Research On Key Technologies Of Physical Protection System Based On Internet Of Things

This thesis is according to the special project background and the security requireiments of the physical protection system,on the basis of a certain integrated security monitoring system,a software and hardware system for personnel communication positioning and material status positioning is realized,and a personnel-based real-time communication positioning and material status are developed.In the indoor positioning system for positioning,the fingerprint positioning algorithm based on BP neural network is studied.Mainly include the following:(1)Aiming at personnel supervision in the security area,the ESP32 Internet of Things module is used to develop a wearable tag for WiFi wireless positioning and two-way communication,and a combination of dual anti-removal mechanisms is proposed to prevent tags by collecting heart rate collection and sensing contacts.Disengagement,to meet the requirements of reliable binding of positioning tags and wearing objects,for the security monitoring requirements of materials in the area,a three-tier mechanism using nine-axis acceleration gyroscope sensors,photosensitive sensors and positioning information is proposed to monitor the state of materials,which realizes the realization of materials The design and development of the state positioning system,based on the MQTT communication protocol,the data communication between the upper computer and the lower computer is realized.Combining the functions of the lower computer of each module of the system and the software client of the upper computer,perform experiments on the parameters of each module and analyze the data results.(2)Aiming at the large positioning error of traditional methods,a fingeiprint positioning algorithm based on BP neural network is proposed and implemented.Receive the energy information of the positioning tag through the base station to construct the fingerprint database,use the fingerprint database data set as the training sample set to train the BP neural network to obtain the network model,use the trained BP neural network model to calculate the RSSI data value received by the system in real time to obtain the estimate position.(3)The experimental results show that the positioning system in this paper is reliable.Compared with the fingerprint positioning algorithm of the virtual reference tag position,the estimated average positioning accuracy obtained by the fingerprint positioning algorithm based on the BP neural network in this paper is greatly improved.