Chained content pre-placement method and system in LEO satellite network

Abstract

The invention provides a chained content pre-placement method and system in an LEO satellite network. The method comprises the following steps: acquiring content which needs to be pre-placed in an LEO satellite cache node; splitting the content needing to be pre-placed in the LEO satellite cache node to obtain content blocks needing to be pre-placed in the LEO satellite cache node; performing global search optimization by using a heuristic algorithm in a search space comprising all candidate schemes for pre-placing the content blocks, and performing iteration to obtain a content block pre-placing scheme which is optimal or approximately optimal on a set index; wherein the set index comprises content cache update flow cost and / or content placement balance degree cost; and according to the content block pre-placement scheme, a chained storage structure is adopted to place the content blocks needing to be pre-placed in the LEO satellite cache nodes to the corresponding LEO satellite cache nodes.

Description

Technical Field

[0001] This invention relates to the field of content storage technology for satellite edge computing networks, and in particular to a chained content pre-placement method and system in LEO satellite networks. Background Technology

[0002] With the rapid growth of emerging multimedia content and mobile data services, the structure of internet traffic is undergoing profound changes. In particular, video content traffic accounted for 65% of total internet traffic in 2022. [1] This proportion continues to climb. Emerging applications such as video streaming services, online games, and remote work and learning platforms are constantly driving up the demand for network bandwidth and stability. These applications require networks capable of supporting large-scale, high-speed content transmission and ensuring a high-quality user experience, especially under high-concurrency access. Meanwhile, the rapid development of the Internet of Things (IoT) is also driving exponential growth in data traffic. Thousands upon thousands of sensors and smart devices are constantly generating data that needs to be transmitted, processed, and stored in the network in real time. This change makes the need for high-bandwidth, low-latency, and high-reliability network services increasingly urgent. To adapt to this trend, network architecture needs profound changes to support more complex and real-time-critical application scenarios.

[0003] Content Delivery Networks (CDNs) have been widely used in traditional terrestrial networks. [2] CDN has become a crucial technology supporting internet traffic. By pre-deploying content from remote central servers to distribution nodes closer to users, CDN significantly reduces user access latency, improves content distribution efficiency, and enhances user experience. Although terrestrial CDNs have been operating for many years and have played a vital role globally, some challenges remain from a global perspective. Specifically, users in remote areas may experience higher round-trip latency from the nearest CDN node due to geographical and population density factors. [3] The root cause of this problem is the lack of sufficient infrastructure in remote areas, where sparse populations make it difficult to build cost-effective CDN nodes. Furthermore, the terrain obstacles and high construction costs of terrestrial networks also pose numerous challenges to the deployment of terrestrial CDNs, making it difficult for users in these areas to obtain low-latency network services.

[0004] With the continuous development of satellite communication technology, Satellite-Terrestrial Integrated Networks (STIN) have become an important development direction for future global network architecture. STIN aims to compensate for the shortcomings of traditional terrestrial networks in remote areas by combining terrestrial and satellite networks. As an emerging network technology, edge computing can provide users with nearby computing resources by extending cloud computing platforms to the network edge and even mobile devices themselves. In the context of satellite-terrestrial edge computing, European satellite operator Eutelsat and satellite internet company OneWeb have begun exploring satellite CDN solutions within STIN. The construction of Low Earth Orbit (LEO) satellite networks provides a new direction for improving global communication networks. [4] [5] By deploying high-capacity content storage and caching facilities on LEO satellites, these satellites can become global "airborne cache nodes," providing users worldwide with fast and efficient content distribution services. This satellite-ground collaborative (or satellite-ground hybrid) content delivery network not only compensates for the shortcomings of terrestrial CDNs but also provides low-latency content distribution globally, greatly enhancing the user experience.

[0005] However, despite the potential of satellite-ground collaborative content delivery networks (CDNs) for global communication services and low-latency content distribution, they still face many challenges in practical applications. First, the high dynamics of LEO satellites due to their high-speed motion in space orbits lead to frequent link interruptions, affecting communication stability. The constant changes in satellite position and orientation make the continuity and stability of the link between satellites and ground stations a critical issue. These frequent topology changes not only affect the effectiveness of end-to-end content distribution but also have a profound impact on the quality of services such as real-time applications and multimedia transmission. Second, due to the limited resources of satellites, a single satellite can only carry a limited amount of cached content, posing a challenge to effectively store and manage content. Furthermore, satellite communication costs are much higher than terrestrial communication, meaning that LEO satellites must comprehensively consider communication costs, storage costs, and network bandwidth when caching content. Finally, designing efficient content placement is a critical issue that urgently needs to be addressed. While ensuring the effective utilization of satellite resources, it is also necessary to meet the access needs of users in different regions. These challenges require the development of new algorithms and strategies to optimize satellite network resource allocation.

[0006] Currently, the application of heuristic algorithms for pre-placement of chained content in LEO satellite networks is still in its early stages. Further development of content pre-placement schemes for LEO satellites to achieve efficient utilization of LEO satellite resources remains a gap. Therefore, this invention focuses on designing a content pre-placement method for LEO satellite scenarios to improve the performance and reliability of LEO satellite services, enabling flexible pre-placement of chained content and efficient utilization of satellite resources. Summary of the Invention

[0007] In view of this, embodiments of the present invention provide a chained content pre-placement method and system in a LEO satellite network to eliminate or improve one or more defects existing in the prior art.

[0008] One aspect of the present invention provides a chained content pre-placement method in a LEO satellite network. The method includes the following steps: obtaining content to be pre-placed on LEO satellite cache nodes; decomposing the content to be pre-placed on LEO satellite cache nodes to obtain content blocks; performing a global search optimization using a heuristic algorithm in a search space including all candidate schemes for content block pre-placement, iteratively obtaining a content block pre-placement scheme that achieves optimal or near-optimal results on a set metric; wherein the set metric includes content cache update traffic cost and / or content placement balance cost; and placing the content blocks to be pre-placed on LEO satellite cache nodes to the corresponding LEO satellite cache nodes using a chained storage structure according to the content block pre-placement scheme.

[0009] In some embodiments of the present invention, the step of obtaining the content to be pre-placed on the LEO satellite cache node includes: obtaining the content to be pre-placed, dividing the content to be pre-placed into content to be pre-placed on the ground cache node and content to be pre-placed on the LEO satellite cache node; distributing the content to be pre-placed on the ground cache node to the ground cache node using the ground CDN network; wherein the ground cache node is managed by the ground CDN network.

[0010] In some embodiments of the present invention, the step of dividing the content to be pre-placed into content to be pre-placed on ground cache nodes and content to be pre-placed on LEO satellite cache nodes includes: performing a popularity analysis on each content to be pre-placed, selecting content with a popularity exceeding a preset threshold to be pre-placed on ground cache nodes, and classifying content with a popularity not exceeding the preset threshold as the content to be pre-placed on LEO satellite cache nodes.

[0011] In some embodiments of the present invention, the step of disassembling the content to be pre-placed on the LEO satellite cache node to obtain content blocks to be pre-placed on the LEO satellite cache node includes: setting an upper limit on the size of a single piece of content according to the requirements of chained storage, disassembling the content exceeding the upper limit into content blocks, and using pointers of the chained storage structure to link the content blocks together.

[0012] In some embodiments of the present invention, prior to the step of performing global search optimization using a heuristic algorithm, the method further includes: obtaining the state parameters of each LEO satellite cache node, evaluating the state of the LEO satellite cache node based on the state parameters, and using the state evaluation result of the LEO satellite cache node as a resource constraint condition for performing global search optimization using a heuristic algorithm; the state parameters include some or all of the following: the current geographical location of the LEO satellite cache node, cache capacity, cache capacity usage, satellite mobility, satellite orbit parameters, inter-satellite link bandwidth, and satellite-to-ground link bandwidth; the state of the LEO satellite cache node includes: remaining available cache capacity.

[0013] In some embodiments of the present invention, the heuristic algorithm is an atomic search optimization algorithm; the step of using the heuristic algorithm to perform global search optimization and iteratively obtain a content chunk pre-placement scheme that achieves optimal or near-optimal results on a set metric includes: initially generating multiple candidate schemes for content chunk pre-placement to form a solution space; in the atomic search optimization algorithm, an atom represents a candidate scheme for content chunk pre-placement; wherein, the dimension of the atom is the product of the number of content chunks and the number of LEO satellite cache nodes, and each dimension represents the caching status of the content chunks on the LEO satellite cache nodes; using a fitness value function as... The objective function of the atomic search optimization algorithm is defined as follows: the fitness value of each atom is calculated to measure the merits of the corresponding content chunking pre-placement scheme; wherein, the fitness value function covers the content cache update traffic cost and the content placement balance cost; during the iteration process, the fitness value of each atom is calculated, and the atomic attraction and repulsion are calculated based on the fitness value; based on the atomic attraction and repulsion, the velocity and position of the atom are updated; the iteration terminates when a preset number of iterations is reached or the current optimal content chunking pre-placement scheme reaches a preset performance threshold, thus obtaining the optimal or near-optimal content chunking pre-placement scheme in terms of the set indicators.

[0014] In some embodiments of the present invention, the LEO satellite cache node is managed by the LEO satellite CDN network, and the LEO satellite CDN network is used to implement the step of using a chained storage structure to divide the content that needs to be pre-placed in the LEO satellite cache node into blocks and place them in the corresponding LEO satellite cache nodes.

[0015] In some embodiments of the present invention, after the content to be pre-placed on the LEO satellite cache node is divided into blocks and placed on the corresponding LEO satellite cache node using a chained storage structure, the method further includes: the LEO satellite cache node and / or the ground cache node supporting receiving and responding to content acquisition requests from ground terminal users.

[0016] Corresponding to the above methods, the present invention also provides a chained content pre-placement system in a LEO satellite network, characterized in that the chained content pre-placement system includes a content distribution and storage system deployed on LEO satellites and a server cluster deployed on the ground, for implementing the steps of any of the methods described in the above embodiments.

[0017] In some embodiments of the present invention, the content distribution and storage system deployed on LEO satellites is a LEO satellite CDN network, the server cluster deployed on the ground is a terrestrial CDN network, and the LEO satellite cache nodes and / or terrestrial cache nodes support receiving and responding to content requests from terrestrial end users.

[0018] The chain-based content pre-placement method in the LEO satellite network proposed in this invention can divide the content into blocks according to access frequency and relevance, and pre-distribute these content blocks to various cache nodes using a chain-based storage structure, which is beneficial for achieving fast access and efficient distribution.

[0019] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0020] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. In the drawings: Figure 1 This is a flowchart of a chain-style content pre-placement method in a LEO satellite network according to an embodiment of the present invention.

[0022] Figure 2 This is a diagram of the existing STAR FRONT content caching architecture.

[0023] Figure 3This is a diagram of the architecture of a LEO satellite multicast system with existing double caching technology.

[0024] Figure 4 This is a structural diagram of a satellite-ground collaborative chain-type content pre-placement system in one embodiment of the present invention.

[0025] Figure 5 This is a structural diagram of a satellite-ground collaborative chain-type content pre-placement system according to another embodiment of the present invention.

[0026] Figure 6 This is a flowchart of a chain-like content pre-placement method based on a heuristic algorithm in one embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0028] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0029] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0030] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0031] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0032] Existing technology 1 introduces a cost-effective content caching framework (STAR ​​FRONT) that optimizes a global CDN and enables low-latency content access anywhere. This framework collaboratively builds a CDN on emerging LEO constellations and existing cloud platforms to meet low-latency requirements while minimizing operational costs. Specifically, STAR FRONT leverages an emerging mega-constellation of thousands of LEO satellites, equipped with high-speed data links and storage, to achieve ubiquitous and low-latency data access. The framework places replicas on LEO satellite caches or terrestrial cloud caches and dynamically distributes user requests to appropriate cache servers based on different constellation parameters, cloud / user distribution, and pricing strategies.

[0033] Figure 2 This is a diagram of the existing STAR FRONT content caching architecture. Figure 2 As shown, the caching system consists of LEO satellites and ground stations, forming a multi-layered, collaborative network structure. The introduction of this system architecture aims to improve the utilization, robustness, and flexibility of content caching, providing a new research approach for performance optimization of satellite communication systems.

[0034] The existing technology has the following drawbacks: (1) The system needs to coordinate between the dynamic LEO satellites and the existing cloud platform. Although the STARFRONT framework looks promising in theory, it may encounter significant challenges in actual deployment and operation. These include integration issues with the existing Internet infrastructure, coordination issues between satellites and ground stations, and the complexity of maintaining such a distributed system. In addition, due to the high dynamism of LEO satellites, the system needs to be able to quickly adapt to the constantly changing network topology, which may put pressure on the stability and reliability of the system. (2) In large-scale CDN systems, content updates and synchronization may face many challenges, such as network latency, bandwidth limitations, and content version conflicts. The existing technology does not fully discuss these challenges and corresponding solutions, which is crucial to ensuring that CDN nodes can provide the latest content. CDN nodes need to handle a large number of user requests and data transmission tasks, so load balancing and resource allocation have become important technical challenges. The existing technology does not discuss in detail how to implement efficient load balancing algorithms and dynamic resource allocation strategies, which is crucial to optimizing CDN performance and improving resource utilization. (3) Although the LEO satellite network provides a new global coverage layer, it still relies on ground infrastructure, such as ground stations and data centers. Existing technologies may not fully account for the vulnerabilities of these ground infrastructures. For example, natural disasters, war, or other unforeseen events may damage or disable ground stations, thereby affecting the operation of the entire network. Therefore, how to maintain network continuity and resilience when ground infrastructure is damaged is an issue that requires further investigation.

[0035] Existing technology 2 proposes an efficient content caching method for LEO satellite networks, aiming to minimize content delivery time by jointly optimizing beam coverage, bandwidth allocation, and transmission power. This study leverages the flexibility of multi-beam LEO satellites and the geographical distribution of users to optimize the performance of an LEO satellite-assisted edge caching system. Specifically, beam coverage is optimized using the K-means algorithm, and radio resource allocation is optimized using an iterative algorithm based on the Continuous Convex Approximation (SCA). Simulation results show that this method significantly outperforms traditional precoding methods in minimizing the maximum content delivery time and substantially reduces the average content delivery time at different return rates.

[0036] Figure 3 This is a diagram of the existing technology's second-cached LEO satellite multicast system architecture. (Example:) Figure 3 As shown, the caching system proposed in this scheme consists of LEO satellites, gateways, and a central cloud, forming a multi-layered, collaborative network structure. The LEO satellites are equipped with flexible payloads, capable of generating multiple arbitrary-shaped spot beams to serve single-antenna user equipment within the coverage area. Each spot beam can support multiple spatially multiplexed data streams to meet the content requests of different user groups. The gateway, acting as a communication bridge between the LEO satellites and the central cloud, is responsible for data forwarding and management. The central cloud stores a rich content library. Based on user needs and caching strategies, the LEO satellites cache some content locally to reduce dependence on the central cloud and lower content delivery latency. This system architecture aims to improve the utilization, robustness, and flexibility of content caching, providing a new research approach for performance optimization of satellite communication systems.

[0037] The existing technology 2 has the following problems: (1) Although the existing technology 2 can theoretically significantly reduce content delivery time, it may face many challenges in practical applications. The rapid movement of LEO satellites may cause frequent switching of the connection between user equipment and satellites, which not only increases the complexity of the system, but may also cause signal interruption and transmission delay. The solution does not fully discuss these practical problems and their solutions, which may limit the application of the framework in practical scenarios. (2) The solution mainly focuses on how to improve content delivery efficiency by optimizing beam coverage and wireless resource allocation, but does not fully consider the dynamic nature of user requests and the timeliness of content. In practical applications, users' demand for content is constantly changing, and the updating and synchronization of content may be affected by factors such as network latency and bandwidth limitations. The solution does not discuss in detail how to implement an efficient cache update strategy in a dynamic environment, which is crucial to ensuring that users can obtain the latest content. The solution also does not fully consider how to deal with sudden high traffic requests, which may lead to a decrease in system performance and affect user experience. (3) Although the solution proposes a method to improve content caching performance by utilizing the LEO satellite network, this method still relies on ground infrastructure, such as gateways and central cloud. The proposed solution does not adequately address how to maintain system continuity and resilience when ground infrastructure is damaged, which may limit its robustness in practical applications. Furthermore, it lacks detailed discussion on how to achieve efficient resource allocation and management under different network topologies and user distributions, which is crucial for system scalability and flexibility.

[0038] Therefore, this invention aims to solve the chain-style content pre-placement optimization method for satellite-ground collaborative CDN in LEO satellite networks, mainly focusing on solving the following three technical problems: (1) Exploring how to reasonably divide large-scale content into blocks so as to achieve more efficient data storage, transmission and retrieval in satellite-ground collaborative CDN. By dividing the content into multiple small blocks, the redundant transmission and storage pressure of single content can be reduced, while supporting more flexible content scheduling and updates, thereby improving the user request response speed and network resource utilization efficiency. (2) Further focusing on the chain-style placement and update strategy. In satellite-ground collaborative CDN, chain-style content placement can intelligently select the content placement position between nodes according to the content access frequency, latency requirements and network topology, ensuring that users can obtain the required data from the nearest node when making a request. This strategy reduces the data transmission distance, optimizes the use of network bandwidth, and greatly improves the system response speed and user experience. (3) Proposing an optimization strategy based on the Atom Search Optimization (ASO) algorithm, which aims to solve the complex problems in content placement and resource scheduling. It can efficiently search and optimize the location of content storage, achieve optimal content distribution, thereby reducing network congestion, improving data transmission rate, and enabling adaptive adjustment in changing network environments to ensure system stability and high performance.

[0039] Figure 1 This is a flowchart of a chain-style content pre-placement method in a LEO satellite network according to an embodiment of the present invention. The method includes the following steps: Step S110: Obtain the content that needs to be pre-placed on the LEO satellite cache node.

[0040] Step S120: Decompose the content to be pre-placed on the LEO satellite cache node to obtain content blocks to be pre-placed on the LEO satellite cache node. Step S130: In the search space including all candidate schemes for content chunk pre-placement, a heuristic algorithm is used for global search optimization to iteratively obtain the optimal or near-optimal content chunk pre-placement scheme on the set indicators; wherein, the set indicators include content cache update traffic cost and / or content placement balance cost. Step S140: According to the content block pre-placement scheme, the content that needs to be pre-placed on the LEO satellite cache node is divided into blocks and placed on the corresponding LEO satellite cache node using a chain storage structure.

[0041] The chain-based content pre-placement method in the LEO satellite network proposed in this invention can divide the content into blocks according to access frequency and relevance, and pre-distribute these content blocks to various cache nodes using a chain-based storage structure, which is beneficial for achieving fast access and efficient distribution.

[0042] In some embodiments of the present invention, the step of obtaining the content to be pre-placed on the LEO satellite cache node includes: obtaining the content to be pre-placed, dividing the content to be pre-placed into content to be pre-placed on the ground cache node and content to be pre-placed on the LEO satellite cache node; distributing the content to be pre-placed on the ground cache node to the ground cache node using the ground CDN network; wherein the ground cache node is managed by the ground CDN network.

[0043] By adopting this embodiment of the invention, the newly designed multi-level optimization mechanism facilitates satellite-ground collaboration, enables the resources of satellite and ground networks to work together, optimizes the pre-placement of content, and ensures efficient utilization of cache and balanced distribution of network load.

[0044] In some embodiments of the present invention, the step of dividing the content to be pre-placed into content to be pre-placed on ground cache nodes and content to be pre-placed on LEO satellite cache nodes includes: performing a popularity analysis on each piece of content to be pre-placed, selecting content with a popularity exceeding a preset threshold to be pre-placed on ground cache nodes, and classifying content with a popularity not exceeding the preset threshold as the content to be pre-placed on LEO satellite cache nodes.

[0045] By employing this invention embodiment, through this multi-level optimization mechanism, satellite cache nodes are used to prioritize storing non-hot content, while terrestrial CDN is used to process static or hot content. This facilitates the intelligent selection of the optimal content pre-placement location among different levels of caching based on the distribution of user requests, the popularity of content, and network load. This helps ensure efficient cache utilization and balanced distribution of network load, thereby improving the response speed of user requests and the user experience.

[0046] In some embodiments of the present invention, the step of disassembling the content to be pre-placed on the LEO satellite cache node to obtain content blocks to be pre-placed on the LEO satellite cache node includes: setting an upper limit on the size of a single piece of content according to the requirements of chained storage, disassembling the content exceeding the upper limit into content blocks, and using pointers of the chained storage structure to link the content blocks together.

[0047] By employing this embodiment of the invention, content is divided into blocks and pre-placed using a chained storage structure, enabling intelligent updates between cache nodes. This helps to cope with changes in content access and limitations of cache space. This mechanism can significantly improve cache hit rate and reduce content transmission latency.

[0048] In some embodiments of the present invention, prior to the step of performing global search optimization using a heuristic algorithm, the method further includes: obtaining the state parameters of each LEO satellite cache node, evaluating the state of the LEO satellite cache node based on the state parameters, and using the state evaluation result of the LEO satellite cache node as the resource constraint condition for performing global search optimization using a heuristic algorithm. The state parameters include some or all of the following: the current geographical location of the LEO satellite cache node, cache capacity, cache capacity usage, satellite mobility, satellite orbit parameters, inter-satellite link bandwidth, and satellite-to-ground link bandwidth. The state of the LEO satellite cache node includes: remaining available cache capacity.

[0049] By employing this embodiment of the invention, a content chunking pre-placement scheme suitable for current resource constraints can be dynamically generated in response to changes in current content access and limitations in cache space. This helps reduce content transmission latency, and is particularly beneficial in resource-constrained satellite communication networks, where it can optimize network performance and improve user experience.

[0050] In some embodiments of the present invention, the heuristic algorithm is an atomic search optimization algorithm.

[0051] In some embodiments of the present invention, the step of using a heuristic algorithm to perform global search optimization and iteratively obtain a content block pre-placement scheme that achieves optimal or near-optimal results on a given metric includes: (1) Initially generate multiple candidate schemes for pre-placement of content blocks to form a solution space. In the atomic search optimization algorithm, an atom is used to represent a candidate scheme for pre-placement of content blocks. The dimension of the atom is the product of the number of content blocks and the number of LEO satellite cache nodes. Each dimension represents the caching status of content blocks on LEO satellite cache nodes.

[0052] (2) The fitness value function is used as the objective function of the atomic search optimization algorithm. The fitness value of each atom is calculated to measure the merits of the corresponding content block pre-placement scheme. The fitness value function covers the content cache update traffic cost and the content placement balance cost.

[0053] (3) During the iteration process, calculate the fitness value of each atom, calculate the attraction and repulsion of the atom based on the fitness value, and update the velocity and position of the atom based on the attraction and repulsion of the atom.

[0054] (4) When the preset number of iterations is reached or the current optimal content block pre-placement scheme reaches the preset performance threshold, the iteration is terminated, and the optimal or near-optimal content block pre-placement scheme is obtained in terms of the set indicators.

[0055] This invention, by introducing an atomic search optimization algorithm, enables efficient content caching and allocation in large-scale satellite networks. Through reasonable initialization, fitness calculation, quality update, and attraction / repulsion calculation steps, this method helps ensure the efficiency of the search process and the acquisition of the optimal solution, providing a practical technical solution for content pre-placement in LEO satellite networks.

[0056] In some embodiments of the present invention, the LEO satellite cache node is managed by the LEO satellite CDN network, and the LEO satellite CDN network is used to implement the step of using a chained storage structure to divide the content that needs to be pre-placed in the LEO satellite cache node into blocks and place them in the corresponding LEO satellite cache nodes.

[0057] By employing this embodiment of the invention, content can be distributed using a CDN network, ensuring that LEO satellites can receive data updates in a timely manner.

[0058] In some embodiments of the present invention, after the content to be pre-placed on the LEO satellite cache node is divided into blocks and placed on the corresponding LEO satellite cache node using a chained storage structure, the method further includes: the LEO satellite cache node and / or the ground cache node supporting receiving and responding to content acquisition requests from ground terminal users.

[0059] By employing this embodiment of the invention, the collaboration between terrestrial CDN and satellite CDN also ensures redundant backup of content, further improving the availability and reliability of content.

[0060] Figure 4 This is a structural diagram of a satellite-ground collaborative chain-type content pre-placement system according to an embodiment of the present invention. Figure 4 As shown, the modeling process for the heuristic algorithm-based satellite-ground collaborative chain-type content pre-placement method is as follows.

[0061] The LEO satellite constellation is characterized by a large number of satellites, a distributed network structure, rapid dynamic changes, and frequent inter-satellite interactions. In practical applications, LEO satellites typically need to collaborate with neighboring satellites to complete content storage and pre-placement tasks, rather than relying solely on local satellites to perform all operations. However, the satellite network topology, resource distribution, and user requests exhibit highly dynamic changes in both time and space, increasing the difficulty and overhead of resource management for LEO satellites. Therefore, to address these challenges, a distributed content pre-placement network structure for the LEO satellite constellation is proposed, such as... Figure 4 As shown, this structure consists of two main parts: end users, a terrestrial CDN network, and a LEO satellite CDN network.

[0062] like Figure 4As shown, end users refer to devices or clusters of devices using the space-ground collaborative CDN service. This may include personal terminals on the ground (such as smartphones, computers, etc.), mobile devices (such as vehicles, drones, etc.), and marine and aerial platforms (such as ships, aircraft, etc.). End users are represented as U={1,…,U}. The set of requested content can be defined as F={1,…F}, and the size of file f can be represented as… End users send content requests to the satellite-terrestrial collaborative CDN network through various access methods. These requests may be for static content (such as videos, images, and web pages) or dynamic content (such as application data and real-time information), and the spatiotemporal distribution of these requests changes as users move. In a chained content pre-placement scenario, end users do not directly request content from the data source but instead quickly obtain content through content storage and pre-placement solutions from both satellite and terrestrial CDNs. As end-user needs dynamically change, the system must be able to store the required content in the optimal location and transmit it efficiently via the shortest path, ensuring both timeliness and access speed.

[0063] like Figure 4 As shown, the LEO satellite CDN network is a key component of the space-ground collaborative CDN, referring to the content distribution and storage system deployed on LEO satellites. LEO satellites are dynamic and periodic; assuming the repetition period of the LEO satellite network is... The cycle can be Divided into multiple time slots This scheme considers an inclined orbit constellation, with four inter-satellite links: two links from satellites in the same orbit and the other two links from satellites in adjacent orbits. This refers to the LEO satellite constellation. The primary task of a satellite CDN is to collaborate with terrestrial CDNs to store, transmit, and distribute content based on end-user needs and pre-placement plans. LEO satellites use chained content pre-placement to distribute popular content from the terrestrial CDN to multiple satellite nodes within the constellation. When an end-user makes a request, the satellite can directly provide content stored locally or on nearby satellites, reducing latency with the terrestrial CDN. The core advantages of satellite CDNs lie in their wide coverage and even node distribution, enabling global content distribution. Furthermore, the high-speed mobility of LEO satellites allows for flexible chained content pre-placement, dynamically adjusting content distribution strategies based on network traffic, content demand, and satellite orbits. Satellite CDNs effectively alleviate pressure on terrestrial networks and improve user experience, especially in remote areas and mobile environments, providing stable, low-latency content services.

[0064] The terrestrial CDN network is another important component of the space-ground collaborative CDN architecture, mainly responsible for providing source content and initial content storage. This represents a set of terrestrial CDN servers. Terrestrial CDNs are typically deployed on server clusters around the world, possessing large-scale storage capacity and high-speed network connections, capable of acquiring, storing, and updating large amounts of data from content sources. In the chained content pre-placement scenario of satellite-terrestrial collaborative CDN, the terrestrial CDN is responsible for the source distribution and initial caching of content. Through cooperation with satellite CDNs, the terrestrial CDN supports the pre-placement and distribution of content to LEO satellites. When an end-user's request cannot be found on a satellite CDN node, the satellite requests the source content from the terrestrial CDN, which then transmits the content to the satellite or satellite cluster, thus completing the final content distribution. The terrestrial CDN is also responsible for regularly updating and caching the content, ensuring that the satellite CDN can obtain the latest data in a timely manner. Simultaneously, the collaboration between the terrestrial and satellite CDNs ensures redundant backup of the content, further improving its availability and reliability. This invention uses a graph to represent the network topology, defining infrastructure and devices in the network as endpoints, with the endpoint set represented as... .

[0065] Figure 5 This is a structural diagram of a satellite-ground collaborative chain-type content pre-placement system according to another embodiment of the present invention.

[0066] like Figure 5 As shown, the location of terrestrial CDNs is relatively constant, making frequent content updates unnecessary. However, for LEO satellite networks, due to their high dynamism and resource constraints, optimally updating content to achieve the best utilization of space resources is a crucial challenge. This invention layers the LEO satellite CDN and terrestrial CDN, considering popularity when placing content, and proposes a layered proportional placement mechanism (LPPM). LPPM first analyzes the popularity distribution information of content, selecting content with relatively high popularity for caching in the lower-level terrestrial CDN, while the remaining content is chained in the upper-level LEO satellite CDN, improving cache utilization through chaining. We set a variable threshold. Select the top popularity percentage The content is placed on terrestrial caching servers. High-popularity content is deployed across all terrestrial CDNs before low-popularity content is deployed to satellites.

[0067] exist Figure 5Based on the disclosed content, this invention introduces an ASO-based heuristic algorithm into the chained content pre-placement method. By dynamically adjusting the cache placement of satellite-to-ground CDN nodes, it can adapt to situations involving high-speed satellite movement and rapidly changing user requests. The introduction of ASO not only reduces communication latency and improves data transmission speed but also effectively reduces energy consumption and improves the overall system efficiency. By adopting a distributed content placement scheme, it is beneficial to achieve a more flexible and efficient response to dynamic computing requests in the satellite network. The ASO-based heuristic algorithm facilitates the optimization of content pre-placement, possessing advantages such as strong optimization capabilities and fast convergence speed.

[0068] Figure 6 This is a flowchart of a chain-like content pre-placement method based on a heuristic algorithm in one embodiment of the present invention. (Combined with...) Figure 6 This section provides a complete overview of the chained content pre-placement method. For example... Figure 6 As shown, the method includes the following steps: Step S610: Obtain all pre-placed content for the current region. Based on the user's access requirements, the system obtains a list of content related to the region.

[0069] Step S620: By analyzing the popularity of the content, the system classifies the content into those placed on ground cache nodes and those placed on satellite cache nodes.

[0070] Step S630: For content that needs to be placed in the satellite cache node, the system performs block processing, breaking down large volumes of data into smaller units.

[0071] Step S640: The system assesses the current status of each satellite cache node, including the geographical location and storage capacity usage of each satellite node.

[0072] Step S650: The system constructs a chain-like content pre-placement mechanism based on heuristic algorithms, enabling the satellite network to achieve optimal content placement under resource constraints.

[0073] Step S660: Based on the generated placement strategy, the system performs content pre-placement. According to the predetermined strategy, the content will be deployed to the appropriate cache nodes. After placement is complete, the system can quickly distribute requested content to users.

[0074] The large-scale content placement problem can be modeled as an integer linear programming problem, which is NP-hard and therefore cannot be efficiently solved using conventional algorithms in multi-node and large-scale networks. Furthermore, considering the chained placement of content, content chunking needs to be distributed and coordinated across multiple LEO satellites, further increasing the problem's complexity. To address this issue, this paper proposes a heuristic algorithm based on Atom Search Optimization (ASO). ASO is a novel intelligent algorithm based on a molecular dynamics model. It simulates the displacement of atoms in a molecular system due to mutual forces and system constraints. In a molecular system, there are interaction forces (attraction and repulsion) between adjacent atoms, and the globally optimal atom exerts geometric constraints on other atoms. Gravity drives atoms to explore the entire search space extensively, while repulsion enables them to effectively develop potential regions. The algorithm can cache and allocate content more efficiently to meet multiple optimization objectives, finding near-optimal solutions in a shorter time. This algorithm effectively addresses the challenges of content pre-placement in large-scale satellite networks and significantly reduces computational complexity.

[0075] In a specific embodiment of the present invention, the generation process of the chained content pre-placement scheme based on the ASO algorithm is as follows: 1) ASO initialization candidate solutions (i.e., modeling and initialization of large-scale content placement problems): Candidate solutions for chained content deployment are represented by multiple atoms. Each atom represents a possible content placement scheme. Each atom is defined as a vector. ,in This indicates whether the i-th atom caches a content chunk on the n-th satellite. The dimension of an atom is the product of the number of content chunks and the number of satellite cache nodes; each dimension represents the caching status of the content chunk on the satellite. During initialization, the atom's position and velocity are assigned randomly.

[0076] 2) Calculate the fitness value function for ASO (i.e., when the heuristic algorithm is ASO, the fitness value function is used as the objective function): The fitness value of each atom is used to measure the quality of the corresponding content chunking and pre-placement scheme. The set of atom fitness values ​​can be represented as follows: A lower fitness value indicates a better solution, better meeting the needs of content distribution and caching. During the iteration process, atoms with lower fitness will receive more search opportunities, driving the search process towards a better solution.

[0077] In this scheme, the fitness value function encompasses both the content caching update traffic cost and the content placement balance cost. The formula for the fitness value function is as follows: It is expressed as follows: in, This represents a performance coefficient indicating the cost of content caching and updating traffic, as well as the cost of balancing content placement. This indicates the cost of updating content cache. This represents the cost of content placement balance.

[0078] 3) Calculate the global best and worst fitness values: In the search space, the change in velocity or acceleration of any atom is equal to the force acting on the atom divided by the mass of the particle. In ASO, obtaining the global best fitness and the individual best fitness is essential. Represents atoms In time The fitness value (Fit value) is calculated in each round. The globally optimal fitness can be obtained through calculation. and worst fitness .

[0079] 4) Updating Atom Mass: All atoms possess a certain mass. At the simplest level, atomic mass can be evaluated using its fitness value; the better the fitness value of the function, the greater the atom's mass, which can reduce its acceleration. In time... atom The quality is defined as : 5) Identifying Neighboring Atoms: To enhance the exploratory nature of the first iteration phase, each atom needs to interact with as many and as few neighboring atoms with better fitness values ​​as possible. To improve mining in the final iteration phase, an atom needs to interact with as few atoms as possible with better fitness values ​​than its neighbors. Therefore, K is a function of time, gradually decreasing with the number of iterations, and can be calculated as follows: .

[0080] 6) Calculate the attractive and repulsive forces between atoms: Gravity It can be considered as time In the The attraction of each dimension acts on the atom The resultant force on the d-th dimension. The Leonard-Jones potential (LJ potential) is used as the interaction force from the j-th atom in the d-th dimension to the i-th atom at time t. Atoms maintain a relative distance that varies within a certain range due to repulsion or attraction; the repulsion relative to the equilibrium distance varies much more than the attraction. The attraction is negative, and the repulsion is positive, so atoms will not converge to a specific location. Kbest is a subset of the population of atoms consisting of the first K atoms with the best functional fitness values. Then, the sum of the components with random weights in the d-th dimension acting on the i-th atom from other atoms can be considered as a resultant force, expressed as: in It is a random number in [0,1]. Geometric constraints in molecular dynamics play an important role in atomic motion. In ASO, for simplicity, it is assumed that each atom has a covalent bond with the optimal atom, and therefore each atom is subject to the constraint force of the optimal atom. Thus, the constraint force can be defined as: The Lagrange multiplier is expressed as... , It is the multiplier coefficient.

[0081] 7) Calculate acceleration, update velocity and position: Update the velocity and position of the atom based on the effects of attraction and repulsion. time No. Acceleration in each dimension It can be written as: To simplify the algorithm, in time speed ,Location It can be represented as: 8) Output Results The algorithm converges by setting the number of iterations. If the stopping condition is met, the final optimal content placement scheme is output. Finally, the optimal content placement scheme, i.e., which content should be stored on which satellites, is output to optimize cache load balancing, update traffic costs, and other objectives.

[0082] By employing the specific embodiments described above and introducing an atomic search optimization algorithm, content caching and allocation can be performed efficiently in large-scale satellite networks. This method, through reasonable initialization, fitness calculation, quality update, and attraction / repulsion calculation steps, helps ensure the efficiency of the search process and the acquisition of the optimal solution, providing a practical technical solution for content pre-placement in LEO satellite networks.

[0083] To verify the chained content pre-placement method in the LEO satellite network proposed in this invention, it was implemented and verified when the satellite network communication service provider deployed the satellite network and provided user services.

[0084] When satellite network communication service providers deploy satellite networks and provide user services, the chained content pre-placement scheme of this invention optimizes the content storage and retrieval process by implementing distributed cache management in the LEO satellite network. In specific implementation, the satellite network pre-analyzes hot content based on end-user needs and geographical distribution, and pre-places content blocks in the caches of various LEO satellite nodes. Each satellite node automatically updates and replaces cached content according to the chained content structure, ensuring that users can quickly obtain the required content upon request, reducing data transmission latency across satellite links.

[0085] The chained content pre-placement scheme improves bandwidth utilization efficiency of the LEO satellite network by dynamically adjusting the allocation of cached content. When a service request occurs, the system determines which satellite node to retrieve content from based on factors such as the cache status of satellite nodes, the user request type, and the network topology. If the cache of a satellite node does not match the requested content, the system requests data from adjacent nodes according to the chained storage structure, thereby minimizing latency and redundant traffic. Through this adaptive content pre-placement scheme, this invention effectively improves user service quality and optimizes satellite network resource utilization.

[0086] In another aspect, the present invention proposes a chained content pre-placement system in a LEO satellite network, the chained content pre-placement system comprising a content distribution and storage system deployed on LEO satellites and a server cluster deployed on the ground, for implementing the steps of the method as described in any of the above embodiments.

[0087] Furthermore, the content distribution and storage system deployed on LEO satellites can be an LEO satellite CDN network, and the server clusters deployed on the ground can be a terrestrial CDN network. LEO satellite cache nodes and / or terrestrial cache nodes support receiving and responding to content requests from terrestrial end users.

[0088] The key technical features of the chained content pre-placement method and system in the LEO satellite network proposed in this invention, compared with the prior art, include, but are not limited to: (1) A chain-based pre-placement scheme based on user behavior and content popularity. This scheme aims to dynamically predict and select the most likely content to be accessed for pre-placement by deeply analyzing users' content access history, behavioral patterns, and content popularity distribution. Through continuous learning of user behavior and real-time updates on changes in content popularity, the system can dynamically adjust its caching strategy, prioritizing the storage of high-demand content that is likely to be requested in a short period of time. The advantage of this technology is that it can effectively reduce invalid content in the cache, improve the cache hit rate, thereby significantly reducing the latency of user requests and optimizing the efficiency of network bandwidth utilization.

[0089] (2) Multi-level optimization mechanism based on satellite-ground collaboration. This mechanism constructs a multi-level caching architecture by coordinating the resources of satellite and terrestrial networks, which is beneficial for optimizing content pre-placement. This invention combines satellite caching and terrestrial CDN to form a hierarchical caching structure, in which satellite cache nodes can prioritize storing non-hot content, while the terrestrial CDN handles more static or hot content. The system can intelligently select the optimal content pre-placement location among different levels of caching based on the distribution of user requests, the popularity of content, and the network load, which is beneficial for ensuring efficient cache utilization and balanced distribution of network load. This mechanism can not only effectively reduce the communication load between satellite and ground, but also improve the response speed of user requests, realize the collaborative optimization of satellite and terrestrial network resources, and significantly improve the overall system performance.

[0090] (3) Intelligent cache update mechanism based on chained storage structure pre-placement. This mechanism optimizes cache utilization efficiency and reduces unnecessary resource waste by intelligently updating the content in the cache. To ensure the efficiency of content pre-placement, the system adopts a chained content update mechanism, which can intelligently update between cache nodes, which is beneficial for coping with changes in content access and cache space limitations. This mechanism can significantly improve the cache hit rate, reduce content transmission latency, and reduce the pressure on network bandwidth and storage resources, achieving more efficient resource management. Especially in resource-constrained satellite communication networks, it can optimize network performance and improve user experience.

[0091] The chained content pre-placement method and system in the LEO satellite network proposed in this invention can achieve beneficial effects including, but not limited to: (1) This invention proposes a cache pre-placement scheme based on chained content chunking, aiming to improve the efficiency of content distribution in LEO satellite networks. This technical solution divides content into chunks according to access frequency and relevance, and utilizes a chained storage structure to pre-distribute these content chunks to various cache nodes, which facilitates fast access and efficient distribution. Through chained content chunking, this invention can dynamically adjust the content chunk distribution strategy based on hot content and user behavior, thereby significantly improving the cache hit rate and reducing network transmission latency. Chained content chunking avoids resource waste in the caching system, reduces unnecessary bandwidth consumption, and effectively improves data transmission efficiency in satellite networks, especially suitable for LEO satellite communication environments with limited bandwidth and high load.

[0092] (2) This invention proposes a chain-based content pre-placement technology, which improves the system's response speed to user requests by pre-distributing popular content in the LEO satellite network. The chain-based pre-placement scheme first identifies potential popular content based on multi-dimensional data such as users' historical access behavior, geographical distribution, and task requirements, and then pre-places this content in the caches of various satellite nodes according to priority. This invention's chain-based pre-placement significantly reduces latency and bandwidth consumption during the request process by pre-placing content on multiple satellite cache nodes. This invention achieves collaborative work between multiple cache nodes in a chain-like manner, enabling rapid migration and updating of cached content, ensuring the timeliness and relevance of the content.

[0093] (3) This invention proposes a heuristic algorithm for pre-placement of LEO satellite chain content. This invention also proposes an ASO-based optimization method for service deployment in LEO satellite networks, which has strong adaptability and scalability.

[0094] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0095] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0096] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0097] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0098] References (such as patents / papers / standards): [1] Sandvine, “Global Internet Phenomena Report 2023.” [Online].Available: https: / / www.sandvine.com / global-internet-phenomena-report-2023 [2] Cisco Visual Networking Index, Complete Forecast Update.Accessed: Mar. 1, 2023. [Online]. Available: https: / / www.cisco.com / c / dam / m / en_us / network-intelligence / service-provider / digital-transformation / knowledge-network-webinars / pdfs / 1213-business-services-ckn.pdf [3] Z. Lai, H. Li, Q. Zhang, Q. Wu and J. Wu,"StarFront:Cooperatively Constructing Pervasive and Low-Latency CDNs Upon Emerging LEOSatellites and Clouds,"in IEEE / ACM Transactions on Networking, vol. 31, no.6, pp. 2559-2574, Dec. 2023, doi: 10.1109 / TNET.2023.3260166. [4] Z. Xiao et al.,"LEO satellite access network (LEO-SAN) towards6G: Challenges and approaches", IEEE Wireless Commun., Dec. 2022. [5] T. Chen et al.,"Efficient uplink transmission in ultra-dense LEOsatellite networks with multiband antennas", IEEE Commun. Lett., vol. 26, no.6, pp. 1373-1377, Jun. 2022。

Claims

1. A method for pre-placing chained content in a LEO satellite network, characterized in that, include: Retrieve the content that needs to be pre-placed on the LEO satellite cache node; The content that needs to be pre-placed on the LEO satellite cache node is broken down into content blocks that need to be pre-placed on the LEO satellite cache node; In the search space including all candidate schemes for content chunk pre-placement, a heuristic algorithm is used for global search optimization to iteratively obtain the optimal or near-optimal content chunk pre-placement scheme on the set indicators; wherein, the set indicators include content cache update traffic cost and / or content placement balance cost; According to the content block pre-placement scheme, the content that needs to be pre-placed on the LEO satellite cache node is divided into blocks and placed on the corresponding LEO satellite cache node using a chain storage structure.

2. The method according to claim 1, characterized in that, The step of obtaining the content that needs to be pre-placed on the LEO satellite cache node includes: Obtain the content that needs to be pre-placed, and divide the content into content that needs to be pre-placed on the ground cache node and content that needs to be pre-placed on the LEO satellite cache node; Content that needs to be pre-placed on ground cache nodes is distributed and stored in the ground cache nodes using a ground CDN network; wherein the ground cache nodes are managed by the ground CDN network.

3. The method according to claim 2, characterized in that, The step of dividing the content to be pre-placed into content to be pre-placed on ground cache nodes and content to be pre-placed on LEO satellite cache nodes includes: Popularity analysis is performed on each piece of content that needs to be pre-placed. Content with popularity exceeding a preset threshold is selected and pre-placed on the ground cache node. Content with popularity not exceeding the preset threshold is classified as the content that needs to be pre-placed on the LEO satellite cache node.

4. The method according to claim 1, characterized in that, The step of breaking down the content to be pre-placed on the LEO satellite cache node to obtain content blocks to be pre-placed on the LEO satellite cache node includes: Set an upper limit on the size of a single piece of content according to the requirements of linked storage. Content that exceeds the size limit is broken down into content blocks, and the content blocks are linked together using pointers in the linked storage structure.

5. The method according to claim 1, characterized in that, Before the step of using a heuristic algorithm for global search optimization, the method further includes: obtaining the state parameters of each LEO satellite cache node, evaluating the state of the LEO satellite cache node based on the state parameters, and using the state evaluation result of the LEO satellite cache node as the resource constraint condition for using a heuristic algorithm for global search optimization. The status parameters include some or all of the following: the current geographical location of the LEO satellite cache node, cache capacity, cache capacity usage, satellite mobility, satellite orbit parameters, inter-satellite link bandwidth, and satellite-to-ground link bandwidth; The status of the LEO satellite cache node includes: remaining available cache capacity.

6. The method according to claim 1, characterized in that, The heuristic algorithm is an atomic search optimization algorithm; The step of using a heuristic algorithm for global search optimization to iteratively obtain an optimal or near-optimal content chunking pre-placement scheme based on set metrics includes: Initially, multiple candidate schemes for pre-placement of content chunks are generated to form the solution space. In the atomic search optimization algorithm, an atom is used to represent a candidate scheme for pre-placement of content chunks. The dimension of the atom is the product of the number of content chunks and the number of LEO satellite cache nodes. Each dimension represents the caching status of the content chunks on the LEO satellite cache nodes. The fitness value function is used as the objective function of the atomic search optimization algorithm. The fitness value of each atom is calculated to measure the merits of the corresponding content chunking and pre-placement scheme. The fitness value function covers the content cache update traffic cost and the content placement balance cost. During the iteration process, the fitness value of each atom is calculated, the attraction and repulsion of the atom are calculated based on the fitness value, and the velocity and position of the atom are updated based on the attraction and repulsion of the atom. The iteration terminates when the preset number of iterations is reached or the current optimal content chunking pre-placement scheme reaches the preset performance threshold, thus obtaining the optimal or near-optimal content chunking pre-placement scheme in terms of the set indicators.

7. The method according to claim 1, characterized in that, The LEO satellite cache nodes are managed by the LEO satellite CDN network, which uses the LEO satellite CDN network to implement the step of using a chained storage structure to divide the content that needs to be pre-placed in the LEO satellite cache nodes into blocks and place them in the corresponding LEO satellite cache nodes.

8. The method according to claim 1, characterized in that, After using a chained storage structure to divide the content that needs to be pre-placed on the LEO satellite cache node into blocks and place them on the corresponding LEO satellite cache node, the method further includes: the LEO satellite cache node and / or the ground cache node supporting receiving and responding to content acquisition requests from ground terminal users.

9. A chain-style content pre-placement system in a LEO satellite network, characterized in that, The chain-based content pre-placement system includes a content distribution and storage system deployed on LEO satellites and a server cluster deployed on the ground, for implementing the steps of the method according to any one of claims 1-8.

10. The system according to claim 9, characterized in that, The content distribution and storage system deployed on LEO satellites is called the LEO satellite CDN network, and the server clusters deployed on the ground are called the terrestrial CDN network. LEO satellite cache nodes and / or terrestrial cache nodes support receiving and responding to content requests from terrestrial end users.

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