The power Internet of Things(PIoT) is a smart service system that offers full-state awareness, efficient information processing, and convenient and flexible applications to users. However, these services also pose a risk of privacy leakage. The existing research on privacy protection of power data mainly concentrates on secure aggregation, but seldom addresses the core technology of many basic services, such as KNN query. Unlike traditional relational data, the PIoT collects flowing data of user electricity consumption, and the various power parameters exhibit dynamic correlations. Attackers can use data mining and other methods to infer future trends in data changes. Therefore, this paper proposed a privacy-preserving KNN query method. Firstly, it proposed a similarity measurement model based on bucket distance, and proved the upper and lower bounds of the error between the similarity measurement model based on bucket distance and the similarity measurement model based on Euclidean distance. Through this model, the similarity measurement could be transformed into set intersection operations. Then, it constructed a privacy-preserving function, which could generate different data privacy-preserving functions and query privacy-preserving functions for various smart terminals by substituting different parameters. Based on this, it proposed a data encoding scheme based on bucket partitioning and random number allocation. After being encrypted by the privacy-preserving function, the encoded data possessed the characteristic of ciphertext indistinguishability, and could effectively resist various attacks such as chosen plaintext attacks, data mining attacks, statistical analysis attacks, ICA attacks, and inference prediction attacks. Analysis and simulation demonstrate that the proposed secure KNN query method not only has high security but also has low overhead.