Research On The Control System Of Oil Extraction Joint Station Based On PLC

Oilfield exploration units has been very focused on oilfield perforation process operation safety,the complex oil field perforating process operation,because there are many potential safety hazard,attaches great importance to the employees of perforating process expertise training,in order to make the perforation employees quickly grasp the perforating process operating knowledge skill levels,make employees well skilled to operate process and deal with the problem,and reduce the accident rate.Design and develop the skill training and assessment system for perforators,and realize a series of functions of online training and assessment management for perforators.However,the traditional online training system is not intelligent and personalized enough.As the examination system of professional skills and knowledge of oilfield perforation has a large number of questions,employees spend a lot of time and energy to get the training questions they want,so they cannot get specific exercises.This recommendation system is in a traditional online training appraisal system introduced in personalized recommendation technology,according to each perforation employees interested in the knowledge of the situation and collect questions recommend suitable to the practice of test questions,realize the personalized training item recommendation,take the initiative to provide the training to employees try to practice,strengthen the perforating employee training’s enthusiasm and initiative,so as to improve the efficiency of perforating staff training.This paper mainly completes the following contents:1.Preprocessing such as word segmentation was carried out to extract features of the perforation training question topic text,and user interest model was established according to the information collected by users to carry out content-based item recommendation.A series of content-based recommendation processes such as extracting test features,building user interest model,etc.First of all,the data of test questions were preprocessed,and word segmentation and word removal were performed respectively.The vector space model represents the eigenvalues of the training test questions,and the t F-IDF method is used to weight the eigenvalues to establish the user interest model2.By collecting the user’s historical answer data,it can be regarded as the perforating staff’s grading of the training questions,so as to establish the employee-test scoring matrix.The user-based collaborative filtering algorithm was used to find the set of similar employees at the same level,and the Top-n items of the recommended test for similaremployees were taken as the recommendation list.This paper introduces the idea and implementation process of the mixed content-based and user-based collaborative filtering;3.Through the characteristics of different levels of information of perforators to solve the new user cold start problem in the recommendation system;Finally,according to the evaluation criteria of the recommendation system,the recommendation algorithm proposed in this paper is tested and compared with a single recommendation algorithm,which proves that the recommendation efficiency is better and can improve the training efficiency of perforators;4.Based on the analysis of the system flow and the determination of the core algorithm,the functional framework of the system is designed and completed.A personalized training test question recommendation system based on B/S architecture is designed and implemented,and other functions and personalized test question recommendation modules in the system are realized.The item recommendation list is generated based on the calculated item similarity and employee similarity.