Research On Early Warning Method Of Railway Slope Monitoring Based On Blockchain

Recently,preventing,reducing and stopping the slope disaster,and decreasing the economic loss and casualties,caused by the disaster,are considered as the most effective measures in the railway slope disaster monitoring and early warning system.The safety and stability attributes of railway slope monitoring data are important guarantees of railway slope disaster monitoring and warning system.As the trend of railway slope monitoring and early warning is to be daily and real-time,traditional data storage is not applicable in the practice of inputting slope monitoring information.Blockchain technology has the characteristics of immutability,decentralization,encryption and so on,meeting the real-time data storage requirements of railway slope monitoring.According to the actual characteristics of railway slope monitoring and early warning,it is meaningful to design a blockchain system suitable for railway slope.The monitoring data of railway slope is characterized by small samples and the development of slope displacement is nonlinear.The commonly used grey system model and neural network model cannot simultaneously satisfy the two characteristics of slope monitoring data.It is necessary to seek a nonlinear prediction model for small samples.Different slopes have different stability thresholds depending on their own characteristics,so it is essential to find a general slope stability test method on the grounds of dynamic monitoring values.The main research contents and conclusions of this article are included:(1)The Blockchain storage architecture way of railway slope monitoring was designed to provide the theoretical basis for the storage and security of big data.In the light of actual characteristics of railway slope monitoring project,the architectural approach of slope monitoring information chain was proposed,which was grounded in the geographic information.For the application of system automation,the block marking and automaticidentification technology was explored in research.(2)The commonly used prediction models for the railway slope displacement were compared and analyzed in research.Specifically,the applicability of GM(1,1)model,DGM(2,1)model,grey Markov model and neural network model were comprehensively studied.(3)Based on the advantages and disadvantages of the grey system model and the neural network model,one grey neural network model was developed in research,which verifies the applicability and accuracy of the prediction system through the actual data in the slope monitoring and early warning project in Guizhou area.