车联网智能协同缓存策略：车辆匹配、内容推荐与资源优化

梁琰; 张海君; 任超; 马旭; 任语铮

doi:10.12263/DZXB.20260065

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车联网智能协同缓存策略：车辆匹配、内容推荐与资源优化

更新时间：2026-04-14

- 车联网智能协同缓存策略：车辆匹配、内容推荐与资源优化
- Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization
- 电子学报 2026年页码：1-16
- 作者机构：
  
  1.北京科技大学计算机与通信工程学院，北京 100083
  2.北京科技大学人工智能学院，北京 100083
- 作者简介：
  
  [ "梁琰男，1998年1月出生于湖北省孝感市。现为北京科技大学计算机与通信工程学院博士研究生。主要研究方向为未来智能网络、车联网。E-mail: liangy188@126.com" ]
  [ "张海君男，1986年10月出生于辽宁省朝阳市。现为北京科技大学人工智能学院教授、博士生导师。主要研究方向为智能通信、无人机通信、卫星通信。中国电子学会会员编号：E190020833S。 E-mail: zhanghaijun@ustb.edu.cn" ]
  [ "任超 1989年4月出生于山东省菏泽市。现为北京科技大学计算机与通信工程学院副教授。主要研究方向为无线多模态通信、未来移动网络、智能通信。中国电子学会会员编号：E190157259M。E-mail: chaoren@ustb.edu.cn" ]
  [ "马旭男，1996年7月出生于山东省菏泽市。现为北京科技大学计算机与通信工程学院博士研究生。主要研究方向为6G移动通信、空天地一体化网络。 E-mail: maxu@xs.ustb.edu.cn" ]
  [ "任语铮女，1995年3月出生于河北省保定市。现为北京科技大学计算机与通信工程学院副教授。主要研究方向为未来网络、智能内生网络。中国电子学会会员编号：E190157261M。 E-mail: renyuzheng@ustb.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62225103;U22B2003;U2441227;U24A20211);北京市自然科学基金(L241008);中央高校基本科研业务费(FRF-TP-22-002C2);小米青年学者
- DOI：10.12263/DZXB.20260065
  中图分类号： TN929.5;
- 收稿：2026-01-16，
  
  录用：2026-02-24，
  
  网络首发：2026-04-14，
- 稿件说明：
移动端阅览
梁琰, 张海君, 任超, 等. 车联网智能协同缓存策略：车辆匹配、内容推荐与资源优化[J/OL]. 电子学报, 2026,1-16.

LIANG Yan, ZHANG Haijun, REN Chao, et al. Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-16.
梁琰, 张海君, 任超, 等. 车联网智能协同缓存策略：车辆匹配、内容推荐与资源优化[J/OL]. 电子学报, 2026,1-16. DOI： 10.12263/DZXB.20260065.

LIANG Yan, ZHANG Haijun, REN Chao, et al. Intelligent Cooperative Caching Strategy of Internet of Vehicles: Vehicle Matching, Content Recommendation and Resource Optimization[J/OL]. ACTA ELECTRONICA SINICA, 2026, 1-16. DOI： 10.12263/DZXB.20260065.

摘要

自动驾驶、高清地图更新及车载娱乐等车联网应用的规模化落地引发数据量爆发式增长，对缓存任务调度、内容处理推荐及网络资源分配提出了更高要求。协同缓存作为缓解车联网核心网络负载、降低内容获取端到端时延的关键技术，其性能高度依赖车辆匹配、内容推荐与资源优化三个核心环节的深度协同，而现有方案普遍存在车辆匹配稳定性差、推荐适配性不足、资源优化效率偏低等问题，难以适配高动态车联网环境的需求。为了应对这些挑战，本文从通信匹配、内容适配、资源优化三个维度展开研究，提出一套车联网智能协同缓存策略。首先，针对车辆高移动性导致的通信链路稳定性差、频繁切换等问题，设计短期车道预测驱动的车辆协同匹配算法，综合挖掘车道流量时空相关性、车辆通信速率及节点资源占用率等多维度特征，实现需求车辆与服务车辆、路边单元的协同匹配，为内容推荐与资源优化构建稳定的通信基础。其次，针对传统缓存推荐策略对新老用户群体适配性不足、个性化指标优化不充分等问题，构建融合协同过滤与内容特征的混合推荐算法，针对新用户设计流行度-多样性双维度推荐策略解决冷启动问题，针对老用户依据历史行为动态调整协同过滤与内容特征的推荐权重，有效提升缓存内容与用户需求的契合度，较基准算法使推荐命中率提升21.11%、F1分数等推荐指标提升20.94%，减少冗余数据传输与对远程云端资源的依赖。最后，面向协同匹配关系与内容推荐分布形成的动态环境状态，提出基于软演员评论家的资源优化算法，通过最大化策略熵实现缓存任务分解与带宽资源分配的自适应动态决策，较TD3（Twin Delayed Deep Deterministic Policy Gradient）和DDPG（Deep Deterministic Policy Gradient）算法使缓存任务成本分别降低5.80%和13.27%。仿真实验结果表明，所提智能协同缓存策略在通信效率、推荐指标、资源调度经济性等方面均优于现有基准算法，能有效适配车联网高动态特性，为“车路云一体化”场景下的协同缓存系统设计提供了有效理论支撑。

Abstract

The large-scale deployment of Internet of Vehicles (IoV) applications such as autonomous driving

high-definition map updates and in-vehicle entertainment has triggered an explosive growth in data volume

imposing higher requirements on cache task scheduling

content processing and recommendation

as well as network resource allocation. As a key technology to alleviate the network load of IoV and reduce the end-to-end delay of content acquisition

cooperative caching’s performance highly relies on the in-depth collaboration of three core links: vehicle matching

content recommendation and resource optimization. However

existing schemes generally suffer from poor stability of vehicle matching

insufficient recommendation adaptability and low resource optimization efficiency

making it difficult to adapt to the demands of the highly dynamic IoV environment. To address these challenges

this paper conducts research from three dimensions of communication matching

content adaptation and resource optimization

and proposes an intelligent cooperative caching strategy for IoV. Firstly

aiming at the problems of poor communication link stability and frequent handovers caused by the high mobility of vehicles

a short-term lane prediction-driven vehicle cooperative matching(SLPVCM) algorithm is designed. This algorithm comprehensively explores multi-dimensional features including spatiotemporal correlation of lane flow

vehicle communication rate and node resource occupancy rate

to realize the collaborative matching between demand vehicles

service vehicles and roadside units

thus establishing a stable communication foundation for content recommendation and resource optimization. Secondly

in view of the insufficient adaptability of traditional cache recommendation strategies to new and existing user groups and the inadequate optimization of personalized indicators

a hybrid recommendation based on collaborative filtering and content features(HRCF2) algorithm is constructed. For new users

a dual-dimensional recommendation strategy of popularity and diversity is designed to solve the cold-start problem; for existing users

the recommendation weights of collaborative filtering and content features are dynamically adjusted according to historical behaviors

which effectively improves the matching degree between cached content and user demands. Compared with the benchmark algorithms

this algorithm increases the recommendation hit rate by 21.11% and improves recommendation indicators such as F1-score by 20.94%

while reducing redundant data transmission and the dependence on remote cloud resources. Finally

facing the dynamic environmental state formed by collaborative matching relationships and content recommendation distribution

a soft Actor-Critic based vehicle caching tasks and resources optimization(SVCTRO) algorithm is proposed. By maximizing the policy entropy

this algorithm achieves adaptive and dynamic decision-making for cache task decomposition and bandwidth resource allocation

reducing the cache task costs by 5.80% and 13.27% respectively compared with the Twin Delayed Deep Deterministic Policy Gradient(TD3) and Deep Deterministic Policy Gradient(DDPG) algorithms. Simulation experimental results show that the proposed intelligent cooperative caching strategy outperforms the existing benchmark algorithms in terms of communication efficiency

recommendation indicators and resource scheduling economy

and can effectively adapt to the highly dynamic characteristics of IoV. It provides an effective theoretical support for the design of cooperative caching systems in the vehicle-road-cloud integration scenario.

关键词

Keywords

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