Research On Shore Based Monitoring And Health Prediction System Of Offshore Platform

The rapid economic development is closely related to the demand of oil and natural gas,and the offshore platform is widely used as an important structural equipment for oil and gas resources development.However,in recent years,the global climate is changeable,and the offshore platform is located in the harsh marine environment,which is affected by wind,wave and tide load,resulting in the gradual aging of the offshore platform structure and the rapid decline of its bearing capacity.This not only causes damage to the platform facilities,but also poses a great threat to the physical and mental health of people working at sea and exploiting resources.Therefore,it is necessary to establish an intelligent and effective early warning model for the health status of offshore platforms,to evaluate and predict the health status of offshore platforms on a regular basis,and to master their usage and health degree.Aiming at the increasingly prominent problem of health condition assessment of offshore platforms,this paper proposes a health condition prediction model of offshore platforms combining improved genetic algorithm and BP neural network,and develops the health prediction system of offshore platform,the main work is as follows:Firstly,through the study of the current health status of offshore platform structure at home and abroad and combined with the structural characteristics of offshore platform itself,the evaluation indexes and the classification standards of each index are established to measure the health status of offshore platform,and the classification and definition of each level of the health status of offshore platform are carried out.Secondly,through the index system of health condition evaluation of offshore platform,the content of load information collection of offshore platform structure is determined,and the corresponding hardware selection and sensor distribution are carried out,the system framework of the health monitoring of the offshore platform is established.Thirdly,the structure principle,algorithm steps,advantages and disadvantages of error back propagation algorithm(BP)and genetic algorithm(GA)are introduced.The improved genetic algorithm is introduced to optimize the BP neural network.The improved genetic algorithm is used to determine the initial weight and threshold of the BP neural network,and then it is substituted into the BP neural network The optimal solution or approximate solution can be found by training until convergence.Fourthly,this paper analyzes and verifies the monitoring data of an offshore platformas experimental samples,establishes BP neural network model,GA-BP neural network model and IAGA-BP neural network model proposed in this paper to predict the health status of the offshore platform.The simulation results show that the method proposed in this paper not only has fast convergence speed and high accuracy,but also has higher prediction accuracy,which can more effectively evaluate the health status of offshore platforms.Finally,B/S is used as the system framework,combined with the MVC design model to develop the ocean platform health prediction system.Java is used as the background development language,Mysql is used to store and manage the data,Matlab is used to analyze and evaluate the monitoring data.The software is divided into monitoring index classification standard module,platform health status description module,monitoring index information module,platform health status evaluation module and historical monitoring data module,which realizes the prediction and evaluation of the health status of the offshore platform.