Tourism Management Strategies under the Intelligent Tourism IoT Service Platform.

This paper conducts an in-depth study and analysis of the tourism management strategy of the intelligent tourism IoT service platform. The intelligent tourism system is designed from the overall system architecture to the design of each functional module of the system, and the database design is completed with the help of the database E-R diagram. At the same time, this paper uses a weighted regression mathematical method to analyze the passenger flow at different stages of the scenic spot by establishing a scenic spot passenger flow prediction model and giving the results of passenger flow prediction for the scenic spot in the next seven days. In addition, this paper conducts a research based on the ant colony algorithm to establish an itinerary planning and recommendation model to recommend an itinerary that meets the needs of scenic tourists and provides reliable tour guidance based on their expected touring time and touring expenses in the scenic area. The method simplifies the solution of service combination problems in the IoT environment by transforming them into service instance preferences within abstract services. The effectiveness of the method is demonstrated through theoretical derivation. On this basis, the residual energy and energy consumption of smart terminal devices abstracted as cumulative aggregation QoS attributes, and an energy consumption balancing service combination method based on cumulative aggregation QoS is proposed. The countermeasures and suggestions are proposed to increase tourism publicity and give full play to the advantages of tourism resources; strengthen tourism environment monitoring and improve the legal and regulatory system; raise tourism construction funds and strengthen infrastructure construction; improve residents' participation and promote healthy tourism development, as well as to make long-term planning and innovate the management mode; establish a coordinating body and improve the supervision system; establish government macro-control and formulate regulations; and strengthen the management function and the management optimization strategy of cultivating grassroots organizations. Through experimental comparison with other methods, the results show that the method ensures the lifecycle and stability of the entire service portfolio workflow while providing low-energy portfolio services.