In the research of location privacy protection, the existing methods are mostly based on the traditional anonymization, fuzzy and cryptography technology and little success in the big data environment, for example the sensor networks contain sensitive information, which is compulsory to be appropriately protected. Current trends such as "Industrie 4.0" and Internet of Things (IoT), generate, process, and exchange vast amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks.However, previous methods overlooked the privacy protection issue, leading to privacy violation. In this paper, we propose a location privacy protection method that satisfying differential privacy constraint to protect location data privacy and maximize the utility of data and algorithm in Industrial Internet of Things.In view of the high value and low density of location data, we combine the utility with the privacy and builds a multilevel location information tree model. Furthermore, the index mechanism of differential privacy is used to select data according to the tree node accessing frequency. Finally, the Laplace scheme is used to add noises to accessing frequency of the selecting data. As is shown in the theoretical analysis and the experimental results, the proposed strategy can achieve significant improvements in terms of security, privacy, and applicability.