x ,v y Can the remaining energy constraint be satisfied? If node v x ,v y If both formulas (9) and (10) are satisfied, then the connection will be... Add to the connected subgraph G' and update the satellite node v. x ,v y Remaining energy and

[0065] Step 3: Repeat Step 2 until all connections in all time slots have been traversed. Obtain the connection subgraph G”=(V,E”) based on the minimum energy consumption path and remaining energy constraints.

[0066] Step 6: Establish constraints on the number of transponders, flow conservation, small satellite memory capacity, and link capacity.

[0067] (1) Establish a constraint on the number of repeaters

[0068] Assuming each small satellite is equipped with a pair of transponder antennas, due to the limitation on the number of small satellite transponders, each small satellite can only establish a transmission link with one other small satellite or ground station at a time, and the following constraints must be satisfied:

[0069]

[0070]

[0071] (13) indicates that the established inter-satellite (satellite-to-ground) link is bidirectional.

[0072] (2) Flow conservation constraint

[0073] use This represents the set of all real-time data collected by a satellite node in any time slot, where Indicates satellite node v i The real-time data stream collected in the t-th time slot; using This represents the set of all non-real-time data collected by a satellite node in any time slot, where Indicates satellite node v i The non-real-time data stream collected in the t-th time slot, therefore Represents all data streams during the acquisition and transmission process. (Definition) For data flow f in the link The amount of data on the network. Data flowing in and out of a node must satisfy the law of flow conservation. Flow conservation is described as the sum of all outgoing data streams and data streams in the buffer equaling the total amount of data collected, i.e.:

[0074]

[0075] in This indicates the satellite node v. i The amount of data stored in data stream f at the end of time slot t, src(f) is the data stream f. i,t The source satellite, st(f) is the data stream f i,t The starting time slot.

[0076] For data relay satellites, the following requirements must be met:

[0077]

[0078] (3) Establish small satellite memory capacity constraints

[0079] Small satellites have limited memory capacity. After time slot t ends, satellite node v... i The total amount of data stored cannot exceed its total storage capacity B. max ,Right now:

[0080]

[0081] (4) Establish link capacity constraints

[0082] All from satellite node v i Converging to satellite node v j The data flow f cannot exceed the capacity of the corresponding link, that is:

[0083]

[0084]

[0085] Where C ss and C sg These are the capacities for inter-satellite links and satellite-to-ground links, respectively.

[0086] Step 7: Construct a connection plan design model and use the snake optimization algorithm to design connection plans for observation data with different requirements, generating connection plans that minimize real-time data latency while maximizing non-real-time data throughput.

[0087] (1) Constructing a connection plan design model

[0088] The goal of connection planning is to minimize the latency of real-time data in the network while maximizing the throughput of non-real-time data. Considering various constraints in the real-world environment, the connection planning problem can be formulated as follows:

[0089]

[0090] conform to:

[0091] C1: C2: C3: C4: C5: C6: C7:

[0092] (2) Solving the connection planning problem using the snake optimization algorithm

[0093] First, an initial population is randomly generated and divided into male and female populations. Individuals within each population are represented by a binary code representing a connection plan over a given period. Then, the male and female populations undergo two phases: exploration and development. In the exploration phase, both populations search for "food" (global search). When food is plentiful, they enter the development phase (local search). During development, if the "temperature" is above a threshold, males and females move closer to the "food." If the "temperature" is below the threshold, males and females randomly engage in either "fighting" or "mating" to exchange information and find the optimal solution. Finally, after multiple iterations, the optimal connection plan is generated.

[0094] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions.

[0095] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0096] The above embodiments should be understood as illustrative only and not as limiting the scope of protection of the present invention. After reading the description of the present invention, those skilled in the art can make various alterations or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A satellite network connection plan design method based on shadow-aware topology control, characterized in that, Comprise the following steps: S1: Obtain satellite visibility matrix and satellite yin and yang surface position information in a cycle; S2: Constructing a spatio-temporal graph based on shadow awareness wherein denotes all nodes in denotes all connections in S3: Establish a small satellite energy model according to the satellite energy collection and consumption process; S4: Calculate the lowest energy path between all pairs of nodes , obtaining a subgraph of connections based on the lowest energy paths ; S5: traversing all connections in each time slot in turn, judging whether the establishment of a connection meets the residual energy constraint, and adding the connection meeting the residual energy constraint to the connection subgraph In S5, otherwise, continue to traverse all connections in the current time slot until all connections in all time slots are traversed; and obtain the connection subgraph based on the lowest energy consumption path and the residual energy constraint In S5, otherwise, continue to traverse all connections in the current time slot until all connections in all time slots are traversed; and obtain the connection subgraph based on the lowest energy consumption path and the residual energy constraint ; S6: Establish the number of transponders, flow conservation constraints, satellite memory capacity constraints and link capacity constraints; S7: Build a connection plan design model, and use a snake optimization algorithm to design a connection plan for observation data with different requirements to generate a connection plan that minimizes real-time data delay while maximizing non-real-time data throughput; The connection plan for observation data with different requirements is designed by using the snake optimization algorithm, which specifically comprises: First, randomly generate an initial population, and divide the population into male and female populations. The individuals in the population are binary coded connection plans in a cycle. Then the male and female populations undergo exploration and development stages. In the exploration stage, the male and female populations search for "food" (global search). When the "food" is sufficient, enter the development stage (local search). In the development stage, if the "temperature" is higher than the threshold, the male and female populations approach the "food". When the "temperature" is lower than the threshold, the male and female individuals randomly undergo "battle" and "mating" modes. The purpose is to exchange information in the two populations to find the optimal solution. After multiple iterations, a connection plan that minimizes real-time data delay while maximizing non-real-time data throughput is generated.

2. The method of claim 1, wherein, The step S1 of obtaining satellite visibility matrix and satellite yin and yang surface position information in a cycle specifically comprises: The visibility matrix between satellites and between satellites and ground stations within a given period is obtained using STK software, and a matrix of size [value missing] is used. 3D 0,1 matrix express, ,in This represents the number of network nodes, i.e., the number of satellites. Add ground stations , This represents the number of time slots in the network. Indicates the first Nodes in each time slot and nodes A connection can be established if the conditions are not met, otherwise a connection cannot be established. A size of [size missing] is used. It is a two-dimensional 0,1 matrix Indicates the status of the satellite node. Indicates the first Satellite nodes in each time slot It is located on the sunny side of the Earth, and vice versa.

3. The method of claim 1, wherein, The step S2 considers the characteristics that the small satellites periodically appear on the earth's dark side and the bright side and the network topology changes rapidly, and constructs a time-space graph based on shadow perception according to the visibility matrix and the satellite dark-bright side position information , specifically comprising the following steps: (1) Divide the dynamic network into multiple time slots Given small satellite orbit operation period The dynamic network is divided into several discrete, continuous and equal length time slots, denoted as The length of each time slot is , and the network topology is considered to be fixed within a time slot, and the dynamic network is converted into a series of static graph sequences ; (2) Represent the state of the satellite Using the state matrix , described in the Each time slot satellite node Whether it is located on the dark or sunny side of the Earth, and thus a series of networks in time slots static graph sequence Converted into a shadow-aware spatiotemporal graph, denoted as ,in It is the collection of all satellite nodes and ground station nodes in the network. It is the set of all inter-satellite and satellite-to-ground connections in the network; in a shadow-aware spatiotemporal graph In the diagram, nodes located on the sunlit side of the Earth are represented in white, and nodes located on the shadowed side of the Earth are represented in black. In shadow-aware spatiotemporal graphs In, it is defined Layers, each layer has There are n nodes, and the entire node set is represented as . And the picture There are a total of There are 10 nodes, which contain two types of connections: temporal connections and spatial connections; temporal connections... Connecting to the first Layer and first The same node of the layer Spatial connection Indicates the first Each time slot, node to its neighboring nodes Forward messages; by defining a shadow-aware spatiotemporal map, the transmission of any data and changes in the position information of the shadow and light sides of small satellites can be described.

4. The method of claim 1, wherein, The step S3 of establishing a small satellite energy model according to the satellite energy collection and consumption process specifically comprises the following steps: The battery energy consumption of the small satellite mainly consists of two parts; the first part is the basic energy consumed to maintain the system, which belongs to static energy consumption; the second part is the energy consumption brought by the small satellite observation data collection and data transmission, which belongs to dynamic energy consumption; the static energy consumption is: wherein is the static power, the dynamic energy consumption is: wherein , respectively represent satellite nodes in a time slot energy consumed for receiving data and energy consumed for transmitting data; the energy consumed for transmitting data and the energy consumed for receiving data are respectively represented as: Equation (3) and equation (4) introduce Boolean variables If the connection is activated, then , otherwise , is the transmit power of the satellite-to-ground link; and are the receive power and transmit power of the inter-satellite link, respectively; in space, laser communication links are used between satellites, and The relationship between them is: wherein , , , , , , are the transmit antenna gain, the receive antenna gain, the signal wavelength, the transmit optical component benefit, the receive optical component benefit, the off-axis gain and the distance between the transponders, respectively; the farther the distance between the two nodes, the greater the required transmit power, given the receive power and other parameters; In summary, the satellite node The energy consumed in the first time slot is: Satellite In The energy absorbed and stored by the time slot is : wherein is the absorption rate of solar energy by the satellite solar panels when represents the satellite node is located on the Earth's sun face when represents the satellite node is located on the Earth's sun face when represents the satellite node is located on the Earth's sun face when represents the satellite node is located on the Earth's sun face when represents the satellite node is located on the Earth's sun face when represents the satellite node is located on the Earth's sun face when Satellite In time slot Remaining energy at end time Is: wherein is a satellite the remaining energy at the end of the previous time slot; due to the limited capacity of the satellite battery, the remaining energy of the satellite node needs to satisfy the following constraint: wherein is the battery capacity of the small satellite, is the maximum depth of discharge of the small satellite battery; Finally, it is assumed that the ground station can work normally continuously.

5. The method of claim 1, wherein, The step S4 calculates the lowest energy consumption path between all node pairs , to obtain a connection subgraph based on the lowest energy consumption path , specifically comprising the following steps: Define the connectivity in the space-time graph based on shadow perception; A topology control algorithm based on shadow perception is proposed, and the basic steps are as follows: Step 1: Get the distances between all satellites in all time slots using STK software, and use a 3D matrix to represent , where represents the distance between node and node in the th time slot; based on the satellite energy model, pre-process the energy consumption of all potential connections according to the distance matrix ; Step 2: Traverse all pairs of nodes, in each round, select any pair of nodes , find the lowest energy path for the pair of nodes in the shadow-aware spatio-temporal graph using Dijkstra algorithm ; Step 3: Determine and establish the lowest energy consumption path If a connection satisfies the residual energy constraint, add it to the connection subgraph. If the condition is not met, then there is no solution for this case, and it is not within the scope of discussion; at the same time, two two-dimensional matrices are introduced. and Record small satellite nodes separately In the The maximum and minimum energy consumption for establishing an inter-satellite link in a time slot are only determined when the satellite node... The corresponding updates are only made when the link is established. When no link is established and The default value is 0; Step 4: Repeat Step 2 and Step 3, and perform all the lowest energy paths to the connection subgraph based on the lowest energy paths ; the connection subgraph guarantees that there is a usable directed path between all pairs of nodes while satisfying the remaining energy constraints.

6. The method of claim 5, wherein, The definition of connectivity in the space-time graph based on shadow perception specifically comprises: Definition 1: In the figure In the middle, if Includes slave nodes arrive The path, then the two nodes and The graphs are connected; if the graphs are connected. If every pair of distinct nodes in the graph is connected, then the graph is... They are connected; Definition 1 means that a shadow-aware spatiotemporal graph is connected in a cycle if and only if there is at least one directed path between every pair of nodes in the shadow-aware spatiotemporal graph in a cycle; this guarantees that any data can be delivered between any two nodes in the network in a cycle;​​​ To maintain the connectivity of the whole network and meet the energy requirement of small satellites, the lowest energy consumption path between any node pair is searched The connectivity subgraph constructed in this way still satisfies the connectivity of the network, but in order to meet the residual energy constraint of the satellite nodes at the same time, the initial energy reserve of the satellite is required to at least meet the establishment of the lowest energy consumption path; otherwise, the connectivity and residual energy constraints of the satellite cannot be met at the same time.

7. The method of claim 5, wherein, The step S5 specifically comprises the following steps: (1) Method for determining whether a new connection can be added ​ To add a potential inter-satellite link First, it is necessary to determine the satellite nodes. Does establishing this connection consume more energy than satellite nodes? In the The maximum energy that a time slot may consume. If a connection is established The energy consumed is less than Then determine the satellite node Will establishing this connection affect the current and future time slots, if satellite nodes... In the If a time slot has already been used as a receiving node or a transmitting node, then the connection can be established. Add directly to the connection subgraph But if satellite nodes In the If no time slot is used as a receiving node, then the satellite node needs to be determined according to formulas (8) and (9). The remaining energy is used to determine whether it is sufficient to establish the inter-satellite link. If it is, the remaining energy is sufficient to establish the inter-satellite link, and the determination is made on whether establishing the inter-satellite link will affect a future time slot. The remaining energy constraint requires that after the link is established, in each time slot from the next time slot to the end of the cycle, the satellite node... The remaining energy is still sufficient to establish connections with minimal power consumption, ensuring the satellite nodes... To ensure that inter-satellite connections can be established even when the remaining battery power is insufficient, the following constraints must be met: That is, the newly added connection needs to meet the residual energy constraint in the current time slot and the future time slot; Conversely, if the energy required to establish this inter-satellite link is greater than... Then it must be determined whether establishing the connection in the current time slot and future time slots would violate the remaining energy constraint; at this time, the satellite node The energy calculation method has changed, satellite nodes The remaining energy only needs to be subtracted Establish link Energy consumed and The difference between them, that is: Then, according to constraint (9), it is judged whether the residual energy of the satellite node is enough to establish the inter-satellite connection; if constraint (9) is satisfied, it is further judged whether the establishment of the inter-satellite connection will have an impact on a future time slot, and only when the node and both satisfy constraint (10) at the same time, the inter-satellite link is added to the connection subgraph . If a potential satellite-to-ground connection is added , it is only necessary to check whether the satellite node satisfies constraints (9) and (10), and then add the satellite-to-ground connection that satisfies the remaining energy constraints to the connection subgraph . (2) Traverse all time slots and select potential connections that meet the residual energy constraint.

8. The method of claim 7, wherein, The step S6 specifically comprises the following steps: (1) Transponder number constraint Each small satellite can only establish a transmission connection with one small satellite or a ground station at the same time, and needs to meet the following constraints: Equation (13) indicates that the established inter-satellite or satellite-ground connection is bidirectional; (2) Flow conservation constraint Small satellites are used in Earth observation missions. This represents the set of all real-time data collected by the small satellite within one period, where Indicates small satellite node v i Real-time data collected in the t-th time slot; using This represents the set of all non-real-time data collected by the small satellite within one period, where Indicates satellite node v i The non-real-time data collected in the t-th time slot, Represents all data streams during the acquisition and transmission process; defines For data stream f in connection The amount of data collected; flow conservation is described as the sum of all outgoing data streams and data streams in satellite memory equaling the total amount of data collected, i.e.: in This indicates the satellite node v. i The amount of data stored in data stream f at the end of time slot t, src(f) is the data stream f. i,t The source satellite, st(f) is the data stream f i,t The starting time slot; For data relay satellites, the following needs to be met: (3) Establish satellite memory capacity constraints Consider a practical network model, i.e. the space of satellite memory is limited; at the end of time slot t, the satellite node v i The total amount of all data stored cannot exceed its total memory capacity B max i.e.: (4) Establish link capacity constraints All from satellite node v i Converging to satellite node v j The data flow f cannot exceed the capacity of the corresponding link, that is: where C ss and C sg are the capacities of the inter-satellite and space-ground links, respectively.

9. The method of claim 8, wherein, The step S7 specifically comprises the following steps: (1) Build a connection plan design model Express the connection plan design problem as: Comply with: (2) Using the snake optimization algorithm to design the connection plan for different requirements of observation data.

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