# Research on Monitoring and Early Warning of the Mine Backfill System Based on Blockchain Technology
###### Abstract
Recently, machine learning has been increasingly used to detect faults during system operation [15; 16]. As a typical method of machine learning, transfer learning, can train existing models using small samples of data to obtain training models suitable for different scenarios. [PERSON] et al. [17] designed a convolutional neural network model training method based on transfer learning that can automatically extract effective fault features from vibration signals and complete fault diagnosis of electric pumps using only a small amount of sample data. Based on the idea of classification learning, [PERSON] [18] proposed a fault diagnosis method based on transfer learning to achieve automatic and efficient diagnosis of equipment faults on the basis of fault diagnosis knowledge representation, fault diagnosis algorithm research, and similarity calculation of fault vectors. [PERSON] et al. [19] proposed a rolling bearing fault diagnosis method based on AlexNet and transfer learning. The collected data were set as training samples and trained in the finely tuned, pre-trained AlexNet network. During the test, it was found that the method can still be used to diagnose the common fault types of rolling bearings when the marked fault data are scarce. Aiming at solving the problems of fewer data samples and the low data storage safety performance of collecting equipment in a mine filling system, this paper constructs a mine filling overlimit early warning system based on blockchain and transfer learning, which mainly includes the following two models:  
(1) Overlimit warning model: based on transfer learning, an overlimit warning model is established to collect the operation data generated by the equipment during the operation of the mine filling system. The convolutional neural network is used to predict the future change in the equipment operation data over a certain period of time, and all the data are detected to obtain the time of the future overlimit situation for different equipment;  
(2) Data management model: data storage and retrieval are realized by combining the blockchain and the IPFS. After the overlimit data in operation are detected by the over-limit early warning model, the administrator stores the overlimit data on the IPFS and the returned file identifier on the blockchain to ensure the security performance of data storage. At the same time, alliance chain technology is used to design the data retrieval processes of different users. The administrator can directly retrieve the required data after obtaining the access credentials given by the third-party trust institution, and the user needs to obtain the required data from the administrator after obtaining the access credentials issued by the trust institution to ensure the performance of data transmission is secure. The rest of this article is arranged as follows. Section 1 introduces the preliminary knowledge. Section 2 describes the design goals and model framework of the mine filling overlimit warning system. Section 3 presents a system for experimental evaluation and theoretical analysis to explain the advantages of the model. Section 4 summarizes the paper.