Multi-unmanned aerial vehicle assisted wireless cache network time delay optimization method and system

Abstract

The invention discloses a multi-unmanned aerial vehicle assisted wireless cache network time delay optimization method and system, and relates to the technical field of wireless cache network time delay optimization. The method comprises the following steps: constructing a wireless cache network architecture model of cooperative work of multiple unmanned aerial vehicles and a ground base station based on the unmanned aerial vehicle residual electric quantity proportion; information age is introduced as an index for measuring file content freshness based on a wireless cache network architecture model, and a cache updating mechanism is perfected. According to the invention, the file distribution efficiency is improved by constructing a wireless cache architecture of cooperative work of multiple unmanned aerial vehicles and the ground base station based on the unmanned aerial vehicle residual electric quantity proportion; compared with a single unmanned aerial vehicle, multi-unmanned aerial vehicle cooperative caching can more effectively share caching burden, and service interruption or delay caused by electric quantity or caching capacity limitation is avoided; the designed weighted K-means algorithm considering the position, activeness and importance of the user ensures that the unmanned aerial vehicle is deployed near the active and important user.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless cache network delay optimization, and in particular to a method and system for multi-UAV-assisted wireless cache network delay optimization. Background Art

[0002] With the continuous growth of network application demand, traditional wireless communication networks are facing increasingly severe challenges. With the increase in the number of users and the explosion of data traffic, the traditional base station architecture cannot effectively cope with the huge data transmission demand, resulting in problems such as too long transmission paths and too long response delays. Especially in environments with wide coverage or uneven user distribution, the burden on base stations increases, resulting in a significant decline in network performance.

[0003] In order to solve this problem, in recent years, drone-assisted wireless cache networks have gradually attracted attention as a new type of network architecture. Due to the advantages of flexible deployment and rapid response, drones can provide file distribution services to users in the air, significantly reducing the coverage blind spots and transmission delays faced by traditional base stations. Although this architecture has shown advantages in improving transmission efficiency and network response speed, the cache capacity of a single drone is limited and is subject to power constraints. Traditional multi-drone location deployment work mostly only considers the distance between users, ignoring the impact of user activity and importance on file distribution services. For this reason, the present invention proposes a multi-drone-assisted wireless cache network delay optimization method and system. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for optimizing the delay of a multi-drone assisted wireless cache network, and to improve the file distribution efficiency and user response speed through the collaborative working mechanism between multiple drones and base stations. Compared with a single drone, the collaborative work of multiple drones can effectively share the cache burden, avoid service interruption or delay caused by power or cache capacity limitations, and improve resource utilization efficiency through a collaborative cache mechanism; by considering a shared cache mode based on the remaining power ratio of drones, the remaining power limitation of drones in actual applications is fully considered to improve the overall cache efficiency of the system; at the same time, based on three factors: user location, user activity and user importance, a weighted K-means algorithm is proposed to determine the deployment location of multiple drones, thereby improving the overall user experience; finally, by dynamically adjusting the file cache placement strategy, the system delay can be significantly reduced under multiple constraints, while ensuring the timeliness of the file content. The method can adapt to complex multi-drone assisted wireless cache network application scenarios, and provide optimized technical solutions for smart cities, remote monitoring and other environments.

[0005] According to a first aspect of the present invention, in order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for optimizing the delay of a multi-drone-assisted wireless cache network, wherein the multi-drone-assisted wireless cache network includes users, base stations, and drones that respond to users according to user requests in a cellular network, including the following steps: Users in the cellular network area are divided into multiple clusters, each of which corresponds to a drone for service. The drones are equipped with a cache unit, which is used to cache the file content published by the ground source node. The drone corresponding to the user cluster transmits the cached file content to the user through the channel according to the user's request; If the file content is located in the cache unit of the drone in the user's adjacent cluster, the cache unit of the drone in the adjacent cluster is transmitted to the cache unit of the drone in the user's corresponding cluster, and the drone in the user's corresponding cluster transmits the file to the user through the channel; Taking the cache capacity of drones, the cache location of file contents, the freshness requirements of file contents and the stability of multi-drone assisted wireless cache network as constraints, a deep reinforcement learning algorithm is used for optimization. The network delay of multi-drone assisted wireless cache is optimized with the goal of maximizing the response rate of multi-drone assisted wireless cache network to users based on file content according to user requests.

[0006] Furthermore, users in the cellular network area are divided into two clusters, cluster 1 and cluster 2, and each cluster is equipped with a drone. and drones The base station acts as a backup transmission node and can provide file transfer services to remote users when the drone cache is insufficient. In addition, a source node exists in the cellular network for real-time monitoring and generating the latest file version; , drones , drones , and the base station caches the dynamic file content generated by the source node through wireless transmission, and sends the file to the user on demand.

[0007] Furthermore, drones and drones The current time slot will be based on its own power , The cache quantity of the files cached on the drone side is allocated in the following proportion: .

[0008] Furthermore, the file content is transmitted to the user through the channel by the drone of the user's corresponding cluster according to the user's request, which includes the following situations: (41) If the file content requested by the user in cluster 1 is When caching on the client, the drone Provided to users through a single channel; (42) If the file content requested by the user in cluster 1 is on the drone The end cache is handled by the drone Send to drone , and then by drone Send to users; (43) If the file content requested by the user in cluster 1 is cached at the base station, it will be provided to the user through a single channel by the base station.

[0009] Furthermore, the channel between the user and the drone is modeled as a line-of-sight or non-line-of-sight air-to-ground connection. The average service rate to users within its service range is expressed as: In the formula, It's a drone To User The service rate, is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; It's a drone Available bandwidth, is the transmitting power of the drone, It's a drone With users The channel gain between Is a single file of uniform size, is Gaussian noise, It's a drone The number of users in the service range, in the scenario of two adjacent drones, ; The transmission link between the source node and the UAV also considers the air-to-ground connection model of line-of-sight or non-line-of-sight connection. The file refresh rate is expressed as: In the formula, , From source node to drone The transmission bandwidth and transmission power, The source node and the drone The channel gain between Is a single file of uniform size, is Gaussian noise; Drones and drones Considering the air-to-air connection model with line-of-sight connection, the transmission rate between two UAVs is expressed as: In the formula, is the transmission bandwidth between the two drones, is the transmission power between the two drones, is the additional attenuation factor for the line-of-sight link, and UAVs and drones location, Is a single file of uniform size, is Gaussian noise; The channel between the user and the base station is a ground-to-ground connection, based on path loss and Rayleigh fading modeling, the base station to cluster The average service rate of users in is expressed as: In the formula, Base station to user The service rate, is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; is the available bandwidth of the base station, is the transmission power of the base station, Base station and user The channel gain between Is a single file of uniform size, is Gaussian noise, It's a drone The number of users in the service area; The transmission link between the source node and the base station is also modeled based on path loss and Rayleigh fading. The file refresh rate from the source node to the base station is: In the formula, , are the transmission bandwidth and transmission power from the source node to the base station, respectively. is the channel gain between the source node and the base station, Is a single file of uniform size, is Gaussian noise.

[0010] Furthermore, the drone or base station will check the freshness of the file before sending it, and introduce the information age as an indicator to measure the freshness of the file content. Taking the user request in cluster 1 as an example, the details are as follows: (61) Introducing M / M / 1 queues to represent the drone service process and capture the additional latency load caused by cache refresh, the drone End file in time slot The internal average refresh probability is expressed as: In the formula, It's a drone End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, The user in cluster 1 has a Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to drone The file refresh rate; The user in cluster 1 is in time slot Receive drone Terminal File The average information age is: In the formula, It's a drone The average service rate to all users in cluster 1, From source node to drone The file refresh rate, is the cache refresh window size, The user in cluster 1 has a Single file The request rate, is the base of natural logarithms; Drones The average service delay from the end to the user in cluster 1 is expressed as: In the formula, is the drone requested by a cluster user End file in time slot Average refresh probability, It's a drone The average service rate to all users in cluster 1, From source node to drone The file refresh rate, The user in cluster 1 has a The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within; Drones The average probability of a file request initiated by a user in cluster 1 triggering a cache refresh is expressed as: In the formula, It's a drone End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, The user in cluster 1 has a Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to drone The file refresh rate; Users in cluster 1 receive the drone Terminal File The average information age is: In the formula, It's a drone The average service rate to all users in cluster 1, It's a drone and drones The transmission rate between From source node to drone The file refresh rate, is the cache refresh window size, The user in cluster 1 has a Single file The request rate, is the base of natural logarithms; Drones The average service delay from the end to the user in cluster 1 is expressed as: In the formula, is the drone requested by a cluster user End file in time slot Average refresh probability, It's a drone The average service rate to all users in cluster 1, It's a drone and drones The transmission rate between From source node to drone The file refresh rate, The user in cluster 1 has a The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within; (62) Introducing M / M / 1 queues to represent the base station service process and capture the additional delay load caused by cache refresh, the base station The average probability of a file request initiated by a user in cluster 1 triggering a cache refresh is expressed as: In the formula, It is a base station End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, is the user in cluster 1 to the base station Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates base station In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to base station The file refresh rate; The user in cluster 1 is in time slot Received from base station Terminal File The average information age is: In the formula, It is a base station The average service rate to all users in cluster 1, From source node to base station The file refresh rate, is the cache refresh window size, is the user in cluster 1 to the base station Single file The request rate, is the base of natural logarithms; Base Station The average service delay to users in cluster 1 is expressed as: In the formula, is the base station requested by cluster 1 user End file in time slot Average refresh probability, It is a base station The average service rate to all users in cluster 1, From source node to base station The file refresh rate, is the user in cluster 1 to the base station The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates base station In time slot A collection of file caches within.

[0011] Furthermore, the users in cluster 2 are analyzed using the same method as in (61) and (62). Receive drone , drones and base station Terminal File The average information ages are: In the formula, It's a drone The average transmission rate to users in cluster 2, It's a drone and drones The transmission rate between It is a base station The average transmission rate to users in cluster 2, , and From source node to drone , drones and base station The file refresh rate, is the cache refresh window, , and They are respectively the users in cluster 2 and the drones Terminal, drone Terminal and base station Single file The request rate, is the total request rate of users in cluster 2, is a file In time slot The popularity of , and UAVs , drones and base station In time slot The file cache collection within; Drones , drones and base station The average service delay to users in cluster 2 is expressed as: In the formula, , and In drones , drones and base station On the other end, the average probability of a file request initiated by a user in cluster 2 triggering a cache refresh; , and In drones , drones and base station The user in cluster 2 is in time slot A proposed document The probability that a request triggers a cache refresh; , and They are respectively the users in cluster 2 and the drones Terminal, drone Terminal and base station Single file The request rate, is the total request rate of users in cluster 2, is a file In time slot The popularity of , and UAVs , drones and base station In time slot The file cache collection within; , and They are respectively the users in cluster 2 and the drones , drones and base station The total request rate of the client cache files, It's a drone The average transmission rate to users in cluster 2, It's a drone and drones The transmission rate between It is a base station The average transmission rate to users in cluster 2, , and From source node to drone , drones and base station The file refresh rate; The wireless cache network as a whole is in the time slot The average service delay is expressed as: .

[0012] Furthermore, taking the cache capacity of drones, the cache location of file contents, the freshness requirements of file contents, and the stability of multi-drone-assisted wireless cache networks as constraints, a deep reinforcement learning algorithm is used for optimization, as follows: (81) Establishing decision variables: Defining drones and drones The locations are and , a set of decision variables Indicates time slot document Cache location: in, Representative drone , Representative drone , Representative base station ; Defined as time slot The cache location of each file, is the total number of files; (82) Establish an optimization goal. The goal of the optimization process is to determine the deployment location of the UAV and find a set of file caching decisions under various constraints to minimize the system delay. The specific objective function is defined as the long-term system delay: In the formula, It is in the time slot The average service delay of is the expectation operator, which means taking the expected value of the random variable in the brackets. is the time step, Indicates extreme operation; (83) Set the constraints for the delay optimization decision, specifically: (831) UAV cache capacity constraint: The cache capacity of the UAV is limited. The number of files on the cache cannot exceed its maximum cache size. , the formula is: In the formula, is the uniform size of all files, Indicates time slot document Cache on drone , is the total number of files; (832) Cache decision constraint: Each file can only be cached in one node, expressed as: (833) File content freshness constraint: Users in clusters 1 and 2 have timeliness requirements for the files they obtain. The files obtained from drones or base stations cannot exceed the maximum age limit of the file information when they reach the user end. , the formula is: In the formula, , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File Average information age of (834) System stability requirement: In the M / M / 1 service system, the file request arrival rate between the drone and the base station is less than the file request departure rate to ensure system stability. The formula is: In the formula, , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate; (835) The optimization problem and its constraints are expressed as follows: In the formula, and UAVs and drones location, It is a time slot The cache location of each file, Represents a file In time slot Cache on Node , Representative drone , Representative drone , Representative base station ; is the long-term system delay; C1 indicates that the drone’s cache capacity is limited and the cache is stored in the drone The number of files on the cache cannot exceed its maximum cache size. , is the uniform size of all files, Indicates time slot document Cache on drone , is the total number of files; C2 means each file can only be cached in one node; C3 and C4 mean the number of files cached from the drone. or base station The retrieved file cannot exceed the maximum age limit of the file information when it reaches the user end. ; , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File Average information age; C5 and C6 represent drones With base station The file request arrival rate at the client must be less than the file request departure rate; , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate.

[0013] Furthermore, the optimization problem in step (835) is divided into two sub-problems, including: the drone location deployment problem and the file cache decision problem, as follows: (91) The weighted K-means algorithm based on user location, user activity and user importance is used to determine the drone deployment location, specifically: (91.1) Initialization: Randomly generate drones Initial Position ; (91.2) Calculate for each user To drones Distance: In the formula, Is a user location, is the total set of users, is the total number of users; (91.3) User Assign to the nearest drone ,in ; (91.4) Recalculate drones based on user location, user activity and user importance Location: In the formula, Is a user location; is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; It is a cluster The total set of users in It is a cluster Total number of users in (91.5) Termination condition: Continue iterating steps (91.1) to (91.4) until the drone meets and drones The sum of the two position changes is less than the threshold ; (92) A deep Q network is used to solve the file cache decision problem. The deep Q network algorithm includes the following algorithm steps: (92.1) Initialization: Initialize the state space, action space, and target reward function of DQN, and randomly initialize the parameters of the Q network , and create the target Q network; (92.2) Status definition: System status The probability vector of file request in the current time slot 、The remaining battery power of the drone in the current time slot and the previous time slot cache strategy composition; (92.3) Action selection: Action Indicates the cache strategy for the current time slot, , decide on the time slot Cache to drone or base station documents; (92.4) Reward calculation: Reward function Defined according to optimization objectives and constraints, including system average delay The minimization of the resource consumption is carried out, and the penalty term is introduced to ensure that the constraints are met, which are the cache capacity limit, cache location limit, file freshness requirement and system stability constraint. The reward function expression is: in, Is the system in time slot Average service delay of Represents the drone cache capacity constraint, ensuring that the cache is in the drone The number of files on the cache cannot exceed its maximum cache size. ; Represents the cache decision constraint, ensuring that each file can only be cached on one node; Indicates drone The freshness of the end file content is constrained to ensure that The file obtained by the client cannot exceed the maximum age limit of the file information when it reaches the user end. ; Indicates base station The freshness of the end file content is constrained to ensure that The file obtained by the client cannot exceed the maximum age limit of the file information when it reaches the user end. ; Indicates drone End system stability constraints ensure that drones The file request arrival rate at the client is less than the file request departure rate; Indicates base station End system stability constraints ensure base station The file request arrival rate at the client is less than the file request departure rate. is the penalty coefficient, and the max function means taking the maximum value in the brackets. is the uniform size of all files, It's a drone Cache capacity, Indicates time slot document The cache location, is the total number of files, , Clusters Chinese users use drones Terminal, Base Station The request arrival rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File The average information age, Is the maximum age limit of file information, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate; (92.5) Network update: Randomly extract small batches of samples based on the experience replay mechanism Perform training using the target Q value Update the parameters of the Q network ; (92.6) Action execution: Execute the selected action , update system status , record reward ; (92.7) Termination condition: Continue iterating steps (92.1) to (92.6) until the termination condition of Q value convergence is met; (92.8) After the optimization solution is completed, when using the trained DQN to obtain the file cache strategy, the current system state is input into the trained Q network, the Q value of each action is calculated, and the action with the largest Q value is selected. , the selected action This is the optimal cache strategy for the current time slot.

[0014] According to a second aspect of the present invention, the present invention provides a multi-UAV assisted wireless cache network delay optimization system, which is used to implement the above-mentioned multi-UAV assisted wireless cache network delay optimization method, including: The first building module is used to build a wireless cache network architecture model based on the remaining power ratio of the drones and the ground base station to work together, so as to improve the file distribution efficiency and user response speed; The update mechanism improvement module is used to introduce information age as a key indicator of file content freshness based on the wireless cache network architecture model and improve the cache update mechanism; The second building block determines the drone deployment location based on the weighted K-means algorithm based on user location, user activity and user importance to improve the overall user experience; The third building module is used to build an intelligent optimization model, which uses a deep reinforcement learning algorithm to dynamically adjust the cache placement strategy to obtain the optimal cache strategy.

[0015] The present invention has at least the following beneficial effects: 1. The present invention successfully improves the file distribution efficiency and user response speed by combining the collaborative working mechanism between multiple drones and base stations, and greatly optimizes the delay performance of the wireless cache network. Compared with a single drone, the collaborative work of multiple drones can effectively share the cache burden, avoid service interruptions or delays caused by power or cache capacity limitations, and improve resource utilization efficiency through a collaborative cache mechanism.

[0016] 2. The present invention adopts a shared cache mode based on the proportion of the remaining power of the drone in the cache strategy, fully considering the remaining power limit of the drone, significantly improving the cache efficiency of the system, and solving the problems of limited capacity of a single drone and limited remaining power of the drone in the traditional network.

[0017] 3. The present invention combines the three factors of user location, activity and importance, and uses a weighted K-means algorithm to optimize the deployment location of drones, so that the system can more accurately meet user needs and improve the overall user experience.

[0018] 4. The present invention introduces information age as a key indicator to measure the freshness of file content, improves and optimizes the cache update mechanism, ensures timely response to user needs and maintains high-timeliness file distribution services.

[0019] 5. The present invention can not only significantly reduce system latency under multiple constraints, but also ensure the timeliness of file content by dynamically adjusting the file cache placement strategy, adapt to complex multi-UAV assisted wireless cache network application scenarios, and provide an efficient, reliable and timely optimization technology solution for smart cities, remote monitoring and other environments.

[0020] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the optimization method of the present invention; Figure 2 A system scenario diagram of the optimization method of the present invention; Figure 3 This is a performance comparison test chart between the optimization method described in the present invention and the traditional algorithm. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present disclosure.

[0023] Embodiment 1: See also Figure 1 The present invention provides a technical solution: a multi-UAV assisted wireless cache network delay optimization method, including a user in a cellular network, a base station, and a UAV that responds to a user according to a user request, including the following steps: S1. Construct a wireless cache network architecture model based on the remaining power ratio of drones and ground base stations to work together to improve file distribution efficiency and user response speed, as follows: S1-1 builds a multi-drone assisted wireless cache network architecture model, which is randomly distributed in a typical wireless cache cellular network. users, who are far from the base station; multiple drones are deployed in the air to serve ground users; to simplify the analysis, we consider two adjacent drones to carry out system modeling without loss of generality. Similar analysis methods are also applicable to larger-scale scenarios, and then to the case of multiple drone deployments. Users in the entire area are divided into at least two clusters, Cluster 1 and Cluster 2. Cluster 1 is equipped with drones. Cluster 2 is equipped with drones To provide services, each drone is equipped with a cache unit that can cache the file content published by the ground source node; the drone quickly responds to user requests by caching some files, and the base station, as a backup transmission node, can provide file transfer services to remote users when the drone cache is insufficient; in addition, there is a source node in the cellular network that is responsible for real-time monitoring of the surrounding environment and generating the latest file version; in each time slot ,The two drones and the base station cache the dynamic file content generated by the source node through wireless transmission according to the set caching strategy, and send the file to the user as needed; S1-2 The UAV under consideration , drones In the cache scene, the drone and drones The current time slot will be based on its own power , The cache quantity of the files cached on the drone side is allocated in the following proportion: , avoid caching duplicate files through collaborative caching, and improve the overall caching efficiency; when the remaining power of one of the drones is exhausted, use a fully charged drone to replace it; S1-3 Please refer to Figure 2 In the wireless cache network architecture model based on multi-drone assistance, if the file content requested by the user in cluster 1 is in the drone When the end cache (drone 1 cache) is used, the drone Provided to users through a single channel; if the file content requested by the user in cluster 1 is The end cache (drone 2 cache) is stored by the drone. Send to drone , and then by drone Send it to the user; if the file content requested by the user in cluster 1 is cached on the base station side (base station cache), the base station will provide it to the user through a single channel; Before sending a file, the drone or base station checks the freshness of the file. If the information age of the file is lower than the set threshold, the file is directly provided to the user. If the information age exceeds the threshold, the drone or base station obtains the latest version of the file from the source node, refreshes the cache, and then delivers it to the user. In the S1-4 wireless cache network architecture model based on multi-UAV assistance, the channel between the user and the UAV is modeled as a line-of-sight or non-line-of-sight air-to-ground connection. The average service rate to users within its service range can be expressed as: In the formula, It's a drone To User The service rate, is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; It's a drone Available bandwidth, is the transmitting power of the drone, It's a drone With users The channel gain between Is a single file of uniform size, is Gaussian noise, It's a drone The number of users in the service range (in the scenario of two adjacent drones considered in this embodiment, ); The transmission link between the source node and the UAV also considers the air-to-ground connection model of line-of-sight or non-line-of-sight connection. The file refresh rate can be expressed as: In the formula, , From source node to drone The transmission bandwidth and transmission power, The source node and the drone The channel gain between Is a single file of uniform size, is Gaussian noise; Drones and drones Considering the air-to-air connection model with line-of-sight, the transmission rate between two UAVs can be expressed as: In the formula, is the transmission bandwidth between the two drones, is the transmission power between the two drones, is the additional attenuation factor for the line-of-sight link, and UAVs and drones location, Is a single file of uniform size, is Gaussian noise; The channel between the user and the base station is a ground-to-ground connection, based on path loss and Rayleigh fading modeling, the base station to cluster The average service rate of users in can be expressed as: In the formula, Base station to user The service rate, is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; is the available bandwidth of the base station, is the transmission power of the base station, Base station and user The channel gain between Is a single file of uniform size, is Gaussian noise, It's a drone The number of users in the service area; The transmission link between the source node and the base station is also modeled based on path loss and Rayleigh fading. The file refresh rate from the source node to the base station is: In the formula, , are the transmission bandwidth and transmission power from the source node to the base station, respectively. is the channel gain between the source node and the base station, Is a single file of uniform size, is Gaussian noise; S2. Based on the wireless cache network architecture model, the information age is introduced as a key indicator to measure the freshness of the file content, and the cache update mechanism is improved. Taking the user request in cluster 1 as an example, the details are as follows: S2-1 introduces M / M / 1 queues to represent the drone service process and capture the additional latency load caused by cache refresh. End file in time slot The internal average refresh probability can be expressed as: In the formula, It's a drone End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, The user in cluster 1 has a Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to drone The file refresh rate; The user in cluster 1 is in time slot Receive drone Terminal File The average information age is: In the formula, It's a drone The average service rate to all users in cluster 1, From source node to drone The file refresh rate, is the cache refresh window size, The user in cluster 1 has a Single file The request rate, is the base of natural logarithms; Drones The average service delay from the end to the user in cluster 1 can be expressed as: In the formula, is the drone requested by a cluster user End file in time slot Average refresh probability, It's a drone The average service rate to all users in cluster 1, From source node to drone The file refresh rate, The user in cluster 1 has a The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within; Drones The average probability of a file request initiated by a user in cluster 1 triggering a cache refresh can be expressed as: In the formula, It's a drone End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, The user in cluster 1 has a Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to drone The file refresh rate; Users in cluster 1 receive the drone Terminal File The average information age is: In the formula, It's a drone The average service rate to all users in cluster 1, It's a drone and drones The transmission rate between From source node to drone The file refresh rate, is the cache refresh window size, The user in cluster 1 has a Single file The request rate, is the base of natural logarithms; Drones The average service delay from the end to the user in cluster 1 can be expressed as: In the formula, is the drone requested by a cluster user End file in time slot Average refresh probability, It's a drone The average service rate to all users in cluster 1, It's a drone and drones The transmission rate between From source node to drone The file refresh rate, The user in cluster 1 has a The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within; S2-2 introduces M / M / 1 queues to represent the base station service process and capture the additional delay load caused by cache refresh. At the base station, the average probability of a file request initiated by a cluster 1 user triggering a cache refresh can be expressed as: In the formula, It is a base station End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, is the user in cluster 1 to the base station Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates base station In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to base station The file refresh rate; The user in cluster 1 is in time slot Received from base station Terminal File The average information age is: In the formula, It is a base station The average service rate to all users in cluster 1, From source node to base station The file refresh rate, is the cache refresh window size, is the user in cluster 1 to the base station Single file The request rate, is the base of natural logarithms; Base Station The average service delay to users in cluster 1 can be expressed as: In the formula, is the base station requested by cluster 1 user End file in time slot Average refresh probability, It is a base station The average service rate to all users in cluster 1, From source node to base station The file refresh rate, is the user in cluster 1 to the base station The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates base station In time slot The file cache collection within; The users in cluster 2 of S2-3 can be analyzed in the same way. Receive drone , drones and base station Terminal File The average information ages are: In the formula, It's a drone The average transmission rate to users in cluster 2, It's a drone and drones The transmission rate between It is a base station The average transmission rate to users in cluster 2, , and From source node to drone , drones and base station The file refresh rate, is the cache refresh window, , and They are respectively the users in cluster 2 and the drones Terminal, drone Terminal and base station Single file The request rate, is the total request rate of users in cluster 2, is a file In time slot The popularity of , and UAVs , drones and base station In time slot The file cache collection within; Drones , drones and base station The average service delay to users in cluster 2 can be expressed as: In the formula, , and In drones , drones and base station On the other end, the average probability of a file request initiated by a user in cluster 2 triggering a cache refresh; , and In drones , drones and base station The user in cluster 2 is in time slot A proposed document The probability that a request triggers a cache refresh; , and They are respectively the users in cluster 2 and the drones Terminal, drone Terminal and base station Single file The request rate, is the total request rate of users in cluster 2, is a file In time slot The popularity of , and UAVs , drones and base station In time slot The file cache collection within; , and They are respectively the users in cluster 2 and the drones , drones and base station The total request rate of the client cache files, It's a drone The average transmission rate to users in cluster 2, It's a drone and drones The transmission rate between It is a base station The average transmission rate to users in cluster 2, , and From source node to drone , drones and base station The file refresh rate; S2-4 The entire system (wireless cache network system) is in the time slot The average service delay can be expressed as: ; S3. The weighted K-means algorithm based on user location, user activity and user importance is used to determine the deployment location of the drone to improve the overall user experience, as follows: S3-1 Establishing decision variables: Defining drones and drones The location is and , a set of decision variables Indicates time slot document Cache location: in, Representative drone , Representative drone , Representative base station ; Defined as time slot The cache location of each file, is the total number of files; S3-2 establishes the optimization goal. The goal of the optimization process is to determine the deployment location of the drone and find a set of file cache decisions under various constraints to minimize the delay of the wireless cache network system. The specific objective function is defined as the long-term delay of the system: In the formula, It is in the time slot The average service delay of is the expectation operator, which means taking the expected value of the random variable in the brackets. is the time step, Indicates extreme operation; S3-3 sets the constraints for latency optimization decision, specifically: UAV cache capacity constraint: The cache capacity of the UAV is limited. The number of files on the cache cannot exceed its maximum cache size. , the formula is: In the formula, is the uniform size of all files, Indicates time slot document Cache on drone , is the total number of files; Cache decision constraint: Each file can only be cached in one node, expressed as: File content freshness constraint: Users in clusters 1 and 2 have timeliness requirements for the files they obtain. The files obtained from drones or base stations cannot exceed the maximum age limit of the file information when they reach the user end. , the formula is: In the formula, , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File Average information age of Stability requirements for wireless cache network systems: In the adopted M / M / 1 service system, the file request arrival rate between the drone and the base station is less than the file request departure rate to ensure system stability. The formula is: In the formula, , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate; The optimization problem and its constraints are expressed as follows: In the formula, and UAVs and drones location, It is a time slot The cache location of each file, Represents a file In time slot Cache on Node , Representative drone , Representative drone , Representative base station ; is the long-term system delay; C1 indicates that the drone’s cache capacity is limited and the cache is stored in the drone The number of files on the cache cannot exceed its maximum cache size. , is the uniform size of all files, Indicates time slot document Cache on drone , is the total number of files; C2 means each file can only be cached in one node; C3 and C4 mean the number of files cached from the drone. or base station The retrieved file cannot exceed the maximum age limit of the file information when it reaches the user end. ; , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File Average information age; C5 and C6 represent drones With base station The file request arrival rate at the client must be less than the file request departure rate; , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate; S3-4 splits the optimization problem in S3-3 into two sub-problems, including: drone location deployment problem and file cache decision problem; the weighted K-means algorithm based on user location, user activity and user importance is used to determine the drone deployment location, specifically: Initialization: Randomly generate drones Initial Position ; Calculate each user To drones Distance: In the formula, Is a user location, is the total set of users, is the total number of users; The user Assign to the nearest drone ,in ; Recalculate drones based on user location, user activity and user importance Location: In the formula, Is a user location; is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; It is a cluster The total set of users in It is a cluster Total number of users in Termination condition: Continue to iterate the above initialization, calculation of drone positions, etc. until the termination condition is met (the sum of the position changes of the two drones before and after is less than the threshold ); S4. Use deep reinforcement learning algorithm to dynamically adjust the cache placement strategy to obtain the optimal cache strategy, as follows: A deep Q network is used to solve the file cache decision problem. The deep Q network algorithm includes the following algorithm steps: S4-1 Initialization: Initialize the state space, action space, and target reward function of DQN, and randomly initialize the parameters of the Q network , and create the target Q network; S4-2 Status Definition: Wireless Cache Network System Status The probability vector of file request in the current time slot 、The remaining battery power of the drone in the current time slot and the previous time slot cache strategy composition; S4-3 Action Selection: Action Indicates the cache strategy for the current time slot, , decide on the time slot Which files are cached to the drone or base station ; S4-4 Reward Calculation: Reward Function Defined according to optimization objectives and constraints, including system average delay The minimization of the resource consumption is carried out, and the penalty term is introduced to ensure that the constraints are met, which are the cache capacity limit, cache location limit, file freshness requirement and system stability constraint. The S4-5 reward function expression is: in, Is the system in time slot The average service delay of Represents the drone cache capacity constraint, ensuring that the cache is in the drone The number of files on the cache cannot exceed its maximum cache size. , Represents a cache decision constraint, ensuring that each file can only be cached on one node. Indicates drone The freshness of the end file content is constrained to ensure that The file obtained by the client cannot exceed the maximum age limit of the file information when it reaches the user end. , Indicates base station The freshness of the end file content is constrained to ensure that The file obtained by the client cannot exceed the maximum age limit of the file information when it reaches the user end. , Indicates drone End system stability constraints ensure that drones The file request arrival rate at the client is less than the file request departure rate. Indicates base station End system stability constraints ensure base station The file request arrival rate at the client is less than the file request departure rate. is the penalty coefficient, and the max function means taking the maximum value in the brackets. is the uniform size of all files, It's a drone Cache capacity, Indicates time slot document The cache location, is the total number of files, , Clusters Chinese users use drones Terminal, Base Station The request arrival rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File The average information age, Is the maximum age limit of file information, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate; S4-6 Network update: Randomly extract small batches of samples based on the experience replay mechanism Perform training using the target Q value Update the parameters of the Q network ; S4-7 Action Execution: Execute the selected action , update system status , record reward ; S4-8 Termination condition: Continue to iterate the above S4-1 to S4-7 process until the termination condition is met (Q value converges); After S4-9 completes the optimization solution, when using the trained DQN to obtain the file cache strategy, the current system state is input into the trained Q network to calculate the Q value of each possible action and select the action with the largest Q value. , the selected action That is, the optimal cache strategy for the current time slot. According to the optimal cache strategy, the drone is dynamically adjusted. and base station caching decisions to optimize system performance.

[0024] Next, the technical solution of the present invention is further described in conjunction with specific embodiments: An embodiment of the present invention provides a multi-UAV assisted wireless cache network delay optimization method. In order to verify the beneficial effects of the present invention, a scientific demonstration was carried out through simulation experiments.

[0025] Figure 3 A delay comparison chart of the DQN algorithm proposed in this embodiment, the traditional random algorithm, and the particle swarm optimization algorithm within 1000 test rounds is given. It can be seen that the delay of the DQN algorithm proposed in this embodiment is better than that of the particle swarm optimization algorithm and the random algorithm.

[0026] In terms of computational complexity, the simulation platform is CPU Intel i5-13600. In the test phase, the trained DQN model is used to compare the time complexity with other optimization algorithms. The running time of each algorithm per round is shown in Table 1: Table 1 Average duration of each round (seconds) The DQN model trained by the present invention is 487 times faster than the particle swarm optimization algorithm. Figure 3 As shown in Table 1, the DQN algorithm proposed in the present invention can not only effectively reduce the system delay, but also has extremely low time complexity, which fully proves the effectiveness and practicality of the method.

[0027] Embodiment 2: This embodiment provides a multi-UAV assisted wireless cache network delay optimization system, which is used to implement the multi-UAV assisted wireless cache network delay optimization method described in the first embodiment, including: The first building module is used to build a wireless cache network architecture model based on the remaining power ratio of the drones and the ground base station to work together, so as to improve the file distribution efficiency and user response speed; The update mechanism improvement module is used to introduce information age as a key indicator of file content freshness based on the wireless cache network architecture model and improve the cache update mechanism; The second building block determines the drone deployment location based on the weighted K-means algorithm based on user location, user activity and user importance to improve the overall user experience; The third building module is used to build an intelligent optimization model, which uses a deep reinforcement learning algorithm to dynamically adjust the cache placement strategy to obtain the optimal cache strategy.

[0028] Specifically, the above-mentioned first building module, update mechanism improvement module, second building module and third building module can be embedded in a computer processing system, and the computer calls the above-mentioned modules to complete the delay optimization task according to the multi-UAV assisted wireless cache network delay optimization method provided above; the above-mentioned first building module, update mechanism improvement module, second building module and third building module can perform operations according to the specific steps given in the multi-UAV assisted wireless cache network delay optimization method.

[0029] It should be noted that it should be understood that the division of the various modules of the above system is only the division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated, and these modules can all be implemented in the form of software calling through processing elements; they can also be all implemented in the form of hardware; some modules can also be implemented in the form of software calling through processing elements, and some modules can be implemented in the form of hardware. For example, the first building block can be a separately established processing element, or it can be integrated in a certain chip of the above-mentioned device for implementation. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and called and executed by a certain processing element of the above-mentioned device. The functions of the above signal processing module, and the implementation of other modules is similar. In addition, these modules can be fully or partially integrated together, or they can be implemented independently. The processing element described here can be an integrated circuit with signal processing capabilities. In the implementation process, each step of the above method or each of the above modules can be completed by the hardware integrated logic circuit in the processor element or the instructions in the form of software.

[0030] For example, the above modules may be one or more integrated circuits configured to implement the above methods, such as one or more application specific integrated circuits (ASIC), or one or more microprocessors (DSP), or one or more field programmable gate arrays (FPGA). For another example, when a module above is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call program code. For another example, these modules may be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0031] Embodiment three: The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the above-mentioned multi-UAV assisted wireless cache network delay optimization method is adopted.

[0032] It should be noted that the terminal device can be a computer device such as a desktop computer, a laptop computer or a cloud server, and the terminal device includes but is not limited to a processor and a memory. For example, the terminal device can also include input and output devices, network access devices and buses.

[0033] Furthermore, the processor may adopt a central processing unit (CPU). Of course, according to actual usage, other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. may also be adopted. The general-purpose processor may adopt a microprocessor or any conventional processor, etc., and this application does not impose any restrictions on this.

[0034] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0035] For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances. When an element is referred to as being "assembled on", "installed on", "fixed on" or "set on" another element, it can be directly on the other element or there can also be a centered element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be a centered element at the same time. The terms "vertical", "horizontal", "up", "down", "left", "right" and similar expressions used herein are only for illustrative purposes and are not intended to be the only implementation method.

[0036] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0037] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. A method for optimizing the delay of a multi-drone-assisted wireless cache network, wherein the multi-drone-assisted wireless cache network includes users, base stations, and drones that respond to users according to user requests in a cellular network, and is characterized in that: The following steps are involved: Users in the cellular network area are divided into multiple clusters, each of which corresponds to a drone for service. The drones are equipped with a cache unit, which is used to cache the file content published by the ground source node. The drone corresponding to the user cluster transmits the cached file content to the user through the channel according to the user's request; If the file content is located in the cache unit of the drone in the user's adjacent cluster, the cache unit of the drone in the adjacent cluster is transmitted to the cache unit of the drone in the user's corresponding cluster, and the drone in the user's corresponding cluster transmits the file to the user through the channel; Taking the cache capacity of drones, the cache location of file contents, the freshness requirements of file contents and the stability of multi-drone assisted wireless cache network as constraints, a deep reinforcement learning algorithm is used for optimization. The network delay of multi-drone assisted wireless cache is optimized with the goal of maximizing the response rate of multi-drone assisted wireless cache network to users based on file content according to user requests.

2. The multi-UAV assisted wireless cache network delay optimization method according to claim 1 is characterized in that: The users in the cellular network area are divided into two clusters, cluster one and cluster two, and each cluster is equipped with a drone and drones The base station acts as a backup transmission node and can provide file transfer services to remote users when the drone cache is insufficient. In addition, a source node exists in the cellular network for real-time monitoring and generating the latest file version; , drones , drones , and the base station caches the dynamic file content generated by the source node through wireless transmission, and sends the file to the user on demand.

3. The multi-UAV assisted wireless cache network delay optimization method according to claim 2 is characterized in that: Drones and drones The current time slot will be based on its own power , The cache quantity of the files cached on the drone side is allocated in the following proportion: .

4. The multi-UAV assisted wireless cache network delay optimization method according to claim 3 is characterized in that: The file content is transmitted to the user through the channel by the drone of the user's corresponding cluster according to the user's request, including the following situations: (41) If the file content requested by the user in cluster 1 is When caching on the client, the drone Provided to users through a single channel; (42) If the file content requested by the user in cluster 1 is on the drone The end cache is handled by the drone Send to drone , and then by drone Send to users; (43) If the file content requested by the user in cluster 1 is cached at the base station, it will be provided to the user through a single channel by the base station.

5. The multi-UAV assisted wireless cache network delay optimization method according to claim 4 is characterized in that: The channel between the user and the drone is modeled as a line-of-sight or non-line-of-sight air-to-ground connection. The average service rate to users within its service range is expressed as: In the formula, It's a drone To User The service rate, is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; It's a drone Available bandwidth, is the transmitting power of the drone, It's a drone With users The channel gain between Is a single file of uniform size, is Gaussian noise, It's a drone The number of users in the service range, in the scenario of two adjacent drones, ; The transmission link between the source node and the UAV also considers the air-to-ground connection model of line-of-sight or non-line-of-sight connection. The file refresh rate is expressed as: In the formula, , From source node to drone The transmission bandwidth and transmission power, The source node and the drone The channel gain between Is a single file of uniform size, is Gaussian noise; Drones and drones Considering the air-to-air connection model with line-of-sight connection, the transmission rate between two UAVs is expressed as: In the formula, is the transmission bandwidth between the two drones, is the transmission power between the two drones, is the additional attenuation factor for the line-of-sight link, and UAVs and drones location, Is a single file of uniform size, is Gaussian noise; The channel between the user and the base station is a ground-to-ground connection, based on path loss and Rayleigh fading modeling, the base station to cluster The average service rate of users in is expressed as: In the formula, Base station to user The service rate, is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; is the available bandwidth of the base station, is the transmission power of the base station, Base station and user The channel gain between Is a single file of uniform size, is Gaussian noise, It's a drone The number of users in the service area; The transmission link between the source node and the base station is also modeled based on path loss and Rayleigh fading. The file refresh rate from the source node to the base station is: In the formula, , are the transmission bandwidth and transmission power from the source node to the base station, respectively. is the channel gain between the source node and the base station, Is a single file of uniform size, is Gaussian noise.

6. The multi-UAV assisted wireless cache network delay optimization method according to claim 5 is characterized in that: Before sending a file, the drone or base station will check the freshness of the file and introduce the information age as an indicator to measure the freshness of the file content. Take the user request in cluster 1 as an example, the details are as follows: (61) Introducing M / M / 1 queues to represent the drone service process and capture the additional latency load caused by cache refresh, the drone End file in time slot The internal average refresh probability is expressed as: In the formula, It's a drone End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, The user in cluster 1 has a Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to drone The file refresh rate; The user in cluster 1 is in time slot Receive drone Terminal File The average information age is: In the formula, It's a drone The average service rate to all users in cluster 1, From source node to drone The file refresh rate, is the cache refresh window size, The user in cluster 1 has a Single file The request rate, is the base of natural logarithms; Drones The average service delay from the end to the user in cluster 1 is expressed as: In the formula, is the drone requested by a cluster user End file in time slot Average refresh probability, It's a drone The average service rate to all users in cluster 1, From source node to drone The file refresh rate, The user in cluster 1 has a The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within; Drones The average probability of a file request initiated by a user in cluster 1 triggering a cache refresh is expressed as: In the formula, It's a drone End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, The user in cluster 1 has a Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From source node to drone The file refresh rate; Users in cluster 1 receive the drone Terminal File The average information age is: In the formula, It's a drone The average service rate to all users in cluster 1, It's a drone and drones The transmission rate between From source node to drone The file refresh rate, is the cache refresh window size, The user in cluster 1 has a Single file The request rate, is the base of natural logarithms; Drones The average service delay from the end to the user in cluster 1 is expressed as: In the formula, is the drone requested by a cluster user End file in time slot Average refresh probability, It's a drone The average service rate to all users in cluster 1, It's a drone and drones The transmission rate between From source node to drone The file refresh rate, The user in cluster 1 has a The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates drone In time slot The file cache collection within; (62) Introducing M / M / 1 queues to represent the base station service process and capture the additional delay load caused by cache refresh, the base station The average probability of a file request initiated by a user in cluster 1 triggering a cache refresh is expressed as: In the formula, It is a base station End in time slot In the cluster, a user initiates a request to a file The probability that a request triggers a cache refresh, is the user in cluster 1 to the base station Single file The request rate, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates base station In time slot The file cache collection within, is the cache refresh window size, is the base of natural logarithms, From the source node to the base station The file refresh rate; The user in cluster 1 is in time slot Received from base station Terminal File The average information age is: In the formula, It is a base station The average service rate to all users in cluster 1, From the source node to the base station The file refresh rate, is the cache refresh window size, is the user in cluster 1 to the base station Single file The request rate, is the base of natural logarithms; Base Station The average service delay to users in cluster 1 is expressed as: In the formula, is the base station requested by cluster 1 user End file in time slot Average refresh probability, It is a base station The average service rate to all users in cluster 1, From the source node to the base station The file refresh rate, is the user in cluster 1 to the base station The total request rate of the side cache files, is the total request rate of all users in cluster 1, is a file In time slot The popularity of Indicates base station In time slot A collection of file caches within.

7. The multi-UAV assisted wireless cache network delay optimization method according to claim 6 is characterized in that: The users in cluster 2 are analyzed using the same method as in (61) and (62). The users in cluster 2 are in time slot Receive drone , drones and base station Terminal File The average information ages are: In the formula, It's a drone The average transmission rate to users in cluster 2, It's a drone and drones The transmission rate between It is a base station The average transmission rate to users in cluster 2, , and From source node to drone , drones and base station The file refresh rate, is the cache refresh window, , and They are respectively the users in cluster 2 and the drones Terminal, drone Terminal and base station Single file The request rate, is the total request rate of users in cluster 2, is a file In time slot The popularity of , and UAVs , drones and base station In time slot The file cache collection within; Drones , drones and base station The average service delay to users in cluster 2 is expressed as: In the formula, , and In drones , drones and base station On the other end, the average probability of a file request initiated by a user in cluster 2 triggering a cache refresh; , and In drones , drones and base station The user in cluster 2 is in time slot A pair of documents The probability that a request triggers a cache refresh; , and They are respectively the users in cluster 2 and the drones Terminal, drone Terminal and base station Single file The request rate, is the total request rate of users in cluster 2, is a file In time slot The popularity of , and UAVs , drones and base station In time slot The file cache collection within; , and They are respectively the users in cluster 2 and the drones , drones and base station The total request rate of the client cache files, It's a drone The average transmission rate to users in cluster 2, It's a drone and drones The transmission rate between It is a base station The average transmission rate to users in cluster 2, , and From source node to drone , drones and base station The file refresh rate; The wireless cache network as a whole is in the time slot The average service delay is expressed as: 。 8. The multi-UAV assisted wireless cache network delay optimization method according to claim 6 is characterized in that: Taking the cache capacity of drones, the cache location of file contents, the freshness requirements of file contents, and the stability of multi-drone-assisted wireless cache networks as constraints, a deep reinforcement learning algorithm is used for optimization, as follows: (81) Establishing decision variables: Defining drones and drones The locations are and , a set of decision variables Indicates time slot document Cache location: in, Representative drone , Representative drone , Representative base station ; Defined as time slot The cache location of each file, is the total number of files; (82) Establish an optimization goal. The goal of the optimization process is to determine the deployment location of the UAV and find a set of file caching decisions under various constraints to minimize the system delay. The specific objective function is defined as the long-term system delay: In the formula, It is in the time slot The average service delay of is the expectation operator, which means taking the expected value of the random variable in the brackets. is the time step, Indicates extreme operation; (83) Set the constraints for the delay optimization decision, specifically: (831) UAV cache capacity constraint: The cache capacity of the UAV is limited. The maximum number of files in the cache cannot exceed , the formula is: In the formula, is the uniform size of all files, Indicates time slot document Cache on drone , is the total number of files; (832) Cache decision constraint: Each file can only be cached in one node, expressed as: (833) File content freshness constraint: Users in clusters 1 and 2 have timeliness requirements for the files they obtain. The files obtained from drones or base stations cannot exceed the maximum age limit of the file information when they reach the user end. , the formula is: In the formula, , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File Average information age of (834) System stability requirement: In the M / M / 1 service system, the file request arrival rate between the drone and the base station is less than the file request departure rate to ensure system stability. The formula is: In the formula, , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate; (835) The optimization problem and its constraints are expressed as follows: In the formula, and UAVs and drones location, It is a time slot The cache location of each file, Represents a file In time slot Cache on Node , Representative drone , Representative drone , Representative base station ; is the long-term system delay; C1 indicates that the drone’s cache capacity is limited and the cache is stored in the drone The maximum number of files in the cache cannot exceed , is the uniform size of all files, Indicates time slot document Cache on drone , is the total number of files; C2 means each file can only be cached in one node; C3 and C4 mean the number of files cached from the drone. or base station The retrieved file cannot exceed the maximum age limit of the file information when it reaches the user end. ; , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File Average information age; C5 and C6 represent drones With base station The file request arrival rate at the client must be less than the file request departure rate; , Clusters Chinese users use drones Terminal, Base Station The request rate of the client cache file, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate.

9. The multi-UAV assisted wireless cache network delay optimization method according to claim 8, characterized in that: The optimization problem in step (835) is divided into two sub-problems, including: the drone location deployment problem and the file cache decision problem, as follows: (91) The weighted K-means algorithm based on user location, user activity and user importance is used to determine the drone deployment location, specifically: (91.1) Initialization: Randomly generate drones Initial Position ; (91.2) Calculate for each user To drones Distance: In the formula, Is a user location, is the total set of users, is the total number of users; (91.3) User Assign to the nearest drone ,in ; (91.4) Recalculate drones based on user location, user activity and user importance Location: In the formula, Is a user location; is the user's activity, indicating that the user Probability of sending a request; is the user importance, indicating the priority of the user sending the request; It is a cluster The total set of users in It is a cluster Total number of users in (91.5) Termination condition: Continue iterating steps (91.1) to (91.4) until the drone meets and drones The sum of the two position changes is less than the threshold ; (92) A deep Q network is used to solve the file cache decision problem. The deep Q network algorithm includes the following algorithm steps: (92.1) Initialization: Initialize the state space, action space, and target reward function of DQN, and randomly initialize the parameters of the Q network , and create the target Q network; (92.2) Status definition: System status The probability vector of file request in the current time slot 、The remaining battery power of the drone in the current time slot and the previous time slot cache strategy composition; (92.3) Action selection: Action Indicates the cache strategy for the current time slot, , decide on the time slot Cache to drone or base station documents; (92.4) Reward calculation: Reward function Defined according to optimization objectives and constraints, including system average delay The minimization of the resource consumption is carried out, and the penalty term is introduced to ensure that the constraints are met, which are the cache capacity limit, cache location limit, file freshness requirement and system stability constraint. The reward function expression is: in, Is the system in time slot Average service delay of Represents the drone cache capacity constraint, ensuring that the cache is in the drone The maximum number of files in the cache cannot exceed ; Represents the cache decision constraint, ensuring that each file can only be cached on one node; Indicates drone The freshness of the end file content is constrained to ensure that The file obtained by the client cannot exceed the maximum age limit of the file information when it reaches the user end. ; Indicates base station The freshness of the end file content is constrained to ensure that The file obtained by the client cannot exceed the maximum age limit of the file information when it reaches the user end. ; Indicates drone End system stability constraints ensure that drones The file request arrival rate at the client is less than the file request departure rate; Indicates base station End system stability constraints ensure base station The file request arrival rate at the client is less than the file request departure rate. is the penalty coefficient, and the max function means taking the maximum value in the brackets. is the uniform size of all files, It's a drone Cache capacity, Indicates time slot document The cache location, is the total number of files, , Clusters Chinese users use drones Terminal, Base Station The request arrival rate of the client cache file, , UAVs , Base Station In time slot The file cache collection within, , Clusters Chinese users use drones Terminal, Base Station Single file The request rate, , Clusters The user in the time slot Receive drone Terminal, Base Station Terminal File The average information age of Is the maximum age limit of file information, , In drones , Base Station End, Cluster Average probability of a file request initiated by a user triggering a cache refresh, , UAVs , Base Station To Cluster The average transmission rate of users in It's a drone and drones The transmission rate between , From source node to drone , Base Station The file refresh rate; (92.5) Network update: Randomly extract small batches of samples based on the experience replay mechanism Perform training using the target Q value Update the parameters of the Q network ; (92.6) Action execution: Execute the selected action , update system status , record reward ; (92.7) Termination condition: Continue iterating steps (92.1) to (92.6) until the termination condition of Q value convergence is met; (92.8) After the optimization solution is completed, when using the trained DQN to obtain the file cache strategy, the current system state is input into the trained Q network, the Q value of each action is calculated, and the action with the largest Q value is selected. , the selected action This is the optimal cache strategy for the current time slot.

10. A multi-UAV assisted wireless cache network delay optimization system, used to implement the multi-UAV assisted wireless cache network delay optimization method according to any one of claims 1 to 9, characterized in that: include: The first building module is used to build a wireless cache network architecture model based on the remaining power ratio of the drones and the ground base station to work together, so as to improve the file distribution efficiency and user response speed; The update mechanism improvement module is used to introduce information age as a key indicator of file content freshness based on the wireless cache network architecture model and improve the cache update mechanism; The second building block determines the drone deployment location based on the weighted K-means algorithm based on user location, user activity and user importance to improve the overall user experience; The third building module is used to build an intelligent optimization model, which uses a deep reinforcement learning algorithm to dynamically adjust the cache placement strategy to obtain the optimal cache strategy.

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