Research And Application Of Data Fusion Method In Safety Monitoring Of Enterprise Manufacture Based On The Internet Of Things

There are flammable, explosive and toxic characteristics in the raw materials or products in some enterprises, which can easily lead to fire and other production safety accidents that seriously threat people’s life and property safety. And the early detection of fire is an efficient way to reduce the damage caused by the fire. The method of detecting early fire is mainly according to the certain frequency to collecting the related information of the monitoring points to do fusion analysis, but the data collected in a safe state have much redundancy which will make it harder for the data fusion model to mine the knowledge. The structure of traditional fuzzy neural network fusion model relies greatly on the inference rules put forward by experts’ subjective experience. It will need to repeat processing the similar redundant data when processing the data of the early fire monitoring point. So it requires more data and more time to grasp the equal amount of knowledge. Therefore, it is of great significance to seek a better and faster data fusion method to slove the problems in the early fire detection.According to the characteristics of the early fire monitoring point data and the deficiencies that showed in the process of the traditional fuzzy neural network in dealing with these data, this thesis put forward a FIG-NN data fusion method based on the fuzzy information granulation theory and fuzzy neural network to detect early fire. The experimental results show that the FIG-NN method can fully meet the needs of production safety monitoring, and it can give full play to the advantages of fuzzy information granulation to reduce the amount of similar-redundant data, accelerate the training speed and simplify the inference rules. And it can make use of the self-learning ability of the fuzzy neural network to optimize the parameters of the network and process the fuzzy and imprecise data. And the results of the decision fusion model based on the results of the FIG-NN non image data fusion model and the BP image data fusion model have higher credibility and the model can decrease the false negative and positive rate and can improve the fault tolerance of the model.In addition, this paper established a good foundation for the data source of the data fusion analysis. This thesis has improved the CoAP protocol to make it more adapttable to the communication between the resource constrained devices. And then it designed and implemented a communication subsystem based on the improved CoAP protocol to meet the communication requirements of this subject. This eliminates the need for data JSON/XML serialization and de-serialization process and reduces the resource consumption of the resource constrained devices by improving the CoAP message format. At the same time, this paper implemented the open RESTful API in the data center to provide secure data service which can be prepared for the work for data center providing secure API services.The fusion model based on the FIG-NN method is a practice of the fuzzy information granulation theory in early fire detection and is an exploration of the redundant data processing in the training sample, which has a good theoretical and practical significance and referential significance to the researches in related fields. The communication subsystem realized in this paper has broad application prospects in the field of security monitoring.