A method, system and readable medium for network transmission of low-orbit satellite imaging data

Abstract

The present invention provides a method, system and readable medium for network transmission of low-orbit satellite imaging data, the method comprising: converting the satellite dynamic network topology into multiple static topology sets to obtain a spatiotemporal evolution graph model; constructing the objective function of the satellite imaging data network transmission model and determining the constraints; solving the local optimal solution in each time slot according to the time slot division, and then merging the local optimal solutions in each time slot as the optimal solution of the final objective function; wherein, for the local optimal solution in each time slot, it is divided into connection plan construction solution and data transmission solution, and the data transmission solution is solved based on the connection plan construction solution result. When transmitting a large amount of imaging data for low-orbit satellites, this scheme can reasonably plan limited resources to ensure that the imaging data is transmitted to the ground station as much as possible, thereby ensuring the integrity and effectiveness of the data.

Description

Technical Field

[0001] The present invention relates to the field of satellite data processing and satellite communication technology, and mainly to a conflict-free allocation scheme for satellite-borne resources and reasonable processing and planning of data transmission methods, and in particular to a networked transmission method, system and readable medium for low-orbit satellite imaging data. This scheme can be used as an efficient resource scheduling method for a low-orbit satellite network based on earth observation tasks, and can be used for earth observation systems of low-orbit satellite networks, etc. Background Art

[0002] Low-orbit satellites have the advantages of wide coverage, long observation time, and no airspace or national boundaries, and play a pivotal role in weather forecasting, disaster monitoring, natural resource exploration, etc. With the development of satellite technology, satellites are showing a trend of miniaturization and low cost, and are deployed on a large scale in low-Earth orbit.

[0003] With the development of satellite communication payload technology, low-orbit satellites can quickly build stable intersatellite links, greatly improving the satellite observation data transmission capacity. As the satellite observation capability is improved, it becomes an important task to transmit imaging data completely and accurately to the ground station and form image products in a timely manner.

[0004] Due to the high-speed movement of low-orbit satellites in space, the topology of intersatellite links changes frequently, forming a delay-tolerant network (DTN), resulting in almost no continuous stable end-to-end available links between the initial node and the target node. Satellite imaging data transmission based on DTN usually goes through three stages: store-carry-forward. The dynamically changing links and limited onboard storage result in large-capacity imaging data requiring multiple asynchronous storage and forwarding before it can reach the destination node. For example, suppose an imaging satellite needs to transmit a 10,000MB high-resolution image of the earth's surface to the ground station, while the transmission rate of the intersatellite link is 10MB / s and the transmission rate of the satellite-to-ground link is 20MB / s. Due to the unstable link between the satellite and the ground station, the intersatellite link is usually maintained for about 3 minutes. The maximum amount that can be transmitted to the ground station within a 3-minute stable link is 3600MB. Therefore, based on this situation, the satellite must transmit the data asynchronously through packetization, and once the receiving end receives all the packets, they can be reassembled into a complete high-resolution image. This packetization method makes data transmission more reliable and makes full use of available link resources. The image data can only be interpreted after all these sub-packets are transmitted to the ground and re-integrated. However, if any sub-packet is not successfully transmitted back to the ground, the image data cannot be interpreted and the imaging task will fail. However, there is currently no research on the imaging data transmission problem, or a mature solution to this problem.

[0005] Due to the characteristics of low-Earth orbit star networks, such as large transmission delays, discontinuous contacts between nodes, and limited physical resources of communication nodes, the concept of delay-tolerant networks (DTNs) has been proposed in the prior art. In DTNs, potential links between nodes are called contact points, and contact point topology (CT) is defined as the set of available contact points in a fixed network topology within a certain period of time. Contact plan design (CPD) is an in-depth study of how to connect in networks with limited resources. CPD considers the scheduling problem of conflicting contact nodes. Due to conflicts in resource use, not all potential contact nodes are active, which may be due to the need to establish links at each node. The set of all contact nodes after planning is called CP. In recent years, in order to continuously improve net profits and consider different constraints, many different contact selection criteria have been developed for DTN networks, resulting in a diversified and progressive development trend in contact plan design. According to different criteria of link plan, it can be roughly divided into routing-aware, energy-aware, traffic-aware, mission-aware and channel-aware connection plan designs. They make comprehensive considerations on network topology, routing conditions, energy conditions, inter-satellite traffic conditions, mission importance and channel conditions.

[0006] There are some studies on data transmission problems in DTN networks in the prior art. How to effectively characterize spatial information resources in the network is the key to the efficiency of problem solving. The representation of satellite connections in cyberspace usually adopts the model of Time Expended Graph (TEG), which usually decomposes a time period into multiple time slots with sufficiently small durations, and assumes that the topology structure does not change in each time slot, thereby converting the dynamic network topology into a combination of multiple static network topologies. At the algorithm design level, it includes both precise solution methods and non-precise solution methods such as heuristic methods and intelligent optimization methods. However, the existing solutions only consider the differences between tasks, but do not consider the delay and integrity generated during data transmission.

[0007] Comprehensively looking at the existing technologies and solutions, in recent years, the research on small satellite network data transmission problems has focused on how to reduce data transmission delays, improve network responsiveness, and maximize network throughput. Such problems have NP-hard characteristics. Data transmission problems are also usually modeled as a TEG or an extended TEG, but there is currently no CPD research on small satellite data transmission with data integrity as the entry point, and the transmission of imaging data has not considered or fully considered the integrity requirements. At the same time, there are few solutions for large-scale, large-scale imaging satellite transmission scheduling problems. Summary of the invention

[0008] In view of the shortcomings of the prior art, in order to at least partially solve the problems existing in the prior art, the present invention takes the integrity of satellite imaging data transmission as a starting point and proposes the following technical solutions for data transmission scheduling and other issues:

[0009] In a first aspect, the present invention provides a method for network transmission of low-orbit satellite imaging data, the method comprising:

[0010] S1, transforming the satellite dynamic network topology into multiple static topology sets to obtain a spatiotemporal evolution graph model;

[0011] S2. Construct an objective function of a satellite imaging data network transmission model and determine constraints; the constraints include node data flow conservation constraints, satellite connection quantity constraints, link transmission capacity constraints, and satellite energy constraints;

[0012] S3. According to the time slot division, solve the local optimal solution in each time slot respectively, and then merge the local optimal solutions in each time slot as the optimal solution of the final objective function; among them, the local optimal solution in each time slot is divided into connection plan construction solution and data transmission solution, and the data transmission is solved based on the connection plan construction solution result.

[0013] Preferably, the objective function in S2, after T time slots, that is, when t=T, is expressed as:

[0014] ;

[0015] in, Indicates the task The completeness of is the imaging task value factor set by the task control center, F represents the task set, T represents the total number of time slots t, Represents the overall objective function (Objective); the optimization goal of the function is to maximize the value of the complete mission data transmitted to the ground station after T time slots.

[0016] Preferably, the spatiotemporal evolution graph model is expressed as:

[0017] ;

[0018] Among them, V represents a node, and E represents the set of potential connections between links due to visibility;

[0019] In each time slot, V contains three types of nodes: satellite nodes , observe the target node , Ground Station Node ; E includes: the set of visible connections between the satellite and the mission target , Intersatellite Link Set , downlink set .

[0020] Preferably, in S2, the node data flow conservation constraint is: on the satellite node, for each task data flow, the out-degree of the data volume is equal to the in-degree, and the total amount of inter-satellite and satellite-to-ground link transmission does not exceed the maximum transmission value of the link;

[0021] The number of inter-satellite connections is constrained as follows: any satellite can only establish a connection with one satellite at most, and any satellite can only establish a downlink connection with one ground station at most, and any ground station can only establish a connection with one satellite at most;

[0022] The link transmission capacity constraint is: for each transmission link, the total amount of data transmitted in a single transmission does not exceed the upper limit of the link transmission capacity, and the total amount of data in the satellite buffer does not exceed the fixed storage capacity of the satellite;

[0023] The satellite energy constraint is: the remaining energy of the satellite at the beginning of time slot t is between the maximum energy of the satellite and the remaining energy of the battery depth set by the satellite; the remaining energy of the satellite is equal to the remaining energy at the beginning of the previous time slot minus the consumed energy, plus the obtained solar energy.

[0024] Preferably, in S3, solving the local optimal solution in each time slot respectively, and then merging the local optimal solutions in each time slot as the optimal solution of the final objective function, specifically includes:

[0025] S31, for the tth time slot, construct a feasible link set, calculate the scores of the feasible links in the feasible link set, select links based on the feasible link scores, and store the selected links in the selected link set sel;

[0026] S32, based on the selected link set sel and the constraints, derive Transmission scheme of time slots ,make The charge at the end of the time slot is , and set it equal to the power at the beginning of time slot t+1 , The buffer of each satellite at the end of the time slot is , and set it equal to the power at the beginning of time slot t+1 , guiding the construction of the connection plan for the t+1th time slot.

[0027] Preferably, in S3, the connection plan construction and solution are specifically as follows:

[0028] S311, in the tth time slot, traverse each potential link and calculate the score of each potential link; add the potential link to the candidate link list, and sort the links connecting the same ground station in descending order according to the score;

[0029] S312. Based on the result of S301, calculate the ground station importance factor of each ground station;

[0030] S313. Select links with higher scores from ground stations with higher ground station importance factors in turn to determine whether they meet the conflict judgment requirements, and insert links that meet the conflict judgment requirements into the selection link set sel until a complete conflict-free connection solution is constructed.

[0031] Preferably, in S311, the way of calculating the score of each potential link is:

[0032] a. When inter-satellite links and satellite-to-ground links exist at the same time:

[0033] ;

[0034] b. When there is no available connection between the ground station and the satellite:

[0035] ;

[0036] in, , ， ， ， ， ， is the weight factor, satisfying , + , , represents the data integrity factor of satellite i, represents the data integrity factor of satellite j, Denote the ground station node with index g; o indicates that there is no available connection between the ground station and the satellite; and respectively represent the i-th node and the j-th node in the t-th time slot;

[0037] = , is the satellite The maximum amount of solar energy that can be obtained, represents the maximum energy of the i-th satellite, represents the energy consumed by satellite i during normal operation, represents the remaining energy of satellite i in the (t - 1)-th time slot;

[0038] where is all the largest amount among them, represents the link from node to node The maximum transmission capacity;

[0039] , represents whether satellite i has data injection in the t-th time slot, represents the satellite at the time slot can observe the mission value, The meaning and solution of are similar.

[0040] Preferably, in the S312, the calculation method of the ground station importance factor is:

[0041] ;

[0042] where El represents the link set, represents the score of a certain link in contact with the ground station g, and n represents the total number of links connecting the ground station g, and respectively represent the i-th node and the j-th node in the t-th time slot.

[0043] Preferably, in the S3, the data transmission solution is specifically:

[0044] Based on the constraint conditions, solve the optimal solution when the objective function is maximized;

[0045] For the time slot t < T, the form of the objective function is expressed as:

[0046] ;

[0047] wherein, represents the completeness of the task , represents a very small constant, SGV represents the set of all satellites potentially connected to the ground station in the next time slot, G represents the set of ground stations, is the imaging task value factor set by the mission control center, F represents the set of tasks, represents the cache of task f owned by satellite i in the t-th time slot, represents the data cache capacity of satellite s in the t-th time slot, represents the task 's available data volume. When t < T, .

[0048] In a second aspect, the present invention also provides a low-earth orbit satellite imaging data networked transmission system. The system executes the low-earth orbit satellite imaging data networked transmission method as described above. The system includes:

[0049] A model transformation module for transforming the satellite dynamic network topology into a plurality of static topology sets to obtain a spatio-temporal evolution graph model;

[0050] An objective function establishment module for constructing the objective function of the satellite imaging data networked transmission model and determining the constraint conditions; the constraint conditions include node data flow conservation constraints, satellite interconnection quantity constraints, link transmission capacity constraints, and satellite energy constraints;

[0051] A solution module for solving the local optimal solutions in each time slot respectively according to the time slot division, and then merging the local optimal solutions in each time slot as the optimal solution of the final objective function; wherein, for the optimal solution of the objective function, it is split into connection plan construction solution and data transmission solution, and the data transmission solution is carried out on the basis of the connection plan construction solution result.

[0052] Preferably, the solution module further includes a connection plan construction unit and a data transmission solution unit; the connection plan construction unit is used to solve for the connection plan construction and construct a complete conflict-free connection scheme; the data transmission solution unit, based on the result of the connection plan construction unit and the constraint conditions, solves the optimal solution of the objective function by using a linear programming method.

[0053] In a third aspect, the present invention also provides a computer-readable medium. The readable medium stores instructions that can be called and executed by a computer system. When the instructions run, they execute a low-earth orbit satellite imaging data networked transmission method as described above.

[0054] Compared with the prior art, the prior art does not consider the integrity requirements of data during data transmission. When transmitting a large amount of imaging data for low-orbit satellites, the present invention can reasonably plan limited resources to ensure that the imaging data is transmitted to the ground station as much as possible, thereby ensuring the integrity and effectiveness of the data, which plays an important role in the future transmission of low-orbit satellite imaging data and the effective use of on-board resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 This is an example of data transmission mode for low-orbit satellite networks;

[0057] Figure 2 It is a schematic diagram of the spatiotemporal evolution graph model;

[0058] Figure 3 It is a block diagram of the heuristic solution method based on decomposition;

[0059] Figure 4 Quickly construct a schematic diagram for the link;

[0060] Figure 5 Schematic diagram of the system structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It should be clear that the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0062] Those skilled in the art should know that the following specific embodiments or specific implementations are a series of optimized settings listed by the present invention to further explain the specific content of the invention, and these settings can be combined or used in association with each other, unless the present invention clearly states that some or a specific embodiment or implementation cannot be associated or used together with other embodiments or implementations. At the same time, the following specific embodiments or implementations are only used as the most optimized settings, and are not to be understood as limiting the scope of protection of the present invention.

[0063] The purpose of the present invention is to solve the problem that in the process of transmitting a large amount of imaging data by low-orbit satellites, the imaging data is usually forwarded in fragments due to constraints such as satellite storage and energy, resulting in incomplete and unavailable data transmitted to the ground station. This solution can reasonably schedule limited on-board resources to ensure that the imaging data transmitted to the ground station is available and complete.

[0064] To facilitate the following description, some of the main symbols involved in this scheme and their definitions are listed in Table 1 (when multiple symbols are listed in parallel, the order of the symbols corresponds to the order of their meanings). We consider a resource-constrained low-Earth orbit satellite network, which consists of a satellite set S={s1,s2,s3,s4,…}, a ground station set G={g1,g2,g3,g4m…}, and an observation target O={o1,o2,o3,o4,…}; the link between satellites is called an inter-satellite link, and the link between a satellite and a ground station is called a downlink. In such a network, the satellite acquires imaging data, and then transmits the imaging data to the ground station through the inter-satellite link and the downlink. Combined with Figure 1 As shown, Figure 1 Two data transmission modes are given: one is that the satellite directly transmits the imaging data to the ground station after obtaining it; the other is that it is transmitted to the ground station through the intersatellite link. Among them, the double-arrow line represents the intersatellite link, the single-arrow line represents the satellite-to-ground link, and the line connecting target 1 (target1), target 2 (target2) and the satellite represents the acquisition of imaging data. In this embodiment, we assume that the resources of the ground station are not limited, and the ground stations can communicate with each other, so that the data packets of the same task from multiple ground stations can be aggregated.

[0065] Table 1 Symbols and their meanings

[0066]

[0067] In this solution, we propose an extended time evolution graph (ETEG) to formalize the LSIDTSP by considering a series of problem-specific constraints. The following is an explanation of the solution.

[0068] 1. Extended Time-expended graph (ETEG)

[0069] Since the orbits and relative positions of satellites can be accurately predicted by calculation, this scheme introduces the temporal evolution graph model (ETEG) to describe the network topology that changes over time, based on the predictability brought by the time-varying and periodic changes of the inter-satellite network structure. Since the network structure maintains a certain stability in a short period of time, we convert the dynamic network topology into several static topological states. The contact planning horizon is divided into continuous time periods, represented as , which is a set of time slots, so there are T time slots in total. The duration of each time slot is ,This transformation can improve the effectiveness and accuracy of network analysis to a certain extent.

[0070] It is assumed that the ground stations can communicate with each other. ETEG can be composed of Indicates, V represents nodes, and V in each time slot contains three types of nodes , , , represent satellite nodes, observation target nodes, and ground station nodes, respectively, and E represents the set of potential connections between links due to visibility, e.g. Respectively represent the set of potential connections between S to S (i.e., intersatellite links) and S to G (i.e., satellite-to-ground links) links due to visibility in each time slot. Through the temporal and spatial evolution graph (ETEG) model, a dynamic topology is transformed into a set of several static topologies, thereby reducing the complexity of the solution. The set of visible connections between the satellite and the mission target is represented by The intersatellite link set is represented by The table shows that the downlink set is Indicates that we use express , , The union of represents all topological connections.

[0071] We assume that the mission data is transmitted in the network in the form of data streams, and operations such as temporary storage, reception, and forwarding can be performed in the satellite. When the mission data volume is too large and does not match the required resources, data slicing and asynchronous transmission are allowed.

[0072] Combination Figure 2 As shown, Figure 2 (a) shows the connection topology within 3 time slots. In this example, the original dynamic problem is divided into two static sub-problems. In each sub-problem, the connection plan and data transmission plan need to be determined. However, this diagram only describes the physical visibility information in space and does not consider the constraints, so not all connections can be activated. Therefore, the connection plan is actually a subgraph of the connection topology graph. Figure 2 (b) is a connection plan, Figure 2 (c) is the final generated scheduling plan.

[0073] In summary, we use the time-space evolution graph (ETEG) model to model the dynamic network topology as a collection of several static topologies. On this basis, considering the constraints and objective function of the model, we determine an optimal data transmission scheduling scheme to maximize the network benefit under given time constraints. The mathematical model of LSIDTSP is constructed below.

[0074] 2. Construction of mathematical model of LSIDTSP

[0075] 1. Decision variables

[0076] The LSIDTSP problem requires determining the network connection plan and data transmission plan in each time slot. Therefore, in order to represent the connectivity of the link in each time slot, we introduce a binary integer variable To represent the link The connectivity situation, , if connected, it is 1, otherwise it is 0; introduce To represent the link Transfer task The amount of data introduced To reflect the task The degree of completion of the task If all data are transmitted to the ground station, it is 1, otherwise it is 0. and They represent the i-th node and the j-th node in the t-th time slot, respectively, where i S,j S∪G, where S represents the set of satellites.

[0077] 2. Objective Function

[0078] (1)

[0079] The optimization goal is to The value of the complete mission data transmitted to the ground station after time slots is maximized, where The imaging mission value factor set for the Mission Control Center (MMC), represents the objective function of the Tth time slot. The calculation formula of the objective function is shown in formula (1).

[0080] 3. Constraints

[0081] This section further elaborates on the constraints of the ETEG model to represent the limited resources of small satellite systems. Due to the limited resources of small satellites, energy is consumed in observation, storage, forwarding data and maintaining normal operation. At the same time, solar panels also obtain a certain amount of energy during orbital operation. In order to maintain the normal operation and sustainable use of satellites, different satellites have an energy lower limit, and there is also a maximum link capacity between each link. Indicates link Slave Node To Node The maximum transmission capacity of Indicates the amount of cached data of the satellite, represents the maximum cache capacity of satellite i. Assuming that the ground station resources are unlimited and can receive data infinitely, we set the constraints in the model as follows:

[0082] (1) Node data flow conservation constraints

[0083] Mission data is transmitted in the small satellite network in the form of data stream. Formula (2) represents that the capacity of the link between two nodes is equal to the transmission rate of the link multiplied by the time slot length. Formula (3) represents the injection of data. Assume that the amount of mission data acquired by the satellite in each time slot is (i.e. the initial amount of data injected into the network), Indicates the amount of data transmission between satellites. In this embodiment, the importance of each task is defined, where Represents the standardized value factor of each mission set by the Mission Management Center (MMC) to represent the value of different observation tasks.

[0084] (2)

[0085] (3)

[0086] Formula (4) indicates that the out-degree of satellite node data volume is equal to the in-degree. For each task data flow, the flow conservation law is followed. Represents a collection of tasks.

[0087] (4)

[0088] in, ;

[0089] Formula (5) shows that During the time gap, the total transmission volume of the inter-satellite and satellite-to-ground links shall not exceed the maximum transmission value of the link.

[0090] (5)

[0091] (2) Constraints on the number of satellite connections

[0092] Assume that the ground station has unlimited resources, so data storage is not limited, and the ground stations can communicate with each other. Since small satellites have limited resources, they carry a small number of transponders and receivers. Each satellite can only establish a link with one satellite and one ground station at most, so not all potential connections can be activated. Formulas (6)-(10) express this constraint.

[0093] As shown in formula (6), define a binary integer variable , to represent the state of potential connection, if it is connected, it is 1, otherwise it is 0.

[0094] (6)

[0095] Formulas (7) and (8) indicate that for any satellite, a connection can be established with at most one satellite.

[0096] (7)

[0097] (8)

[0098] Formula (9) indicates that for any satellite, a downlink connection can be established with at most one ground station.

[0099] (9)

[0100] Formula (10) indicates that any ground station can establish a connection with at most one satellite.

[0101] (10)

[0102] (3) Link transmission capacity constraints

[0103] For each transmission link, the total amount of task data transmitted in a single transmission cannot exceed the upper limit of the link transmission capacity, as shown in the following formula (11).

[0104] (11)

[0105] Formula (12) indicates that the total amount of data in each satellite buffer does not exceed the satellite's fixed storage capacity.

[0106] (12)

[0107] In the constraints of formula (13), It is a variable indicating the task integrity. When the task data is completely transmitted, the variable value is 1, otherwise it is 0. represents the amount of buffered data of task f on satellite i at time slot t.

[0108] (13)

[0109] (4) Satellite energy constraints

[0110] Satellites consume energy not only in the process of maintaining basic operations, but also when establishing communications with other satellites, forwarding and receiving data. The satellite's own solar panels can obtain some of the energy. Formulas (14), (15), (16), and (17) respectively represent the energy consumed by the satellite when collecting data, forwarding data, and receiving data. Formulas (18) and (19) represent the energy consumed to maintain normal operation. Formula (20) represents the solar energy obtained by the satellite. Where is the length of time in the shadow, that is, the length of time not exposed to sunlight. Formula (21) indicates that the satellite has a lower limit on the amount of electricity required to maintain its normal operation. The battery depth is set in advance.

[0111] (14)

[0112] Indicates satellite The energy consumed by forwarding data, where Represents the power of data forwarding between satellites, Represents the power of the satellite to offload data to the ground station. Represents link transmission task The amount of data.

[0113] (15)

[0114] Indicates satellite In the The energy consumed when obtaining forwarding data in the time slot, where Represents the power of data forwarding between satellites, Represents the transmission task of this link The following constraints are established for the amount of data.

[0115] (16)

[0116] Indicates satellite The energy consumed when acquiring imaging data, where represents the power of data forwarding between satellites, and x represents the transmission task of this link The amount of data, Represents the power of the satellite to acquire imaging data.

[0117] (17)

[0118] Indicates satellite The basic energy consumed to maintain normal operation, including: Represents the base power of the satellite.

[0119] (18)

[0120] (19)

[0121] The solar energy that the satellite can obtain. represents the duration that satellite i is in the shadow plane in time slot t.

[0122] (20)

[0123] Indicates time slot Satellite at the beginning The remaining energy is equal to the remaining energy at the beginning of the previous time slot minus the consumed energy plus the obtained solar energy, and satisfies formula (21), where: Represents the node set consisting of all satellites.

[0124] (twenty one)

[0125] 3. Sequential Two-Phase Heuristic Method (STPHA) for Solving LSIDTSP

[0126] Based on the above-mentioned spatiotemporal evolution graph model and problem decomposition, in order to reduce the complexity of the solution, we need to obtain the transmission scheduling scheme for each time slot, and the transmission schemes of adjacent time slots are tightly coupled. The scheduling scheme of the current time slot will affect all subsequent schemes. The planning and scheduling scheme generated by the current time slot will affect the scheduling results of a series of subsequent time slots. Therefore, we adopt a greedy strategy, only considering the local optimal solution of the current time slot, planning and scheduling the scheme for the current time slot, obtaining the maximum network benefit in the current time slot, and then planning the scheme for the next time slot according to the current scheduling result, and looping until the end of the last time slot, which also shows that the final solution to the problem is the union of the local optimal solutions of a series of time slots.

[0127] The above idea decomposes the LSIDTSP problem into a series of mutually coupled sub-problems TDTSSP. Therefore, the key to solving LSIDTSP is to solve the solution of TDTSSP and obtain the scheduling plan for the current time slot. , , that is, determine the set of links to establish connections and determine the amount of data transmission in the communication links.

[0128] In each time slot, TDTSSP can be further decomposed into two sub-problems, namely, the connection plan construction sub-problem (CPCP) and the data transmission plan generation sub-problem (DTPG). The link fast construction algorithm (LFCA) is used to solve CPCP and determine , and then according to the result of CPCP, i.e., the connection plan of the satellite network, the linear programming method is used to solve DTPG, i.e. .

[0129] Based on the above ideas, in this embodiment, a heuristic solution method based on decomposition is proposed. Figure 3 The details are as follows: First, for the input Time slot, according to the basic constraints of satellite and ground station, build a set of feasible links, select links and store them in the selected link set by calculating the scores of feasible links Then, according to the selected link set , and the constraints, solving the linear programming results in Transmission scheme of time slots , we order The charge at the end of the time slot is , and set it equal to the power at the beginning of time slot t+1 , The buffer size of each satellite at the end of the time slot is , and set it equal to the cache size at the beginning of time slot t+1 , guiding the connection plan design of the t+1th time slot, this framework can greatly reduce the complexity of the problem and improve the efficiency of solving. At the same time, in this embodiment, a linear programming is also designed to solve the DTPG problem.

[0130] 1. Solve the Link Fast Construction Algorithm (LFCA) of CPCP

[0131]

[0132] (a) Overall framework of the algorithm

[0133] This embodiment adopts the strategy of constructing a complete feasible link at one time, retaining as much global information as possible, and comprehensively using DIF (data integrity factor) and GIF (ground station importance factor). We first determine the order of connecting ground stations by the ground station importance factor, and then select links that do not conflict with existing solutions in turn according to the link ranking, and insert them into the link selection plan. middle.

[0134] Specifically, in the tth time slot, first, traverse each potential path, calculate the score of each potential link according to formula (24), add the link to the candidate link list, and sort the links connecting the same ground station in descending order according to the score, calculate the GIF of each ground station, and then based on the greedy idea, select the links with higher scores in the ground station with higher GIF in turn, and insert them into the link selection table after judging the conflict with the existing solution. Until a conflict-free connection plan is constructed. The pseudo code of the above algorithm is as follows:

[0135] Algorithm pseudocode: Link Fast Construction Algorithm (LFCA)

[0136] (b) Data integrity factor (DIF)

[0137] Due to the limited resources of small satellites, a single satellite can establish a connection with at most one satellite and one ground station in each time slot. Therefore, we define a triple to represent a potential connection. The link is composed of Composition, of which Indicates the satellite number connected to the ground station. Representation and The satellite number to which you are connected. express If there is no satellite and If connected, At the same time, considering the case where there is only an inter-satellite link and no satellite-to-ground link, the link structure is The data integrity factor (DIF) is used to measure the integrity of the remaining mission data in the satellite. The data integrity factor (DIF) of satellite i is calculated as shown in formula (23).

[0138] (twenty three)

[0139] in, In the tth time slot, The vector of tasks cached by satellites, where n is the number of tasks in the satellite cache, The DIF factor indicates that if The smaller the value, the less mission data is left in the satellite, indicating that it is easier to transmit it completely. The amount of transmitted data is divided by the total mission amount, and the integrity factor of each satellite is normalized. The larger the value, the greater the potential of the satellite to transmit data completely.

[0140] (c) Calculation of potential link value

[0141] According to formula (24), the scores of each link between the satellite and the ground station are calculated, and the connection with the highest score is found. The specific formula is as follows:

[0142] (twenty four)

[0143] In the above formula, , ， ， ， ， ， is the weight factor, which must satisfy , + , , represents the data integrity factor of satellite i, represents the data integrity factor of satellite j, Indicates the ground station node with index number g. = , It's a satellite The maximum amount of solar energy that can be obtained. ,in Yes all The largest amount, , Indicates whether satellite i has data injection in the tth time slot, Indicates satellite In the The task value that can be observed in the time slot is , its calculation method and meaning are similar to Similarly, those skilled in the art can directly know, and will not be repeated here. Formula (24) shows the trend of establishing a connection with a satellite with high power and high data integrity. Formula (24) represents the situation where the inter-satellite link and the satellite-to-ground link exist at the same time.

[0144] In the case where there is no available connection between the ground station and the satellite, the connection value metric is calculated according to formula (25).

[0145] (25)

[0146] In formula (25), , is the weight coefficient, o means there is no available connection between the ground station and the satellite (only the case of intersatellite link is considered). or satellite If there is a connection with the ground station in the next time slot, the connection value (ie, metric) is calculated; if there is no connection, it is 0.

[0147] (d) Ground station importance factor (GIF)

[0148] The ground station is limited by the transceiver. Any satellite can only establish communication with one ground station. However, it is possible that multiple satellites are visible to the same ground station at the same time, or multiple ground stations are visible to the same satellite. Therefore, it is necessary to select the ground station and introduce the importance factor (GIF) of the ground station. The priority of selecting the satellite connection is also an important factor affecting the transmission efficiency. We define the importance factor of the ground station as shown in formula (26). The importance factor of the ground station Defined as the score of all links in contact with ground station g The sum is divided by the total number of connections n.

[0149] (26)

[0150] in, To connect to the ground station The total number of links, Represents the score of a link in contact with ground station g.

[0151] (e) Algorithm Example

[0152] Combination Figure 4 In the example shown, in order to reduce the number of comparisons of links, in this embodiment, the connections containing the same elements in the triples are preferentially selected, for example, , , and their connection values ​​(metrics) are 0.93 and 0.85 respectively. Since they have exactly the same elements, choose and Any one of them has the same impact on the selection of subsequent links. Therefore, for ground station G1, retaining link L1 with a higher connection value (i.e., metric) score can reduce the number of comparisons in subsequent link construction.

[0153] Continue to combine Figure 4 As shown, assuming that In the time slot, for a satellite system with four ground stations, The relevant links have been arranged in descending order, and the importance of the satellite is calculated according to the satellite importance factor, resulting in The importances of are 0.87, 0.91, 0.65, and 0.55 respectively. Therefore, we first select The connection with the highest score Insert Link Selection Table Then select the ground station with the next highest importance, and judge the link and The conflict, though The highest score, but its In There is a conflict. The conflict judgment here is based on the constraint that each satellite can only establish a connection with one satellite, and a ground station can only establish a connection with one satellite, because and There is a common element s3, so there is a conflict between the two links, that is, s3 can only establish a connection with one of s2 and s4, so only one of the two links can be selected. Therefore, the link with the second highest score is selected. Then select the connection in g3 and g4, and you can get the complete and non-conflicting connection selected as , where 0 represents no satellite or ground station connected to it. After selecting all downlinks, select the link with only intersatellite links to determine whether there is the same element with sel. If there is no same element, it means there is no conflict, and the links with sel are selected in order. By inserting the non-conflicting links, the final connection selection vector can be obtained as follows:

[0154] ;

[0155] (f) Algorithm complexity

[0156] The LFCA algorithm proposed in this scheme needs to calculate the number of calculations of almost all complete links in each time slot. Each calculation needs to calculate the score of the feasible link. The calculation time is ( ,in, Indicates the number of satellites, represents the number of ground stations, and the complexity of path selection is ( ). Therefore, the algorithm performs The algorithm complexity of planning time slots is , its complexity is polynomial level complexity, and it has faster solving efficiency.

[0157] 2. Linear programming method for solving DTPG

[0158] According to the above framework, we can determine The CP (connection plan) in a time slot can be obtained by solving the following problem according to the determined CP. The data transmission plan within each time slot.

[0159]

[0160] ;

[0161] The above constraints can be set as the constraints of formulas (2) to (22). When t < T, the objective function is as shown in the following formula (27).

[0162] (27)

[0163] The integrity of each task in time slot t is represented by and is calculated using the following formula: . It mainly measures the completion degree of data in the ground station. The more data is transmitted, the higher the completion degree, and the closer it is to being complete. Therefore, the weight should also be larger. represents this state. represents an extremely small quantity to ensure that the objective function is non-zero. is a very small constant. represents the set of all satellites that potentially connect to the ground station in the next time slot. represents the data cache volume of satellite s in the t-th time slot. This item ensures that as much task data as possible is transmitted to the ground station.

[0164] When t = T, the objective function is as shown in the following formula (28).

[0165] (28)

[0166] IV. Simulation Scenario Verification

[0167] In another embodiment, we conduct simulation scenario verification for this solution.

[0168] 1. Scenario Setup

[0169] Since there is currently no publicly available benchmark and algorithms for the same problem, in order to test the performance of the algorithm proposed in this solution, simulation scenario experiments are adopted. A total of five groups of scenario experiments are set up. The satellite scales of the five groups of experimental scenarios are different, and all include four ground stations located in China. The 4 ground stations are located in Beijing (40°N, 116°E), Kashgar (39.5°N, 76°E), Sanya (18°N, 109.5°E) and Xi'an (34°N, 10°E) respectively. The orbital parameters of the satellites are shown in Table 2.

[0170] Table 2 Simulation Satellite Orbital Parameters

[0171]

[0172] We use a vector to represent the arrival of the task data volume, where Indicates the task number. Indicates the amount of data for the task, Indicates the satellite number for which mission data can be obtained. Indicates the time slot when the task arrives. For example, [1,12345,1,1] indicates that the total amount of complete data is 12345 Task 1 of the satellite arrives at the beginning of the first time slot. Randomly generated, for the convenience of observation, in this embodiment, the amount of data for each task is set to 12345 .

[0173] The network connection topology diagram is generated based on MATLAB R2018b software and STK 11.2 software. The number of tasks is set to 20 and the time slot period is set to The satellite downlink contact capacity follows an independent distribution in {0, 20, 40} The data transmission rate between S2S links is set to 50 The relevant parameters of the small satellite are set as follows: , , , , , , , .

[0174] The algorithm parameters are set with a span of 0.1. After taking into account the importance of each heuristic factor, the parameters are set as follows, where: =0.3, =0.3, =0.4, , , , .

[0175] 2. Experimental environment

[0176] All simulation experiments were completed on a computer with an Intel core i7 8750H CPU, 16.0GB RAM, and GTX1050Ti. In addition, the optimal solution of the problem was obtained by combining MATLAB R2018b, YALMIP toolkit (an open source and free exact solution toolkit that can be used in conjunction with many commercial solvers) and CPLEX 12.9 (a commercial solution software). All programs were compiled in MATLAB R2018b.

[0177] 3. Algorithm effect analysis

[0178] In order to show the effect of the algorithm proposed in this scheme, since the optimal solution is not obtained for more than 24 hours in the scenario where the number of satellites is greater than 20, this embodiment only considers scenario 1 to analyze the effect of the algorithm to clarify the gap between the algorithm proposed in this scheme and the optimal solution. It can be seen from Tables 3 and 4 that with the increase of the simulation horizon time, the network benefit shows a monotonous trend. In terms of the operation efficiency of the algorithm, LFCA has obvious advantages in solution time, and when the planning time slot exceeds 12, it reaches more than 85% of the optimal solution, and the time consumed is only one percent of the time consumed to solve the optimal solution.

[0179] Table 3

[0180]

[0181] Table 4

[0182]

[0183] Through gap analysis with the optimal solution, the effectiveness of the algorithm framework we proposed is verified, indicating that this algorithm can effectively solve the LSIDTSP problem.

[0184] In another specific embodiment, Figure 5 As shown, this solution can also be implemented in a system manner. The system can execute the low-orbit satellite imaging data network transmission method disclosed in the above embodiment, and the system includes:

[0185] A model conversion module is used to convert the satellite dynamic network topology into multiple static topology sets to obtain a spatiotemporal evolution graph model;

[0186] An objective function building module is used to build an objective function of a satellite imaging data network transmission model and determine constraints; the constraints include node data flow conservation constraints, satellite connection quantity constraints, link transmission capacity constraints, and satellite energy constraints;

[0187] The solution module is used to solve the local optimal solution in each time slot according to the time slot division, and then merge the local optimal solutions in each time slot as the optimal solution of the final objective function; among them, the optimal solution of the objective function is divided into connection plan construction solution and data transmission solution, and the data transmission solution is solved based on the connection plan construction solution result.

[0188] Preferably, the solution module further includes a connection plan construction unit and a data transmission solution unit; the connection plan construction unit is used to solve the connection plan construction and construct a complete conflict-free connection plan; the data transmission solution unit uses linear programming to solve the optimal solution of the objective function based on the results and constraints of the connection plan construction unit.

[0189] In another embodiment, the present solution can be implemented by means of a device, which may include a corresponding module for performing each or several steps in each of the above-mentioned embodiments. Therefore, each step or several steps of each of the above-mentioned embodiments can be performed by a corresponding module, and the electronic device may include one or more of these modules. The module may be one or more hardware modules specifically configured to perform the corresponding steps, or implemented by a processor configured to perform the corresponding steps, or stored in a computer-readable medium for implementation by a processor, or implemented by some combination. The device can be implemented using a bus architecture.

[0190] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0191] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for network transmission of low-orbit satellite imaging data, characterized in that: The method includes: S1. Convert the satellite dynamic network topology structure into multiple static topology sets to obtain a spatio-temporal evolution graph model; S2. Construct an objective function for the satellite imaging data networked transmission model and determine the constraint conditions; the constraint conditions include node data flow conservation constraints, satellite interconnection number constraints, link transmission capacity constraints, and satellite energy constraints; S3. According to time slot division, solve the local optimal solutions in each time slot respectively, and then merge the local optimal solutions in each time slot as the optimal solution of the final objective function; among them, for the local optimal solution in each time slot, it is split into connection plan construction solution and data transmission solution, and the data transmission solution is carried out based on the result of the connection plan construction solution; In S3, solve the local optimal solutions in each time slot respectively, and then merge the local optimal solutions in each time slot as the optimal solution of the final objective function, which specifically includes: S31. For the t-th time slot, construct a set of feasible links, calculate the scores of the feasible links in the set of feasible links, select links based on the scores of the feasible links, and store the selected links in the selected link set sel; S32, based on the selected link set sel and the constraint condition, derive the first Transmission scheme of time slots ,make The charge at the end of the time slot is , and set it equal to the power at the beginning of time slot t+1 , The buffer of each satellite at the end of the time slot is , and set it equal to the power at the beginning of time slot t+1 , guiding the construction of the connection plan for the t+1th time slot; In S3, the connection plan construction solution is specifically: S311. In the t-th time slot, traverse each potential link and calculate the score of each potential link; add the potential link to the candidate link list, and sort the links connecting the same ground station in descending order according to the scores; S312. Based on the result of S301, calculate the ground station importance factor of each ground station; S313. Sequentially select the links with higher scores in the ground stations with higher ground station importance factors, judge whether they meet the conflict judgment requirements, and insert the links that meet the conflict judgment requirements into the selected link set sel until a complete conflict-free connection scheme is constructed; In S3, the data transmission solution is specifically: Based on the constraint conditions, solve the optimal solution when the objective function is maximized; When the time slot t < T, the form of the objective function is expressed as: ； in, Indicates the task The completeness of represents a very small constant, SGV represents the set of satellites that have potential connection with the ground station in the next time slot, G represents the set of ground stations, is the imaging task value factor set by the task control center, F represents the task set, represents the mission that satellite i has in the tth time slot Cache, It represents the data buffer size of satellite s in the tth time slot.

2. The method according to claim 1, characterized in that The objective function in S2, after T time slots, that is, when t = T, the expression is: ； in, Indicates the task The completeness of is the imaging task value factor set by the task control center, F represents the task set, T represents the total number of time slots t, Represents the overall objective function; the optimization goal of the function is to maximize the value of the complete mission data transmitted to the ground station after T time slots.

3. The method according to claim 1, characterized in that In S2, the node data flow conservation constraint is: on the satellite node, for each task data flow, the out-degree of the data volume is equal to the in-degree, and the total inter-satellite and satellite-ground link transmission does not exceed the maximum transmission value of this link; The satellite interconnection number constraint is: any one satellite can establish a connection with at most one satellite, and any one satellite can establish a downlink connection with at most one ground station, and any one ground station can establish a connection with at most one satellite; The link transmission capacity constraint is: for each transmission link, the total amount of data transmitted each time does not exceed the upper limit of the link transmission capacity, and the total amount of data in the satellite buffer does not exceed the fixed storage capacity of the satellite; The satellite energy constraint is: the remaining energy of the satellite at the beginning of time slot t is between the maximum energy of the satellite and the remaining energy of the battery depth set by the satellite; the remaining energy of the satellite is equal to the remaining energy at the beginning of the previous time slot minus the consumed energy, plus the obtained solar energy.

4. The method according to claim 1, characterized in that: In S311, the method for calculating the score of each potential link is: a. When inter-satellite links and satellite-to-ground links exist at the same time: ； b. When there is no available connection between the ground station and the satellite: ； in, , ， ， ， ， ， is the weight factor, satisfying , + , , represents the data integrity factor of satellite i, represents the data integrity factor of satellite j, Indicates the ground station node with index number g; o indicates that there is no available connection between the ground station and the satellite; and They represent the i-th node and the j-th node in the t-th time slot respectively; = , Indicates satellite The maximum amount of solar energy that can be obtained, represents the maximum energy of the ith satellite, represents the energy consumed by the normal operation of satellite i, represents the residual energy of satellite i in the t-1th time slot; ,in, Yes all The largest amount, Indicates link Slave Node To Node The maximum transmission capacity of , Indicates whether there is data injection from satellite i in the tth time slot, represents the mission value that satellite i can observe in the tth time slot, The meaning and solution of resemblance.

5. The method according to claim 1, characterized in that In S312, the ground station importance factor is calculated as follows: ； in, represents a set of links, represents the score of a link in contact with ground station g, n represents the total number of links connecting to ground station g, and They represent the i-th node and the j-th node in the t-th time slot respectively.

6. A low-orbit satellite imaging data network transmission system, characterized in that: The system executes the low-orbit satellite imaging data network transmission method according to any one of claims 1 to 5, and the system comprises: A model conversion module is used to convert the satellite dynamic network topology into multiple static topology sets to obtain a spatiotemporal evolution graph model; An objective function building module is used to build an objective function of a satellite imaging data network transmission model and determine constraints; the constraints include node data flow conservation constraints, satellite connection quantity constraints, link transmission capacity constraints, and satellite energy constraints; The solution module is used to solve the local optimal solution in each time slot according to the time slot division, and then merge the local optimal solutions in each time slot as the optimal solution of the final objective function; among them, the optimal solution of the objective function is divided into connection plan construction solution and data transmission solution, and the data transmission solution is solved based on the connection plan construction solution result.

7. A computer-readable medium, characterized in that The readable medium stores instructions that can be called and executed by a computer system, and when the instructions are executed, a method for networked transmission of low-orbit satellite imaging data as described in any one of claims 1-5 is executed.

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