A Real-time Monitoring Approach Based On Data Stream Of The Supply Context Of Oil At The Gas Stations

The international price of oil fluctuates violently and frequently in recent years,and the market demand of oil products has higher volatility around the adjustment of the oil price.Besides,gas stations around construction sites,gas stations near the highway during the holidays,and gas stations in the city when it is in transfer times of taxis are faced with a sharp increase in demand.As a gas station that directly connects the oil supply side and the demand side,its oil reserves are limited.In addition,the formulation of oil distribution plans requires the dispatcher to make a rough estimate of the trend of oil demand in the next day based on human experience.Therefore,before the tanker arrives at the gas station,the oil inventory of the gas station may not be able to meet the demand of the refueling cars,and the supply shortage will occur.However,as an important material of production and subsistence,the oil must be supplied promptly.Otherwise,it will disrupt the social order seriously and even trigger a serious chain reaction.Thus,it is important to monitor the supply state of the oil and perceive the possible lacking time in real time to issue warnings timely and finally guarantee the supplement.The application and development of Internet of Things(IoT)technology provide decision-makers with transparent and real-time online decision environment,making it possible for online real-time monitoring of the consumption of oil products.However,the decision process of real-time monitoring of oil consumption state of gas stations based on data stream not only needs to perceive the possible lacking time in real time,but also needs to extract the effective context information of oil demand,and ultimately provides useful decision support.The decision process will bring about the following problems: 1)In the process of real-time perceiving the possible lacking time of oil supply at the gas station based on the continuous coming,real-time changing,multi-source data streams,the anomaly of oil supply at a gas station is affected by the current context,such as the consumption rate of oil,the arrival times of tankers,the current oil stock levels,etc.Thus,the possible lacking time changes constantly and it is hard to be captured in real time.2)In the decision process of extracting the effective oil demand state information from the data streams,it is hard to find a way to describe the state of oil demand in a comprehensive way due to the randomness of the oil demand status of the gas station and the arrival of the vehicle and the refueling amount of the vehicle.At the same time,it is hard to extract useful information from a large amount of dynamically changing data in real time.This leads to the difficulties of the real-time monitoring and recording of oil demand context.In terms of this problem,the main work of this research is as follows:(1)A real-time rolling approach to perceiving the lacking time of oil supply is designed.Firstly,the data analysis of the liquid level gauge is carried out.Secondly,based on the data analysis,a rolling update method is proposed to detect the real-time point of the lacking point.(2)A context-based real-time monitoring approach of the demand state of the oil in queuing oiling system is designed.First of all,based on the actual research,we determine the classification criteria of the context patterns of the oil demand state.Secondly,we determine the probability model of the expected consumption rate of oil under each pattern.(3)Numerical analysis and simulation experiments.This paper verifies the efficiency of the proposed approach by consulting simulation models on Arena simulation platform based on real-life investigation.This study can provide decision support for the real-time monitoring of oil supply context state,and it has a certain enlightening significance for other similar problems with the requirement of the real-time monitoring.The research results enrich the theory of real-time monitoring of contextual state under the environment of IoT.It has some inspiration on how to enhance the real-time capability,scientificity and intelligence of context monitoring at the same time.