Business School, Xijing University, Xi'an, Shaanxi 710123, China.
Comput Intell Neurosci. 2022 Jan 12;2022:4828108. doi: 10.1155/2022/4828108. eCollection 2022.
In this article, an in-depth study and analysis of the precision marketing approach are carried out by building an IoT cloud platform and then using the technology of big data information mining. The cloud platform uses the MySQL database combined with the MongoDB database to store the cloud platform data to ensure the correct storage of data as well as to improve the access speed of data. The storage method of IoT temporal data is optimized, and the way of storing data in time slots is used to improve the efficiency of reading large amounts of data. For the scalability of the IoT data storage system, a MongoDB database clustering scheme is designed to ensure the scalability of data storage and disaster recovery capability. The relevant theories of big data marketing are reviewed and analyzed; secondly, based on the relevant theories, combined with the author's work experience and relevant information, a comprehensive analysis and research on the current situation of big data marketing are conducted, focusing on its macro-, micro-, and industry environment. The service model combines the types of user needs, encapsulates the resources obtained by the alliance through data mining for service products, and publishes and delivers them in the form of data products. From the perspective of the development of the telecommunications industry, in terms of technology, the telecommunications industry has seen the development trend of mobile replacing fixed networks and triple play. The development of emerging technologies represented by the Internet of Things and cloud computing has also led to technological changes in the telecommunications industry. Operators are facing new development opportunities and challenges. It also divides the service mode into self-service and consulting service mode according to the different degrees of users' cognition and understanding of the service, as well as proposes standardized data mining service guarantee from two aspects: after-sales service and operation supervision. A customized data mining service is a kind of data mining service for users' personalized needs. And the intelligent data mining service guarantee is proposed from two aspects of multicase experience integration and group intelligence. In the empirical research part, the big data alliance in Big Data Industry Alliance, which provides data mining service as the main business, is selected as the research object, and the data mining service model of the big data alliance proposed in this article is applied to the actual alliance to verify the scientific and rationality of the data mining service model and improve the data mining service model management system.
本文通过构建物联网云平台，并运用大数据信息挖掘技术，对精准营销方法进行了深入的研究和分析。云平台采用 MySQL 数据库结合 MongoDB 数据库存储云平台数据，保证数据的正确存储，提高数据的访问速度。优化物联网时态数据的存储方法，采用数据分时存储方式，提高大量数据的读取效率。针对物联网数据存储系统的可扩展性，设计了 MongoDB 数据库集群方案，保证数据存储的可扩展性和数据容灾能力。对大数据营销的相关理论进行了回顾和分析；其次，基于相关理论，结合作者的工作经验和相关信息，对大数据营销的现状进行了全面的分析和研究，重点分析了其宏观、微观和行业环境。服务模式将用户需求类型结合起来，对联盟通过数据挖掘获取的资源进行封装，形成数据产品，并以数据产品的形式发布和交付。从电信行业的发展来看，在技术方面，电信行业已经看到了移动取代固网和三网融合的发展趋势。以物联网和云计算为代表的新兴技术的发展也导致了电信行业的技术变革。运营商面临着新的发展机遇和挑战。还根据用户对服务的认知和理解程度的不同，将服务模式划分为自助服务模式和咨询服务模式，并从售后服务和运营监管两个方面提出了标准化的数据挖掘服务保障。定制化的数据挖掘服务是针对用户个性化需求的一种数据挖掘服务。并从多案例经验集成和群体智能两个方面提出了智能数据挖掘服务保障。在实证研究部分，选取以数据挖掘服务为主要业务的大数据产业联盟中的大数据联盟作为研究对象，将本文提出的大数据联盟的数据挖掘服务模型应用于实际联盟，验证数据挖掘服务模型的科学性和合理性，完善数据挖掘服务模型管理体系。
