Big data transmission cost optimization method and system for mobile satellite network

Abstract

The invention provides a big data transmission cost optimization method and system for a mobile satellite network, and the method comprises the steps: constructing a first period residual image according to the changes of a network in N periods, calculating the transmission side capacity and cost, and optimizing the network resource distribution; secondly, determining the capacity of the path with the minimum cost and the maximum data transmission quantity; and then, comparing the transmitted data volume with the planned transmission data volume at the end of each time period, activating a new period time expansion graph when the data volume does not reach the standard, generating a residual graph, updating the transmission side capacity, and searching a path with the minimum cost to transmit data until no passable path from the source node to the destination node exists in the current time period, so that the data transmission reliability is improved. When the total amount of the transmission data reaches the standard, task delivery is completed, and a cost optimization scheme is obtained. According to the method, the transmission cost is considered while the total amount of the transmission data is maximized, the transmission cost is effectively reduced, and the transmission efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of routing, and more particularly to a method and system for optimizing the data transmission cost for a mobile satellite network. Background Art

[0002] Currently, there are many networked systems in which the topology structure and link costs of the underlying network change over time. Such a network system is called a time-varying network. A time-varying network is a network whose structure changes over time. In such a network, the connections between nodes may not be fixed but may appear or disappear over time, or their communication capabilities may change. This link cost characteristic is affected by various factors, including changes in the relative positions between nodes caused by the movement of nodes, the allocation of network resources, and the impact of the physical environment on wireless signals, etc.

[0003] Mobile satellite networks have achieved remarkable development in recent years. The development of this field is mainly due to technological progress, cost reduction, and the growing demand for global connectivity. Due to the movement of satellites in their orbits, mobile satellite networks have a certain degree of dynamism, and the communication windows between ground stations and satellites, as well as between satellites, change over time. In recent years, the satellite industry is rapidly building a large-scale network with thousands of satellites, and multiple large satellite constellations such as Starlink and Kuiper are under construction to achieve global coverage of the network. Satellite networks play many important roles in our daily lives, including weather forecasting, environmental monitoring, agricultural management, land use analysis, etc. Additionally, an upcoming application of satellite networks is to provide communication services for ground users. Satellite networks can provide high-throughput global coverage for ground users. Currently, there are still approximately 2.6 billion people globally who do not have access to the Internet, accounting for about 32% of the world's population. With the increase in the number of satellites in orbit and the utilization of inter-satellite communication, the network connectivity and performance between different regions can be significantly enhanced. In addition to commercial applications, governments of various countries have also increased their investment in satellite networks, aiming to improve national security levels and the ability to respond to emergencies. By establishing a more stable and reliable global communication infrastructure, communication links in affected areas can be quickly restored during natural disasters, which is crucial for rescue operations. Satellite networks provide connection solutions for Internet of Things devices in remote areas, promoting the digital transformation of multiple industries such as agriculture, energy, and transportation. Many countries have introduced policy documents to encourage and support the construction of satellite Internet, promoting the rapid development of this industry. Mobile satellite networks will become a key component in providing global communication access. In mobile satellite networks, the change in link costs not only affects the communication quality of tasks but also relates to the cost and efficiency of the entire task execution. Currently, mobile satellite networks face challenges in aspects such as cost control and user experience. Therefore, effective management and optimization of these factors are crucial for improving communication performance in time-varying networks during each transmission task. Due to the dynamic characteristics of mobile satellite networks, some links may only be available within a certain time period. Even when a link is available, the performance it provides may change over time. Previous studies on the task costs of networks were mostly under fixed network topologies and are not applicable to mobile satellite network systems. Summary of the Invention

[0004] To solve the problem of data transmission for tasks while optimizing the path cost, the present invention proposes a big data transmission cost optimization method and system for mobile satellite networks.

[0005] To achieve the above technical effects, the technical solution of the present invention is as follows:

[0006] A big data transmission cost optimization method for mobile satellite networks, comprising the following steps:

[0007] S1: For a time-varying network containing a node set, create a residual graph for the first time period according to its changes within N periods, and calculate the capacity and cost of each transmission edge;

[0008] S2: Determine the minimum-cost path, and obtain the maximum data transmission volume of the path according to the capacity of the minimum-cost path, and transmit the maximum data transmission volume allowed on the minimum-cost path;

[0009] S3: Record the data transmission volume that has passed at the end of the current time period. If the currently passed data transmission volume is less than the planned transmission data volume, activate the time-expanded graph of the next time period, generate the residual graph of the next time period at the same time, update the capacity of the transmission edges of the residual graph of the next time period, find the minimum-cost path on the transmission edges with a capacity not less than 0 and transmit data until there is no passable path from the source node to the destination node in the current time period;

[0010] S4: Repeat steps S2 - S3 until the currently passed data transmission volume is not less than the planned transmission data volume, complete the task delivery, and obtain the cost optimization scheme for task data transfer.

[0011] The present invention also provides a big data transmission cost optimization system for a mobile satellite network, including:

[0012] A transmission edge capacity and cost calculation module, which is used for a time-varying network containing a node set to create a residual graph for the first time period according to its changes within N periods, and calculate the capacity and cost of each transmission edge;

[0013] A maximum data transmission volume module for a path, which is used to determine the minimum-cost path, obtain the maximum data transmission volume of the path according to the capacity of the minimum-cost path, and transmit the maximum data transmission volume allowed on the minimum-cost path;

[0014] A data volume recording module, which is used to record the data transmission volume that has passed. When the passed data transmission volume at the end of each time period is less than the data volume required by the task, activate the time-expanded graph of the next time period, generate the residual graph of the next time period at the same time, update the capacity of the transmission edges of the residual graph of the next time period, find the minimum-cost path on the transmission edges with a capacity not less than 0 and transmit data until there is no passable path from the source node to the destination node in the current time period;

[0015] A scheme output module, which is used to complete the task delivery and output the cost optimization scheme for task data transfer when the passed data transmission volume is not less than the planned transmission data volume.

[0016] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0017] The present invention provides a method and system for optimizing the big data transmission cost in a mobile satellite network. First, for a time-varying network containing a node set, a residual graph of the first time period is created according to its changes in N periods, and the capacity and cost of each transmission edge are calculated to judge the transmission edge cost, thereby optimizing the network resource allocation. Secondly, the capacity of the minimum-cost path is determined, and the maximum data transmission volume of the path is obtained according to the capacity of the minimum-cost path. Then, the data transmission volume that has passed is recorded at the end of the current time period. If the currently passed data transmission volume is less than the planned data transmission volume, the time-expanded graph of the next time period is activated, and at the same time, a residual graph of the next time period is generated, and the capacity of the transmission edge of the residual graph of the next time period is updated. The minimum-cost path is searched for on the transmission edges with a capacity not less than 0 and data is transmitted until there is no path from the source node to the destination with available data transmission volume in the current time period, improving the reliability of data transmission. Finally, until the currently passed data transmission volume is not less than the planned data transmission volume, the task delivery is completed, and a cost optimization scheme for task data transfer is obtained. The present invention takes into account both the transmission cost while ensuring the maximization of the total transmission data volume, effectively reducing the transmission cost and improving the transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is the overall flowchart of the big data transmission cost optimization method for a mobile satellite network shown in the embodiments of the present invention.

[0019] Figure 2 It is a simplified satellite network diagram shown in the embodiments of the present invention.

[0020] Figure 3 It is the time-expanded graph corresponding to the satellite network diagram shown in the embodiments of the present invention.

[0021] Figure 4 It is the architecture diagram of the big data transmission cost optimization system for a mobile satellite network shown in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0023] The terms used in the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0024] It should be understood that although the terms first, second, third, etc. may be used in the present invention to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the present invention, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "when" or "while" or "in response to a determination".

[0025] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0026] Embodiment 1

[0027] This embodiment proposes a method for optimizing the big data transmission cost for a mobile satellite network, as Figure 1 shown, which is a flowchart of the method for optimizing the big data transmission cost for a mobile satellite network in this embodiment.

[0028] In a method for optimizing the big data transmission cost for a mobile satellite network proposed in this embodiment, the following steps are included:

[0029] S1: For a time-varying network including a node set, create a residual graph for the first time period according to its changes in N periods, and calculate the capacity and cost of each transmission edge;

[0030] S2: Determine the minimum-cost path, and obtain the maximum data transmission volume of the path according to the capacity of the minimum-cost path, and transmit the maximum data transmission volume allowed on the minimum-cost path;

[0031] S3: Record the data transmission volume that has passed at the end of the current time period. If the currently passed data transmission volume is less than the planned transmission data volume, activate the time-expanded graph of the next time period, and at the same time generate a residual graph for the next time period, update the capacity of the transmission edges of the residual graph for the next time period, find the minimum-cost path on the transmission edges with a capacity not less than 0 and transmit data until there is no passable path from the source node to the destination node in the current time period;

[0032] S4: Repeat steps S2 - S3 until the currently passed data transfer volume is not less than the planned transfer data volume, then complete the task delivery to obtain the cost - optimized solution for task data transfer.

[0033] In this embodiment, for a time - varying network containing a node set, a residual graph of the first time period is created according to its changes within N periods, and the capacity and cost of each transmission edge are calculated to judge the cost of the transmission edge, thereby optimizing the network resource allocation. Secondly, the capacity of the minimum - cost path is determined, and the maximum data transfer volume of the path is obtained according to the capacity of the minimum - cost path. Then, the passed data transfer volume is recorded at the end of the current time period. If the currently passed data transfer volume is less than the planned transfer data volume, the time - expansion graph of the next time period is activated, and at the same time, a residual graph of the next time period is generated, and the capacity of the transmission edges of the residual graph of the next time period is updated. The minimum - cost path is searched for and data is transmitted on the transmission edges with a capacity not less than 0 until there is no path from the source node to the destination with available data transfer volume in the current time period, improving the reliability of data transfer. Finally, until the currently passed data transfer volume is not less than the planned transfer data volume, the task delivery is completed to obtain the cost - optimized solution for task data transfer. The present invention takes into account both the transmission cost while ensuring the maximization of the total transmission data volume, effectively reducing the transmission cost and improving the transmission efficiency.

[0034] Overall, given a time range T, a time - varying network TN with a node set V* is given. For a network consisting of |V*| nodes, the network changes within N time periods (where the duration of each period can vary, but the network topology remains unchanged within each interval), and its corresponding time - expansion graph consists of a series of N sub - graphs, where each sub - graph represents a snapshot of the network within one period. Each node in V* can be a satellite or a ground station. Given a time - expansion graph G(N) and a task M, first, a residual graph G'(N) is initialized using G(N).

[0035] The time - expansion graph G(N) is defined as follows: Its vertex set V(G(N)) consists of N |V*| vertices. Specifically, each node u ∈ V* has N corresponding vertices in G(N), one for each time period. Let u k denote the representation of u in time period τ k . There are two types of edges in ε(G(N)): transmission edges and links between adjacent network snapshots. For a given time period τ i , if u can send data to v during τ i , then there is a transmission edge (u i , v i ) ∈ ε(G(N)). Further, the capacity of (u i , v i ) is defined as rG(N) (u i , v i )(or simply referred to as r(u i , v i )) which represents the maximum amount of data that can be transmitted from u to v in τ i , in bits. For a time period i, all vertices u i and their transmission edges form a snapshot graph G i . Use the time-expanded graph G(V*, N) (or simply G(N)) to represent the time-varying topology of the network TN from τ1 to τ N . Define G i (V) (or simply G i ) as the modeling of the snapshot graph in τ i . Additionally, use V(G) and ε(G) to represent the vertex set and edge set of a given graph G.

[0036] Additionally, define the residual time-expanded graph G'(N), which is based on the given time-expanded graph G(N), but models the additional information of the residual edge capacity after flow allocation. For the edge (u, v), initially r G′(2) (u, v) = r G(N) (u, v), and no data transmission volume is allocated, i.e., f(u, v) = 0. When a new flow f(u, v) is allocated to an edge (u, v), the capacity of this edge will be reduced by f(u, v) to reflect the "remaining" capacity of the edge. At the same time, add f(u, v) to the capacity of the reverse edge (v, u) to model the allocated data transmission volume. Using the reverse edge data transmission volume means that the corresponding data transmission volume allocated in the previous step can be reduced or removed in subsequent steps. The introduction of the reverse edge is an embodiment of the greedy algorithm, providing a method for data transmission volume exploration and return. Introduce the virtual source node S and the destination node D into G(N). Additionally, connect S to each s where 1 ≤ i ≤ N, and also connect each d where 1 ≤ i ≤ N to D. Solving the optimization problem of large data transmission cost essentially means using a subset of the graph G(N) to transmit as much data as possible on the paths with the lowest to highest costs from S to D until a certain time period meets the task data volume.

[0037] In an alternative embodiment, the S1 step includes the following steps:

[0038] When there is a transmission edge between nodes, calculate the cost of the transmission edge based on the amount of data transmitted by the transmission edge and the distance between the nodes; when there is no transmission edge between nodes, the cost is infinite; its expression is:

[0039]

[0040] where c(u i , vi ) represents the cost of transmission between node u and node v within the i-th time period, (u i , v i ) represents the transmission edge between node u and node v within the i-th time period, u i represents node u within the i-th time period, v i represents node v within the i-th time period, ε(G(N)) represents the transmission edges between the nodes of the time-expanded graph G(N), f(u i , v i ) represents the amount of data transmission that has passed between node u and node v within the i-th time period, w represents the distance, Y(w) represents the function of the impact of distance w on the link cost, and θ represents the weight coefficient of the distance factor.

[0041] Exemplarily, let |V*| represent the number of nodes in the network TN. Define a data transmission task M with the tuple M = {μ, s, d}, where μ represents the amount of data of task M, and s and d represent the source node and the destination node of the task respectively. Assume that each node has a large enough storage capacity to support task transmission. To capture the time-varying topology of such a network, a time-sharing mechanism is adopted to divide the given time range T into N small time periods T = {τ1,..., τ N} such that within any specific time period τ i ∈ T, neither any existing communication link will be disturbed nor any new communication link will be established. For example, given a set of satellite nodes and ground stations, for the time range T, the System Tool Kit (STK) software can be used to find the trajectories of the satellites and calculate the time information of all contacts between any two nodes (i.e., the time when two satellites are within each other's communication range or a satellite and a ground station are within each other's communication range).

[0042] Specifically, for any node u in V*, if there exists a transmission edge (u i , v i ), each data transmission of the task requires a certain cost. If there is no transmission edge between the nodes, the cost is set to ∞. Further define the cost as c x(N) (u i , v i ) (or simply denoted as c(u i , v i ). In different periods, the cost of each transmission edge is not necessarily the same as that of the previous time period.

[0043] Exemplarily, θ is the weight coefficient of the distance factor, used to adjust the degree of influence of distance on the total cost; the function Y(w) can be linear or non-linear. The length of the distance w affects the transmission power and energy consumption of node data, and the function is determined according to the actual situation.

[0044] In addition, attach links to adjacent snapshot graphs in the time-expanded graph. Specifically, consider two time periods τ i and τ i+1 . For any node u of V*, there is a link (u i , u i+1 ) connected between two adjacent time periods. If the task cannot be completed at the current time, the link (u i , u i+1 ) needs to be activated.

[0045] In an alternative embodiment, the maximum data transmission volume of the path is determined by the transmission edge with the minimum capacity of the path; the minimum-cost path is obtained according to the cost attribute of the transmission edge; the maximum data transmission volume of the path is obtained according to the capacity of the minimum-cost path; where:

[0046] The expression for the capacity of the minimum-cost path is:

[0047]

[0048] where r(P(s, d)) represents the capacity of the minimum-cost path, r(·) represents the path capacity, P(s, d) represents the path from node s to node d, s represents the source node of the task, d represents the target node of the task, r(u, v) represents the path capacity from node u to node v, u represents node u, and v represents node v.

[0049] Exemplarily, the data transmission volume of each path is determined by the maximum allowable data transmission volume on the path, that is, determined by the transmission edge with the minimum capacity on the path, and at the same time, the residual graph of the satellite's position prediction at the next time and the cost of the transmission edge can be considered.

[0050] In an alternative embodiment, the steps for determining the minimum-cost path are:

[0051] In the residual graph of the current time period, based on the cost attributes of each transmission edge, select the minimum-cost path of the current state using the SPFA algorithm; when there are paths with the same minimum cost, select the path with fewer transmission edges as the minimum-cost path.

[0052] The Edmonds-Karp algorithm is an algorithm for calculating the maximum data transmission volume of network transmission data. The SPFA (Shortest Path Faster Algorithm) algorithm selects paths according to the cost. SPFA is an algorithm for calculating the single-source shortest path. The present invention combines the two algorithms to consider the influence of the transmission cost factor while achieving the maximum possible data transmission volume.

[0053] The Edmonds-Karp algorithm introduces a residual path, which searches for a suitable residual path from s (source point) to t (destination point) each time to solve the maximum flow of data transmitted in the network.

[0054] The specific steps after combining the Edmonds-Karp algorithm with the SPFA algorithm are as follows:

[0055] ① Create a network residual graph, and the data transmission volume of each transmission edge of the initial network is 0;

[0056] ② Use the SPFA algorithm to find the shortest path from the source point s to the destination point t in the network graph, that is, the minimum cost path. This algorithm takes into account the cost attributes of the transmission edge and selects the path with the lowest cost in the current state each time. If there are multiple paths with the same minimum cost, the path with fewer edges is preferred;

[0057] ③ The maximum amount of data that can be transmitted on this path until there is no residual path between s and t;

[0058] ④Update the remaining capacity and add the reverse path;

[0059] ⑤ Repeat the above steps. If the task cannot be completed in the current time period, activate the network diagram for the next time period and accumulate and calculate the amount of network data transmitted until the task is completed.

[0060] The method of this embodiment uses the SPFA algorithm in path selection based on Edmonds-Karp, and selects the minimum cost path in the current state each time according to the cost attribute of the transmission edge. If there is a path with the same minimum cost, the path with fewer edges will be considered.

[0061] In an optional embodiment, the updating of the capacity of the residual graph transmission edge in the next time period includes the following steps:

[0062] When the transmission edges between the nodes of the residual graph do not contain reverse edges, the remaining capacity is the amount of data planned to be transmitted on the transmission edges;

[0063] When the transmission edge between the nodes of the residual graph contains a reverse edge, the residual capacity is calculated based on the path capacity of the reverse edge and the amount of data planned to be transmitted on the transmission edge; its expression is:

[0064]

[0065] Among them, u represents node u, v represents node v, r(v,u) represents the remaining capacity, f(u,v) represents the amount of data transmitted on the transmission edge (u,v), (v,u) represents the reverse edge, and (G′(N)) represents the transmission edge between the nodes of the residual graph.

[0066] Exemplarily, given a G'(N) and a new flow assigned to an edge (u, v) ∈ ε(G'(N)) with a capacity of f(u, v), the residual capacity r(u, v) of the edge is updated to r(u, v) = r(u, v) - f(u, v). It is worth noting that the currently lowest-cost path selected last time must have at least one path capacity become 0. Therefore, the low-cost paths selected each time must be different, avoiding conflicts caused by data transmission. At the next time, if the data transmission task specified in the previous time period is completed, or the path becomes available again, the capacity of the path must be no less than 0. The Edmonds-Karp algorithm is based on the Ford-Fulkerson (FF) algorithm and uses the special properties of the time-expanded graph to gradually modify the underlying graph during runtime. At the same time, to add reverse capacity, its residual capacity is updated according to whether the reverse edge (v, u) exists. If the edge being considered needs to be added to ε(G′(N)) first.

[0067] At the same time, calculate the optimized link cost:

[0068] OPT-COST(G(k),M):C

[0069] where OPT-cost represents the optimization of the path cost for each time period and satisfies the constraints of the data transmission cost optimization model.

[0070] In an alternative embodiment, the step S3 further includes the following steps: At the initial time, make the path capacity in the time-expanded graph the same as the path capacity in the residual graph, and obtain that the planned data volume to be transmitted on the path is 0; when a new planned data volume for transmission is assigned to the transmission edge, calculate the residual capacity of the transmission edge.

[0071] In an alternative embodiment, the step S3 further includes the following steps:

[0072] Construct an optimization model for data transmission cost in the time-expanded graph and calculate the currently passed data volume according to the data transmission cost optimization model; where:

[0073] The data transmission cost optimization model is specifically:

[0074]

[0075] Among them, u represents node u, v represents node v, f(u, v) represents the amount of data transmitted that has passed through on transmission edge (u, v), r(u, v) represents the capacity of transmission edge (u, v), (u, v) represents the transmission edge, G(k) represents the network graph at the k-th time, ε(G(k)) represents the transmission edges in the network graph at the k-th time, k represents the k-th time, V(G(k)) represents the number of nodes in the network graph of the k-th time period, s1 represents the first node of the task, d k represents the k-th target node of the task, F represents the total amount of data, f(s1, v) represents the amount of data transmitted that has passed through on transmission edge (s1, v), C represents the total cost of completing the task delivery, P(s, d) represents the path from node s to node d, s represents the source node of the task, d represents the target node of the task, c(u, v) represents the cost from node u to node v, and c(·) represents the cost;

[0076] Compare the currently passed data transmission amount with the total planned transmission data amount;

[0077] When the currently passed data transmission amount is greater than or equal to the total planned transmission data amount, complete the task delivery, and calculate the total cost of completing the task delivery according to the cost from node u to node v.

[0078] Exemplarily, from s1 to d k The optimization modeling of the data transmission cost in the time-expanded graph G(k) (with k time periods) from s1 to d is as above. F represents the total amount of data transmitted from s1 to d in G(k), f(u, v) represents the planned data transmission amount on edge (u, v), and C represents the total cost of completing the task delivery from s1 to d. k send, f(u, v) represents the planned data transmission amount on edge (u, v), and C represents the total cost of completing the task delivery from s1 to d. k send.

[0079] In an alternative embodiment, in the step S3, the finding of the minimum-cost path and data transmission on the transmission edge with a capacity not less than 0 includes the following steps:

[0080] When no feasible path is found in the current time period, activate the time-expanded graph of the next time period, and calculate the currently passed data transmission amount in the time-expanded graph;

[0081] Update the cost of the transmission edge, select the path with the minimum cost to transmit the maximum allowable data volume, and transmit the maximum allowable data volume on the path with the sub-optimal cost until the maximum data volume of the current network is reached.

[0082] Exemplarily, the purpose of the big data transmission cost optimization problem is for the mobile satellite network to complete the task delivery at low cost, and the network can provide at least μ data volume for task M within n time periods; its formula expression is:

[0083] OBDDC(G(N), M): C

[0084] s.t., F(G(n), M) ≥ μ

[0085] 1 ≤ n ≤ N

[0086] Where N is the number of time periods considered, task M can definitely be completed within N, n represents the number of periods required for the optimized task, G(n) represents that task M can be completed in the nth period, the network expansion graph of n time periods, and n can be much smaller than N.

[0087] This embodiment works in iterations relying on the special structure of the residual time expansion graph (using a hierarchical organization, with each layer corresponding to a time period). Starting from the time expansion graph of the first time period (i.e., n = 1), the Edmonds-Karp algorithm and the SPFA algorithm are run on it. If no feasible solution can be found before the currently considered time period, the algorithm will consider activating the next time period to expand the graph. Importantly, when expanding the graph, the total data transmission volume can be obtained directly by increasing the original data transmission volume, and there is no need to recalculate the capacity of each path. Specifically, in each iteration of considering the next time period, the algorithm does not start over, and only the cost of the transmission edge needs to be updated. According to the cost of the link, the present invention selects the path with the minimum cost to transmit the maximum allowable data volume, and then transmits the maximum allowable data on the path with the sub-optimal cost until the maximum data transmission volume of the current network is reached. If the task requirements cannot be completed at the current time, the next time network is activated to run the algorithm again until the data transmission volume reaches μ, that is, task M is completed.

[0088] Embodiment 2

[0089] This embodiment demonstrates a simplified satellite network. Specifically, as Figure 2 shown, a simplified satellite network is drawn, including an observation satellite s, four relay satellites a, b, c, e, and a ground destination d. The task is to transmit data from the observation satellite s to the ground station d. The time T is decomposed into {τ1, τ2, τ3}, where τ i = [t i-1 , t i . Based on this time-division mechanism, the corresponding time expansion graph is drawn, as Figure 3 shown, Figure 3It represents a time-expanded diagram corresponding to three time periods, which is composed of network snapshots of three time periods. Among them, the solid edges represent transmission edges. A transmission edge indicates that data can be transmitted between nodes. Each transmission edge has capacity and cost attributes, and the capacity and cost change over time. The dashed edges represent the links between two time periods for each node. When calculating the path cost of the next time period, the links between the nodes adjacent in time for each satellite or ground station are activated.

[0090] First, only generate the residual graph G’(1) based on G(1), that is, the residual graph of the first time period. For this purpose, initially, only set the capacities of the edges (S, s1) and (d1, D) to μ (i.e., the data volume of the task to ensure that these two edges will not be bottlenecks), while keeping the capacities of the edges (S, s i ) and (d i , D) as 0, where It should be noted that in the residual time-expanded graph G(N), the vertex set V(G′ i ) of the interval τ i and the vertex set V(G′ i+1 ) of the interval τ i+1 are only associated through links. At the beginning, there is no connection from G'(1) to the other parts of the graph corresponding to subsequent time periods. The algorithm first finds the order of path costs from low to high in this subgraph, and each time, as much data as possible is transmitted on the path with relatively low cost, obtaining the maximum available data transmission volume F.

[0091] If the data transmission volume F found based on the current subgraph is less than the target μ, the algorithm activates the next layer in G'(N). The activation of the next layer in the time interval τ i+1 only requires activating all corresponding links and setting the capacities of the two special edges (S, s i+1 ) and (d i , D) to μ. When all time periods need to be considered or the current data transmission volume F reaches the task volume μ, stop and output the flow allocation to obtain the optimized task cost scheme.

[0092] Embodiment 3

[0093] This embodiment proposes a big data transmission cost optimization system for mobile satellite networks, applying the big data transmission cost optimization method for mobile satellite networks proposed in Embodiment 1. As Figure 4 shown, it is the architecture diagram of a big data transmission cost optimization system for mobile satellite networks in this embodiment.

[0094] In the big data transmission cost optimization system for mobile satellite networks proposed in this embodiment, it includes:

[0095] A transmission edge capacity and cost calculation module, which is used for a time-varying network containing a node set to create a residual graph of the first time period according to its changes within N periods, and calculate the capacity and cost of each transmission edge;

[0096] A maximum data transmission volume module for a path, which is used to determine the minimum-cost path, obtain the maximum data transmission volume of the path according to the capacity of the minimum-cost path, and transmit the maximum allowable data transmission volume on the minimum-cost path;

[0097] A data volume recording module, which is used to record the passed data transmission volume. When the passed data transmission volume at the end of each time period is less than the data volume required by the task, it activates the time-expanded graph of the next time period, generates a residual graph of the next time period at the same time, updates the capacity of the transmission edges of the residual graph of the next time period, finds the minimum-cost path on the transmission edges with a capacity not less than 0 and transmits data until there is no passable path from the source node to the destination node in the current time period;

[0098] A solution output module, which is used to complete task delivery and output a cost optimization solution for task data transfer when the passed data transmission volume is not less than the planned transmission data volume.

[0099] It can be understood that the system in this embodiment corresponds to the method in Embodiment 1 above. The optional items in Embodiment 1 above are also applicable to this embodiment, so they will not be described repeatedly here.

[0100] Embodiment 4

[0101] This embodiment proposes a storage medium, on which computer-readable instructions are stored. Among them, when the computer-readable instructions are executed by a processor, all or part of the steps of a big data transmission cost optimization method for a mobile satellite network proposed in Embodiment 1 are implemented.

[0102] Exemplarily, the storage medium includes but is not limited to various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0103] Exemplarily, the instructions, programs, code sets, or instruction sets can be implemented using conventional programming languages.

[0104] Exemplarily, the processor includes but is not limited to smartphones, personal computers, servers, network devices, etc., and is used to execute all or part of the steps of a big data transmission cost optimization method for a mobile satellite network described in Embodiment 1.

[0105] Each embodiment in the present invention is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiments, since they are basically similar to the method embodiments, they are described relatively simply, and for the relevant parts, reference can be made to the descriptions in the method embodiments. The device embodiments described above are merely exemplary. The modules described as separate components may or may not be physically separated. When implementing the solution of the present invention, the functions of the modules can be implemented in the same or multiple software and / or hardware. It is also possible to select some or all of the modules according to actual needs to achieve the purpose of the solution of this embodiment.

[0106] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limiting the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made on the basis of the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. A method for optimizing the big data transmission cost for a mobile satellite network, characterized in that, It includes the following steps: S1: For a time-varying network containing a node set, create a residual graph for the first time period according to its changes in N periods, and calculate the capacity and cost of each transmission edge; S2: Determine the minimum-cost path, and obtain the maximum data transmission volume of the path according to the capacity of the minimum-cost path, and transmit the maximum data transmission volume allowed on the minimum-cost path; S3: Record the data transmission volume that has passed at the end of the current time period. If the currently passed data transmission volume is less than the planned transmission data volume, activate the time-expanded graph of the next time period, generate a residual graph for the next time period at the same time, update the capacity of the transmission edges of the residual graph for the next time period, find the minimum-cost path on the transmission edges with a capacity not less than 0 and transmit data until there is no passable path from the source node to the destination node in the current time period; S4: Repeat steps S2 - S3 until the currently passed data transmission volume is not less than the planned transmission data volume, complete the task delivery, and obtain an optimized cost plan for task data transfer.

2. The big data transmission cost optimization method for a mobile satellite network according to claim 1, characterized in that The S1 step includes the following steps: When there is a transmission edge between nodes, calculate the cost of the transmission edge based on the data volume transmitted by the transmission edge and the distance between the nodes; when there is no transmission edge between nodes, the cost is infinite; Its expression is: where, c(u i , v i ) represents the cost of transmission between node u and node v in the i-th time period, (u i , v i ) represents the transmission edge between node u and node v in the i-th time period, u i represents node u in the i-th time period, v i represents node v in the i-th time period, ε(G(N)) represents the transmission edges between the nodes of the time-expanded graph G(N), f(u i , v i ) represents the amount of data transmitted between node u and node v in the i-th time period, w represents the distance, Y(w) represents the function of the impact of distance w on the link cost, and θ represents the weight coefficient of the distance factor.

3. A big data transmission cost optimization method for mobile satellite networks according to claim 1, characterized in that The maximum data transmission volume of the path is determined by the transmission edge with the minimum capacity of the path; obtain the minimum-cost path according to the cost attribute of the transmission edge; obtain the maximum data transmission volume of the path according to the capacity of the minimum-cost path; where: The expression for the capacity of the minimum-cost path is: Among them, r(P(s,d)) represents the capacity of the minimum-cost path, r(·) represents the path capacity, P(s,d) represents the path from node s to node d, s represents the source node of the task, d represents the target node of the task, r(u,v) represents the path capacity from node u to node v, u represents node u, and v represents node v.

4. A method for optimizing the big data transmission cost for a mobile satellite network according to claim 1, characterized in that, The steps for determining the minimum-cost path are: In the residual graph of the current time period, based on the cost attributes of each transmission edge, select the minimum-cost path of the current state using the SPFA algorithm; when there are paths with the same minimum cost, select the path with fewer transmission edges as the minimum-cost path.

5. The big data transmission cost optimization method for a mobile satellite network according to claim 1, characterized in that In the update of the capacity of the transmission edges of the residual graph for the next time period, it includes the following steps: When the transmission edge between the nodes of the residual graph does not include a reverse edge, the remaining capacity is the data volume planned to be transmitted on the transmission edge; When the transmission edge between the nodes of the residual graph includes a reverse edge, calculate the remaining capacity based on the path capacity of the reverse edge and the data volume planned to be transmitted on the transmission edge; its expression is: Among them, u represents node u, v represents node v, r(v, u) represents the remaining capacity, f(u, v) represents the amount of data transmitted that has passed on the transmission edge (u, v), (v, u) represents the reverse edge, and ε(G ′ (N)) represents the transmission edge between the nodes of the residual graph.

6. The big data transmission cost optimization method for a mobile satellite network according to claim 4, characterized in that The S3 step also includes the following steps: At the initial time, make the path capacity in the time-expanded graph the same as the path capacity in the residual graph, and obtain that the planned transmission data volume on the path is 0; when a new planned transmission data volume is allocated to the transmission edge, calculate the remaining capacity of the transmission edge.

7. A method for optimizing the big data transmission cost for a mobile satellite network according to claim 1, characterized in that The S3 step also includes the following steps: Construct an optimized model for data transmission cost in the time-expanded graph, and calculate the currently passed data transmission volume according to the optimized model for data transmission cost; where: The specific data transmission cost optimization model is as follows: Among them, u represents node u, v represents node v, f(u, v) represents the amount of data transmission that has passed on the transmission edge (u, v), r(u, v) represents the capacity of the transmission edge (u, v), (u, v) represents the transmission edge, G(k) represents the network graph at the k-th time, ε(G(k)) represents the transmission edges in the network graph at the k-th time, k represents the k-th time, V(G(k)) represents the number of nodes in the network graph in the k-th time period, s1 represents the first node of the task, d k represents the k-th target node of the task, F represents the total amount of data that has passed in k times, f(s1, v) represents the amount of data transmission that has passed on the transmission edge (s1, v), C represents the total cost for task delivery, {(s, d) represents the path from node s to node d, s represents the source node of the task, d represents the target node of the task, c(u, v) represents the cost from node u to node v, c(·) represents the cost; Compare the currently passed data transmission volume with the total planned data transmission volume; When the currently passed data transmission volume is greater than or equal to the total planned data transmission volume, complete the task delivery, and calculate the total cost of completing the task delivery according to the cost from node u to node v.

8. A method for optimizing the big data transmission cost for a mobile satellite network according to claim 1, characterized in that, In step S3, finding the minimum-cost path on the transmission edge with a capacity of not less than 0 and transmitting data includes the following steps: When no feasible path is found within the current time period, activate the time-expanded graph of the next time period, and calculate the currently passed data transmission volume in the time-expanded graph; Update the cost of the transmission edge, select the path with the minimum cost to transmit the maximum allowable data volume, and transmit the maximum allowable data volume on the path with the sub-optimal cost until the maximum data volume of the current network is reached.

9. A big data transmission cost optimization system for mobile satellite networks, which applies the big data transmission cost optimization method for mobile satellite networks according to any one of claims 1 to 8, and is characterized in that, It includes: A transmission edge capacity and cost calculation module, which is used for a time-varying network containing a node set to create a residual graph of the first time period according to its changes in N periods, and calculate the capacity and cost of each transmission edge; A maximum data transmission volume module for the path, which is used to determine the minimum-cost path, obtain the maximum data transmission volume of the path according to the capacity of the minimum-cost path, and transmit the maximum allowable data transmission volume on the minimum-cost path; A data volume recording module, which is used to record the passed data transmission volume. When the passed data transmission volume at the end of each time period is less than the data volume required by the task, activate the time-expanded graph of the next time period, generate a residual graph of the next time period at the same time, update the capacity of the transmission edge of the residual graph of the next time period, find the minimum-cost path on the transmission edge with a capacity of not less than 0 and transmit data until there is no passable path from the source node to the destination node in the current time period; A solution output module, which is used to complete the task delivery and output the cost optimization solution for task data transfer when the passed data transmission volume is not less than the planned data transmission volume.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements a big data transmission cost optimization method for a mobile satellite network as described in any one of claims 1 to 8.

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