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Flammable And Explosive Oil Gas Security Inspection System

Posted on:2020-05-20Degree:MasterType:Thesis
Country:ChinaCandidate:L W XuFull Text:PDF
GTID:2491306452971919Subject:Control Engineering
Abstract/Summary:PDF Full Text Request
With the continuous development of the social economy,flammable and explosive oil gas has been widely used in production and life,and the many acquisition channels of such substances provide an opportunity for lawbreakers.In order to ensure the safety of people’s lives and property,most densely populated places have strengthened security inspections.In the safety inspection,the non-contact detection of flammable and explosive oil gas dangerous goods is the hot spot in security technology research.The technology requires quick and effective identification of the presence of flammable and explosive oil gas dangerous goods in the container without opening it.However,since this technology is still underdeveloped at present,people are still need to open the container and have a drink in some situations,which greatly affects the efficiency of security inspection.In order to improve the efficiency of security inspection,this paper studies and designs a flammable and explosive oil gas security inspection system to achieve fast and efficient security inspection of flammable and explosive oil gas.Firstly,through the analysis of the overall functional requirements of the system,the system is divided into three modules: face capture module,control terminal and flammable and explosive oil gas security gate.Each module of the system transmits data by means of wireless communication.In order to ensure the detection accuracy of the whole security inspection system,this paper calculates the leakage of flammable and explosive oil and gas by referring to the national standard document of plastic bottle mouth and the equation of source mode of evaporation of volatile liquid,which provides a theoretical basis for selecting suitable sensors.In order to solve the wireless interference problem at the security inspection site,this paper adopts frequency hopping spread spectrum technology to enhance the anti-interference ability of the system.In addition,this paper introduces the working principle of BP neural network and the face capture principle based on Harr feature in detail,which provides theoretical basis for system design.Secondly,this paper completes the hardware design and software design of the three modules by analyzing the functional requirements of it.In the hardware part,the article gives the hardware design scheme based on STM32F103R8T6 microcontroller,and introduces the sensor application circuit,signal processing circuit,fault self-diagnosis circuit of sensor,display circuit,wireless communication circuit,fan drive circuit and power conversion circuit.The system software part adopts the modular design idea,uses C language to program,and completes the design of system software main program,fault self-diagnosis program of sensor,wireless communication program,alarm data processing program,image processing program,host computer program,etc.In the wireless communication program part,this paper analyzes and designs the wireless frequency hopping communication protocol in detail,and gives the protocol contents such as frequency hopping sequence table,channel establishment method and data encryption method to solve the system anti-interference problem.In the alarm data processing program part,this paper presents an intelligent alarm algorithm based on BP neural network to solve the defects of traditional alarm algorithm.In the image processing program part,this paper presents a face extraction algorithm based on Harr feature,which is used to extract face information from the live environment for subsequent processing.Finally,the whole system are tested.The test results show that the system can quickly detect the personnel carrying the flammable and explosive oil gas,which has good application value.
Keywords/Search Tags:Flammable and explosive oil and gas, Security inspection system, Wireless transmission, Neural network, Face capture
PDF Full Text Request
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