Design And Implementation Of Real-time Calculation And Analysis System In Intelligent Station Ticketing System

The development of Internet of Things technology has brought great convenience to people’s lives.Among them,the way of buying tickets for high-speed rail and ordinary trains is also mainly changed from offline to online.However,the network of ticket purchase methods has also brought a large number of illegal scalping tickets such as scalpers.With the technical conditions for ticket purchase superior to ordinary users,scalpers have robbed tickets and hoarded tickets,affecting the travel of ordinary users.Anti-counterfeiting behavior.In addition,the current calculation and analysis system often has a single function,but more and more business needs to be calculated in the ticketing system.After the new algorithm appears,the existing system needs to be quickly docked and used.The old algorithm also needs to be continuously upgraded and replaced Under the premise of the goal of brushing votes,it is necessary to have a highly scalable computing and analysis system to meet more challenges.In order to counteract the yellow cattle brushing behavior and solve the business diversification challenges faced by the existing computing system,this topic is based on the specific business needs of anti-yellow cattle.Taking the improvement of system scalability as a technical indicator,it provides a calculation analysis of an intelligent station ticketing system System design and implementation.This computing system mainly completes the work as follows:1.Analyze the scalping method of scalpers,design a set of anti-ticketing mechanism that combines user historical behavior analysis and real-time online analysis,and complete the project by using K-means clustering and decision tree model,combined with big data realtime computing technology and back-end technology Realized,realized the real-time identification and countermeasure of the yellow cattle brushing behavior in the ticketing system.2.Design and develop AI algorithm service module of ticketing system.Using Django + Sk Learn to develop and implement AI algorithm library and remote RPC call service,realize the storage of commonly used algorithms,custom algorithms(.py format)and the function of providing computing services through network requests,making this system have a high degree of AI algorithm calculation Expandability.3.Design and develop distributed real-time computing service module of ticketing system.In order to meet the high real-time requirements of the system,Flink calculation engine + Yarn + Scala is used as the basic technology.During the ticket purchase window,this calculation module pulls data to realize various business calculation functions such as behavior analysis.It is a key function module for real-time calculation and analysis system to achieve reliable and low-latency calculation under high flow and high concurrency.4.Designed and developed a ticket queuing and distribution module for ticket purchases,combined with a real-time calculation module,to implement a differentiated processing method for users with different priorities,so that ticket purchase requests for ticket users are postponed and penalty for ticket users.After the completion of the design and development,after various unit tests and integration tests,the software reliability and stability of each module were verified,and the overall system function achieved the expected effect.This system can well solve the problem of the first batch of ticket issuance and the scalper itself also uses a large number of user accounts to make alternate ticket purchases.With the existing alternate ticket purchase system,it can strongly respond to the ticketing behavior in the ticketing system.