Research On Location Method Of Industrial Internet Of Things Fingerprint Library

With the continuous development of China's economy,industry is under pressure from industrial upgrading and transformation.The Industrial Internet of Things(IIoT)emerged as the times require.It has the characteristics of comprehensive sensing,interconnected transmission,and intelligent processing.It is widely used in many fields such as intelligent transportation,smart factories,smart grids,and intelligent environment detection.As the basic component of industrial IoT operation,a precise location information is the premise and key to realize the interconnection of industrial IoT.The industrial Internet of Things is mostly in the indoor environment,and the existing positioning technology,especially the indoor positioning technology,cannot meet the needs of computing applications in terms of cost of use,application range and portability,and limits the development of industrial Internet of Things.application.The indoor location technology based on location fingerprint database has become a research hotspot in the field of location perception and industrial Internet of Things in recent years due to its wide range of applications and low cost of location system.In order to realize high-precision and high-efficiency indoor positioning in industrial IoT environment,this paper has done a lot of research on indoor signal propagation characteristics and fingerprint database localization algorithm,and proposed industrial IoT indoor fingerprint database which balances firefly optimization K-means algorithm.The positioning method,the main work and innovation of the paper are as follows:(1)Based on the wireless indoor location fingerprint database location theory,this paper analyzes the location characteristics of industrial indoor fingerprint database.Actually collect wireless signal data,study the time-varying problem of received signal strength,analyze the cause of signal strength change,and propose data preprocessing based on Gaussian filtering to solve the white noise generated during data acquisition and construct a fingerprint database.(2)Aiming at the problem of low matching efficiency in the traditional fingerprint database positioning algorithm,a cluster analysis was performed on the location fingerprint database.In combination with the characteristics of the changing environment under the condition of the Industrial Internet of Things,a K-means clustering algorithm was used.Aiming at the random selection of the initial centroid in the K-means algorithm,the clustering results are greatly affected.Different optimization algorithms are analyzed.A balanced firefly optimized K-means clustering algorithm is proposed to solve the problem of low matching efficiency of the fingerprint database.The K-means clustering algorithm based on the optimized firefly is used to divide the fingerprint database into sub-regions,and the entire fingerprint database is divided into multiple sub-regions.Each sub-region contains a clustering center for region selection.In the online positioning stage,the sub-area is prioritized.The sub-area where the unknown point is located is selected as the target area.The coordinates of the unknown point are calculated using the K nearest neighbor algorithm.The algorithm analysis and experimental comparison prove the effectiveness and feasibility of the method.(3)This paper simulates the industrial IoT environment,so that the location area contains two wireless signals,Wi-Fi and LoRa.Due to the large library shelves and large staff turnover,the existing Wi-Fi environment is used and the LoRa environment is built to meet the complexity of the industrial environment.Therefore,the positioning method proposed in this paper is tested in the library and compared with the traditional industrial Internet of Things indoor fingerprint library positioning method.The results show that the positioning method proposed in this paper has better positioning performance and can better meet the needs of industrial indoor positioning.