万峰林“淡季不淡”数据分析模型

通过多源异构数据融合技术，对平台订单数据、园区摄像头热力图、小桔子 life 平台搜索数据（频率、点击率）等进行清洗与标准化处理（剔除异常订单、统一数据格式）；采用回归分析算法构建价格弹性模型，识别不同客群对票价的敏感系数；运用时间序列分析与趋势预测算法，对 2019-2024 年游客数量、消费结构等数据进行长期趋势拟合；对游客个人信息（如身份标识、精准地理位置）进行匿名化处理，对摄像头热力图数据进行区域级模糊化加工，确保数据合规性。

Leveraging multi-source heterogeneous data fusion techniques, we first clean and standardize multiple datasets including platform order data, thermal maps from park cameras, and search data (including search frequency and click-through rate) from the Xiaojuzi Life platform. The cleaning and standardization procedures involve removing abnormal orders and unifying data formats. Next, a regression analysis algorithm is adopted to construct a price elasticity model, which identifies the sensitivity coefficients of different customer groups towards ticket prices. Then, time series analysis and trend prediction algorithms are applied to fit long-term trends for datasets such as tourist volume and consumption structure spanning from 2019 to 2024. Finally, personal information of tourists (such as identity identifiers and precise geographic locations) is anonymized, and thermal camera map data is processed with regional-level blurring to ensure data compliance.