23 results about "Content popularity" patented technology

The invention relates to the technical field of Internet content distribution, and discloses a distributed edge cache acceleration method based on PCDN, comprising the following steps: step I: terminal node access and resource initialization, which includes node registration and authentication, resource information reporting and node classification and label management; step II, hierarchical caching strategy and content storage, wherein the hierarchical caching strategy and content storage comprises content popularity grading, cache writing strategy and cache replacement and elimination; and step III, dynamic content scheduling and distribution. According to the distributed edge cache acceleration method and device based on the PCDN, the content popularity and regional distribution are pre-judged in advance through a deep learning model, and bandwidths and resources of different networks are integrated through a network fusion technology, so that the purposes of reducing the number of times of source returning requests, breaking through the transmission limitation of a traditional single network (for example, only depending on a cellular network) and improving the transmission efficiency can be achieved. The effects of node contribution traceability and automatic execution excitation are achieved.

The invention provides a content popularity prediction method and device and a medium, and the method comprises the steps: determining a target evaluation user set, and obtaining the behavior characteristics of each target evaluation user in the target evaluation user set for to-be-predicted content and the touch scene characteristics; generating a user evaluation feature vector corresponding to each target evaluation user based on the behavior feature of each target evaluation user for the to-be-predicted content and the touch scene feature; inputting the user evaluation feature vector of each target evaluation user into a pre-trained content popularity prediction model, so that the content popularity prediction model calculates the score of the to-be-predicted content in combination with the touch scene features to obtain a comprehensive score of each target evaluation user for the to-be-predicted content, and determining a popularity prediction score of the to-be-predicted content. In this way, richness of the popularity prediction dimension of the to-be-predicted content can be improved, and then the efficiency and accuracy of popularity prediction of the to-be-predicted content are improved.

The application discloses a kind of similar cache content dynamic placement method for mobile edge network, similar cache is introduced in cooperative mobile edge cache, and similar content different from content request can be delivered.First, solve the cache placement problem of highly dynamic content popularity and network connection state in similar cache.Then a kind of similar cache placement algorithm and similar content delivery algorithm based on counter multi-arm tiger machine are disclosed;The problem is modeled as a counter multi-arm tiger machine model;Select a pull arm, call similar content delivery algorithm to process requests, collect the cache reward of the current stage, return to the similar cache placement algorithm, estimate the reward and weight of the arm according to the reward, repeat execution until termination, and the algorithm can obtain an excellent cache placement scheme adapted to the current environment.Through the mode, the application can solve the similar cooperative cache placement problem existing in the prior art.

The invention relates to an optimization method for mobile edge cache based on federated learning. The situation that the user mobility and the content popularity change continuously in the range of a single base station is considered, and the cache hit rate is increased by predicting the content popularity and placing the request content in an edge cache in advance. The method includes obtaining a user moment trajectory table to simulate a moving path of a user by using an RWP random path point model, selecting the user participating in FL local training in a clustering, and threshold value combination mode in consideration of local training consumption, performing global model aggregation by using an attention mechanism to control model weight, and performing global prediction according to an obtained global prediction model. The predicted request content is cached to the server in advance.

The present invention relates to a mean field game optimization method and system in an edge computing network based on content popularity, comprising: (1) setting a system model; including setting an edge computing network, the edge computing network including a cloud data center, a plurality of edge nodes with storage units, and a plurality of users; (2) modeling the video service caching decision problem in the edge computing network as a stochastic differential game problem, i.e., an SDG problem, based on the remaining storage space status of each edge node and the state change of the task popularity based on the content popularity; (3) using the mean field game method to solve the above SDG problem, obtaining the Nash equilibrium of the video service caching decision problem in the edge computing network, and obtaining the optimal caching strategy of each edge node. The present invention focuses on satisfying user needs and takes into account the caching benefits of edge nodes.

The invention discloses a content caching method and device and electronic equipment. The method comprises the steps that user behavior characteristics are obtained, a strong association rule set is determined according to the user behavior characteristics, and the user behavior characteristics at least comprise user value scores, edge cache access information and content fragment access information; determining candidate content fragments recommended to the target user according to the strong association rule group; determining a posterior probability ratio corresponding to the target edge cache according to the candidate content fragments; a target content fragment is determined according to the posterior probability ratio, the target content fragment is cached to the target edge cache, and the target content fragment is the content fragment with the maximum posterior probability ratio in the candidate content fragments. According to the content recommendation method and device, the technical problem that the content caching efficiency is low due to the fact that only the correlation degree between the user and the content is considered when content recommendation is carried out in the prior art and cache replacement is usually carried out by adopting the product of the fixed content popularity and the area request rate is solved.

The application discloses a self-adaptive content caching method, and the steps are as follows: 1) a request processor extracts characteristic parameters of the content access request information and writes the characteristic parameters into an original characteristic database of a local cache server; 2) content popularity in a next time window is predicted and written into a popularity database; and 3) a cache access interface of the local cache server checks whether the local cache server caches the requested content according to the characteristic parameters extracted by the request processor, if the requested content exists, the local cache server transmits the requested content to a user terminal, if the requested content does not exist, the requested content is acquired from an upstream server and transmitted to the user terminal. The application can fully mine and utilize the characteristics of content requests under real network environment and user access conditions, and dynamically adjust a caching strategy to improve caching efficiency.

This invention relates to the field of edge caching technology in the Internet of Vehicles (IoV) and provides a fairness-based joint optimization method for IoV content caching and power allocation. The method includes: mapping content caching and content provider selection to discrete action sets, and power allocation to continuous actions, and associating discrete and continuous actions; mapping the relationship between the content caching stage and the content delivery stage to the association between discrete and continuous actions; introducing a fairness level α and establishing a system utility function based on the fairness level α; defining content value to quantify content popularity and priority, and defining the reward function of the P-DQN algorithm based on the content value and the system utility function; collecting environmental information and defining the state space of the P-DQN algorithm based on the environmental information; and training the action space, reward function, and state space using the P-DQN algorithm. This invention eliminates the need for relaxation of discrete actions or discretization of continuous actions.

The present invention specifically relates to an edge collaborative caching method based on dynamic content popularity, which solves the technical problems that the existing Internet of Vehicles edge caching technology has low caching efficiency, and the efficient edge caching strategy does not simultaneously consider users' different content popularity preferences and collaborative caching between multiple devices. The present invention includes the following steps: 1) determining a training set and a test set; 2) establishing an LSTM-Attention network model; 3) obtaining a dynamic content popularity model; 4) obtaining cluster centers and the number of clusters; 5) obtaining an edge caching decision plan; 6) when requesting content, determining whether the requested content is cached. If it is cached, the edge servers of the vehicle nodes and roadside units with caching capabilities directly feed back the requested content to the remaining vehicle nodes without caching capabilities; otherwise, triggering a remote server, which directly transmits the requested content to the remaining vehicle nodes without caching capabilities, thereby completing the edge collaborative caching of dynamic content popularity.

The present application discloses a method and device for calculating popular content based on a time decay factor, the method comprising: obtaining the user's click-exposure behavior data for the past N days; calculating the number of days between the date when the user clicked / exposed an item and the current date; combining the click-exposure behavior data and the calculated number of days between the date when the user clicked / exposed an item and the current date to obtain a set A; performing an aggregation operation on set A according to a preset field, calculating the daily click volume and exposure volume of each item, and obtaining a set B; calculating the CTR of each item in set B based on the time decay rate, and obtaining an item CTR result set C; filtering the item CTR result set C and then sorting the CTR values ​​in descending order to obtain the final popular result. The present application takes into account the time decay factor of content popularity, realizes the rapid and accurate calculation of content click-through rate, and can effectively realize the update and promotion display of popular content.

The invention provides a price calculation method and device and related equipment, and the method comprises the steps: determining a first revenue function corresponding to N content providers, the first revenue function being used for representing the sum of revenue of each content provider in the N content providers, the first revenue function is determined based on the content popularity corresponding to each content in the C contents, the preference degree of at least one user to each content provider and the content cache hit rate corresponding to each content provider; c is an integer greater than or equal to 1, and N is an integer greater than or equal to 1; based on the first revenue function and the sum of the storage amounts corresponding to the M network operators, determining a preset renting proportion of the base stations corresponding to the network operators; m is an integer greater than or equal to 1; and determining a preset renting price of the base station based on the preset renting proportion and a second revenue function, wherein the second revenue function is used for representing the sum of revenue of each network operator in the M network operators.

The invention relates to a federal learning auxiliary edge caching method based on an AE-DDPM model, and the method comprises the following steps: building an edge computing network system model which comprises a base station, a remote cloud server and a user; an AE-DDPM model based on federated learning is adopted for training; obtaining global prediction content popularity, and caching the most popular N contents according to the cache capacity of the base station; according to the method, potential feature vectors of user data are extracted through the AE model, the data dimension and sparsity are reduced, data distribution is learned through the DDPM model, and high-quality content popularity prediction is generated; according to the method, the cache unit is deployed at the edge node, so that the user can quickly acquire the pre-cached content on the node, the cache hit rate is effectively improved, the time delay of acquiring the content by the user is reduced, the communication efficiency is remarkably improved, and meanwhile, the risk of privacy disclosure of the user is reduced.

The invention discloses a network audio-visual content recommendation method and system based on multiple roles. The method comprises the following steps: receiving a role selected by a user and sent by a front end; querying a content preference tag set corresponding to the role from a pre-constructed tag set, establishing a content tag index, and searching all matched program contents according to the content tag index; selecting a plurality of program contents from all the program contents according to a preset time priority or popularity priority, and forming a candidate recommendation set; de-duplicating the program contents in the candidate recommendation set, and sorting the de-duplicated program contents according to the content popularity to obtain a recommendation list; and sending the recommendation list to a front end for playing in sequence. According to the method, a simple rule matching algorithm is adopted, programs can be directly recommended according to predefined role content preferences, dependence on a complex machine learning model is avoided, content recommendation can be immediately generated after role selection, waiting for model training or data synchronization is not needed, and response delay is reduced.

The invention provides an information processing method and an electronic device, and the method comprises the steps: respectively caching corresponding contents in a plurality of edge caching devices according to the user preferences of a plurality of content request users for a plurality of contents, the content popularity of the plurality of contents, and the pairing relation between the plurality of content request users and the plurality of edge caching devices; constructing a total cost model according to the downloading cost model, the aging cost model and the updating cost model of the plurality of contents in the target time slot; solving the total cost model according to the cache capacity of each edge cache device, the transmission capacity of a backhaul link between the macro base station and the core network and the maximum tolerance information age, and determining a cache strategy and an update strategy of each content in each edge cache device under the target time slot; and according to the caching strategy and the updating strategy, updating the content cached in each edge caching device. The freshness of the content can be ensured, so that the requirement of a user on the content is met.

The media stream delivery system encodes and fragments media streams into numerous media stream fragments maintained on fragment servers. Devices obtain fragments to reconstruct media streams including live real-time media streams for playback on the devices. A device may perform caching of media stream fragments so that particular fragments need not be accessed again from a fragment server. A fragment server or even a content provider can analyze and monitor characteristics of media streams, viewing behavior, content popularity, etc., to identify fragments for caching at the playback devices. Caching indicators along with time period indicators may be included in the media stream fragments.

The invention discloses an ultra-high-definition digital media content distribution method and system based on fragmentation encryption and dynamic caching, and belongs to the technical field of digital media transmission and content distribution. The method comprises the following steps: encrypting an ultra-high-definition media master file, embedding a digital watermark bound with a terminal identity, and then segmenting the file into a plurality of encrypted fragments; the content popularity index is calculated through a metadata server, and the distribution and retention time of encrypted fragments in edge cache nodes are dynamically managed based on popularity, so that hot contents are widely cached, and cold contents are automatically recycled; and the authorized playing terminal downloads the encrypted fragments from a plurality of edge nodes in parallel, and completes decryption, watermark verification, recombination and playing in a local hardware security module or a trusted execution environment. According to the method, the optimal balance of safety, efficiency and cost in the ultra-high-definition content distribution process is realized, and the method has the advantages of full-chain copyright protection, flexible resource scheduling and high-quality playing experience.

The invention discloses a joint optimization method and system for a two-layer UAV-MEC network, and the method comprises the steps: collecting a historical request, and predicting a content request, and obtaining the popularity of the content; on a long-time scale, an upper-layer unmanned aerial vehicle selects a cache and a plan according to the content popularity, and puts the cache and the plan into an experience pool; in a short time scale, the lower-layer unmanned aerial vehicle performs local cache adjustment and unloading selection according to the content popularity, and puts the content into an experience pool; cVaR is introduced, time delay and energy consumption are combined, rewards are formed, and a unified target is constructed; and according to the experience pool and the unified target, carrying out centralized training and updating a multi-agent strategy, and issuing the multi-agent strategy to each unmanned aerial vehicle for execution. The system comprises an upper-layer unmanned aerial vehicle serving as an air small cloud server and a lower-layer unmanned aerial vehicle serving as an air base station. According to the method, the average time delay, the total energy consumption and the tail time delay risk of the system are minimized through collaborative decision making of two time scales. The method can be widely applied to the field of mobile edge computing.

The invention discloses a heuristic collaborative caching method based on a graph convolutional neural network, and relates to the technical field of edge computing caching. Comprising the steps of 1, analyzing a user-content relationship according to a user request based on a mobile edge computing system, and constructing a knowledge graph according to the user-content relationship, and 2, according to the knowledge graph, using a content popularity prediction algorithm of a graph convolutional neural network to calculate the interested probability of a user in the content, and calculating the interested probability of the user in the content according to the interested probability. And step 3, formulating a heuristic cache content placement strategy, and placing the cache content according to the heuristic cache content placement strategy.

The application claims a dynamic cooperative perception content deployment optimization method, belonging to the field of mobile edge network storage and content distribution, specifically comprising: an edge server collects historical traffic data of short video content, uses a GARCH model to extract volatility rate characteristics of the sequence, and inputs the same into a dynamic LSTM model with a fusion attention mechanism together with historical data to accurately predict content popularity at the next moment. On this basis, an edge controller monitors network link status in real time, dynamically calculates a cooperative radius based on network load to determine a cooperation range; then, combined with predicted popularity, neighbor node cache status and access time delay, the cooperative gain of content is calculated, and a hierarchical cooperative deployment strategy is used to store content decisions in local nodes or overflow to neighbor nodes within the cooperative radius. The application reduces service time delay in network congestion and traffic mutation scenarios, and realizes virtual expansion of edge storage capacity and improvement of cache hit rate through a neighbor cooperation mechanism.

The present invention provides a joint optimization method for content update, push, and transmission, and a wireless caching network, which is applied to a central controller of the wireless caching network. The wireless caching network includes: a central controller, at least one macro base station, multiple user devices, and at least one content server. The central controller receives content requests of user devices obtained from the base stations, groups users based on content identifiers and geographical locations, and the content requests of users in the same group are the same. The central controller quantifies the caching value according to the content popularity and file size, combines the utility function with the channel parameters, and generates a file update strategy. The macro base station distributes content to multicast group users and content servers. The content server caches updated files and serves users within its coverage area. This method achieves the dual maximization of the cache hit rate and the user device rate, improves the data transmission efficiency and user experience, and provides an efficient and practical solution for content update, push, and transmission.

This invention proposes a multi-edge collaborative caching method based on robust federated deep learning. First, RoCoCache designs a novel partitioning mechanism for the multi-dimensional cache space, achieving accurate content recommendation within user classification intervals. Next, RoCoCache develops a new discrete classification variational autoencoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, RoCoCache creates a robust federated deep learning-based original training model and an active cache replacement strategy. This invention combines residual-based adversarial model update detection with similarity-based federated aggregation, avoiding model destruction caused by adversarial updates and enabling active cache replacement to adapt to optimized cache resources, thereby improving cache performance.

The invention discloses a content collaborative caching method and system based on digital twinning, and belongs to the technical field of industrial Internet of Things. According to the system, an efficient content collaborative caching strategy is designed according to dynamic content request characteristics of the mobile terminal, calculation and storage resources of edge nodes can be fully utilized through global scheduling of a digital twin layer, the time delay and transmission cost of content acquisition of the mobile terminal are reduced, and the overall service quality and income of the system are improved. According to the method, a real-time interactive digital twinborn layer is constructed based on multi-dimensional data of a terminal and an edge, and real-time sharing of virtual data between different base stations and updating and maintenance of a digital twinborn model are realized; through organic combination of content popularity prediction and global collaborative caching decision based on digital twinning, a global optimal caching strategy is obtained. According to the method, the perception capability of global information can be enhanced, the reliability of system state synchronization is improved, the precision of the global digital twinborn model is improved, and the global optimality of a cache decision is ensured.