Research On Operation Data Analysis And Appication Key Technologies Of Catering Enterprises

With the rapid and tremendous development of computer and information technology,as well as the comprehensive popularization of network technology,industry data has grown explosively.All walks of life are generating huge and massive data every monment,and these huge data resources have become the important strategic resource for the country and enterprises.Therefore,while bringing significant development opportunities,it also faces great challenges.These large amounts of data reflect the real production ,operation,management and other activities of the enterprises.Effectively managing and using these data,and improving the quality of data applications,can give play to the huge ecnomic and social values behind the data.The data generated by different industries has certain domain,how to effectively mine valuable information from various industries and different massive data has become an urgent problem to be solved in the current practical application.The research in the thesis takes various business data of a large chain catering enterprise in China as the object.Through investigation analysis and research.I have carried out detailed work on some proplems need to be solved urgently in the business operation and informatization profress of the enterprise,especially how to use data analysis and apply the results of data analysis to practical.Specifically,it includes the data analysis and application research on loss prevention audit management of catering enterprises,the business bill data analysis and application research of catering enterprise,and the research on the indoor environment comfort evaluation and monitoring of catering enterprises.The main research work is as follow:First,on the basic of investigation and analysis of various operational management data and audit data of in the enterprise,this thesis proposes a loss prevention audit method based on the business analysis data of catering enterprise.This method is aimed at how to timely warn the abnormal events that may cause loss of corporate profits and remind the managers of the enterprise through technical,and finally find out corresponding internal management progress and personnel that cause the abnormal events.First of all,through of the investigation of the internal process and internal audit status of the enterprise,the types of abnormal events that caused internal losses are clarified.Then,according to the optimal feature sets of different types of abnormal events,the corresponding loss prevention audit model is established by using Naive Bayesian classification algorithm and optimizing the model threshold.Compared with the audit results of manual screening provided by enterprise managers,the experimental results show that the method can effectively identify the abnormal events and give early warning,and can find out the service personnel associated with the abnormal events for the internal auditors of catering enterprise to conduct in-depth investigation.Secondly,based on the bill data generated in the whole operation process of catering enterprises,this thesis proposes a dish recommendation method based on the offline bill data of catering enterprises.This method aims at the problem of how to effectively recommend dishes based on the bill data generated by offline operation.Difference from the data used in personalized recommendation methods based on online customer evaluation,these operational data contain a large amount of recorded data related to customers’ consumption in the restaurant.In the process of dish recommendation,the data mining method is combined with the quantitative analysis of food nutrition and other indicators,and various influencing factors are fully considered to make the recommendation results more real and accurate.Finally,based on the operation bill data of the enterprise,relevant experiments are carried out and compared with the statistical results of the actual bill data.It shows that the recommendation result is effective.Thirdly,the indoor dining environment of catering enterprises is often a place with dense flow of people,which has higher requirements on the comfort of dining environment.The comfort is the subjective feeling of the human body to various external environments.The improvement of comfort can help catering enterprises to improve their sales and brand image,as well as the improvement of the customers` dining experience.In addition,the indoor dining environment is a complex system with multi-factors coupling,which has the characteristics of multi variable,strong coupling,nonlinear and time-varying parameters.The monitoring of various physical environmental parameters by the indoor environmental monitoring network is the basis for the evaluation of indoor comfort,it includes a series of environmental parameters including indoor air temperature,indoor relative humidity and indoor wind speed.Considering the objective environmental factors and human subjective factors,this paper selects PMV index as the basis and combines BP neural network to complete the evaluation of indoor comfort in the catering environment.The simulation results show that the method is effective.In addition,in order to ensure the stable monitoring of relevant environmental parameters in the indoor dining environment comfort monitoring network,a node importance evaluation method in the monitoring network is proposed,which can evaluate the nodes in the network in real time and dynamically can evaluate the importance of nodes in the network in real-time and dynamically.Fourthly,this thesis develops an analysis application system based on the business data of catering enterprises on the basis of the existing research.The system can promptly warn abnormal events in the business operation process of enterprises,which is convenient for enterprise managers to audit and verification,it can analyze the sales situation of dishes according to the historical data and provide effective recommendation results for customers,it can also be used for comfort evaluation and node status monitoring in different indoor areas of different stores.The system function is closely related to some research problems in the related fields of enterprise business data analysis and has certain practical value.The above research contents and achievements reflect several application scenarios and research values on business operation data of catering enterprises.The analysis and research on these data can reflect the operation situation of catering enterprises.It can solve the actual internal problems and forewarn the potential risks,measure the work value of employees and promote post optimization,so that their work can be presented in the form of data.It can predict users’ needs,provide customers with better services and optimize and improve the dining experience.Finally,it can promote the transformation of operation mode,service mode and innovation mode of catering enterprises,realize the value of business operation data,effectively assist enterprises in decision-making,and further improve the operation,management,marketing ability and accuracy of enterprises.