Research On Key Technologies Of Fire Scene Information Recording System

With the rapid development of the economy and the improvement of people's living conditions,electrical products have been increasing.Due to problems such as irregular electricity consumption or aging of the line,disasters such as fire and explosion often occur,Since the internal structure of large buildings is complex,it puts forward higher requirements to the fire rescue Work.As the backbone of emergency rescue such as fire,firefighters shoulder the responsibility of quick and timely rescue.Once a firefighter has an accident,the commander cannot get the specific location and real-time situation of the firefighter,and miss the best rescue opportunity,which will directly threaten the life of the firefighter and even affect the progress of the entire rescue work.In view of the above problems,this paper studies the key technology of the fire scene information recording system and designs the system scheme.The research and design of the system is of great significance to ensure the safety of firefighters and assist rescue decisions.Based on the background of fire rescue work,this paper studies and designs a fire scene information recording system that can obtain real-time information such as firefighter position,movement posture and vital signs in the fire rescue process.Firstly,according to the actual needs of the system,the overall scheme of the system is designed.Then,the key technologies of the firefighters indoor positioning and attitude recognition are analyzed and studied,and finally the system is tested.After comprehensive analysis of the existing indoor positioning technology,Two positioning technologies including the outdoor assisted RSSI positioning and inertial positioning are selected.Among them,the RSSI weighted centroid algorithm is modified and the beacon node deployment problem is analyzed.The experimental results show that the positioning performance is improved by 19.5%,comparing with the uncorrected weight and node optimization.The inertial positioning uses the acceleration double integral estimation displacement and the quaternion attitude solution to obtain the heading angle to achieve indoor positioning,and the offset is corrected and filtered.Processing reduces the effects of accelerometer system errors and noise on displacement estimation.Since the positioning of the RSSI model is susceptible to environment and the inertial positioning will accumulate error with time,the EKF model is selected to fuse the two positioning technologies,and the advantages of the two positioning technologies are utilized to achieve complementary advantages.Secondly,the human body gesture recognition algorithm is analyzed,and the BP neural network algorithm is selected to classify and recognize the human body motion posture.By preprocessing,feature extraction and experimental testing of the attitude data,the test results show that the algorithm can satisfy the gesture recognition demand.Then,select the appropriate model and related circuit design for each functional module in the hardware system,and build the system software development platform and the design of the host computer user interface.Finally,the function of the system is tested and the fusion location algorithm and gesture recognition algorithm are experimentally verified.The experimental results show that the research and design of this paper can meet the basic needs of the system,and it has certain practical value and research significance.