随着web3.0与工业4.0的到来，信息科技在各行各业蓬勃发展，“互联网+停车服务”也应运而生。然而随着机动车辆的增长与数据隐私安全意识的抬头，造成城市交通隐患以及“数据孤岛”的问题，以往的停车服务系统已经无法满足人们的需求，人们需要更加便利的、安全的、符合自己需求的停车服务。因此本文在“智能停车服务”理念的引导下，研究如何构建安全、全面且个性化的智能停车服务生态系统。本文研究智能停车服务的内容及贡献有以下三点。（1）本文设计一种基于复杂网络模型的停车场服务生态系统，研究三类主要对象及四种关联关系，以此构建四个多层网络模型：服务种族功能网络、服务族群竞争网络、服务调用网络、服务族群合作网络。并基于复杂网络模型定义了相应的指标供分析，包含活力、强健性、组织结构复杂性、创新实现力等等。（2）本文设计一种基于分布式加密矩阵分解推荐的停车场服务生态系统FMFParking，结合分布式算法、同态加密Parllier算法及矩阵分解算法，以用户为中心，保护用户的隐私数据。本文证明了FMFParking系统可以在保证精度的同时提高隐私数据的安全性。数据隐私被安全保护可以吸引更多用户加入到系统中并提供更多隐私特征维度的数据，以此构建更加全面且多元的停车推荐服务系统。（3）本文设计一种基于个性化因素推荐的停车场服务生态系统，定义了六种个性化因素，包含用户画像和停车场画像、停车场预测空车位数、行车时间、步行时间、停车费用、周边服务。依据个性化因素提供用户智能停车服务，包含停车场推荐及周边服务推荐的个性化推荐服务等等。本文使用的数据集是河南省郑州市ETC电子收费停车场的真实历史数据，包含河南省内三百多个ETC停车场及其停车用户为期半年的历史记录。本文基于此ETC数据集应用本文设计的系统及算法，对用户进行推荐服务，得到合理的实验结果，验证了系统及算法的有效性。

With the development of web3.0 and industry 4.0, information technology is booming in all walks of life. "Internet + parking service" has also emerged in the parking service industry. However, with the growth of motor vehicles and the rising awareness of data privacy and security, it has caused the problem of urban traffic hazards and "data islands". The previous parking service system can no longer meet people's needs. People need a more convenient, safe parking service system which meets their needs. Therefore, guided by the concept of "smart parking service", this paper studies how to build a safe, comprehensive and personalized smart parking service system. This paper mainly has the following three points on the research content and contribution of intelligent parking service. (1) This paper designs a smart parking service ecosystem based on complex networks. This paper studies three types of main objects and four types of associations to construct four multi-layered network models: service ethnic functional network, service ethnic competition network, service invocation network, and service ethnic cooperation network. Based on the complex network model, corresponding indicators are defined for analysis, including vitality, robustness, complexity of organizational structure, innovation realization ability, and so on. (2) This paper designs a parking recommendation system called FMFParking based on federated learning. FMFParking combines distributed algorithm, homomorphic encryption Parllier algorithm "Parllier" and matrix factorization algorithm. This system is user-centric and protects users' private data. This paper proves that FMFParking can improve the security of private data while ensuring accuracy. The security of data privacy can attract more users to join into this system and provide data with more privacy characteristics, which can build a more comprehensive and diverse parking recommendation service system. (3) This paper designs a parking service ecosystem based on personalized factor recommendation, and defines six personalized factors, including user portraits and parking lot portraits, prediction of the number of empty spaces, driving time, walking time, parking fees, surroundings service. This paper provides users with intelligent parking services based on personalized factors, including personalized recommendation such as parking lot recommendation and surrounding service recommendation, etc. The data set used in this paper is the real historical data of the ETC electronic toll parking lot in Zhengzhou City, Henan Province. It contains the half-year historical records of more than 300 ETC parking lots and their parking users in Henan Province. Based on this ETC data set, this paper applies the system and algorithm designed in this paper to provide users with recommendation service, which obtains reasonable experimental results and verifies the effectiveness of the system and algorithm.