Keywords: big data; Rail transit; Networked operation
In the networking of rail transit, a key point of building big data is to build a rail transit big data management platform with global comprehensive strategy. The basic content of the rail transit data management platform is to reorganize and optimize various work processes of existing rail transit services. If the mass data management platform of rail transit service is successfully established, it can ensure that the system information is clear and reliable, facilitate the system communication between different businesses, facilitate the formation of decision-making information, improve the efficiency of rail transit service information management, reduce operating costs and increase profits.
1 Shortcomings of big data in the operation and management of rail transit network
1. 1 Problems in the statistical information collection system of rail transit In most western developed countries, with the development of computer technology in the field of big data, rail transit has gradually realized a steerable and automatic mode. The changes brought by the new mode to rail transit are very important, which is mainly attributed to the construction of rail transit. The integrated management system and the integration of each subsystem with the original production system form an information management platform with statistical and analytical functions. In recent years, China rail transit has also made progress in computerization, but the big data system cannot meet the needs of all business functions. With the long-term development of rail transit department, the data system also has a lot of data, but the data used is still not enough to accommodate the data storage capacity. With the increasing demand for computerization of rail transit services in China, the development of data has also undergone revolutionary changes. With the rapid development of electronic information technology, the statistics department of rail transit plays an increasingly important role in the global scope, but the rail transit statistics information system is not enough to meet the needs on the road.
1.2 Problems in data processing of rail transit
The data collection process of large-scale rail transit statistical service is divided into three levels, namely, base station, transportation office and headquarters. According to different statistical systems, such as passenger freight yard system, freight ticketing system, station baggage system and other major business systems, the daily data volume and total transaction volume are very large, and the daily average business processing task is heavy. It shows that the statistical data of rail transit is huge. With the development of rail transit computerization, the data that may be related to rail transit statistical service is not limited to this system, but more often involves external systems, such as the open operation of railway passenger transport system 12306 customer service center, and the continuous expansion of rail transit information resources has brought great pressure to rail transit statistical system. Diversified data types include not only structured data (such as reports), but also semi-structured and unstructured data (such as voice, video and images) in the process of rail transit computerization. These different types of data characteristics clearly show the change of traffic statistical characteristics. It is almost impossible for different business systems to have the same memory, storage mode and data management mode, especially non-institutional data. How to extract valuable business information from different structured data and analyze the correlation degree of different data in detail is a problem faced by rail transit statistical service at this stage.
1.3 Problems in the data index system of rail transit
The present situation of rail transit data index system The statistical indicators of rail transit can reflect the direct performance of rail transit service production process, financial revenue and expenditure, resource management, etc. The indicators of various activities are closely related and complement each other, and are summarized as a statistical data index system. The most basic requirement of statistical indicators of rail transit data is to reflect the current operating state of rail transit services. Summarizing the achievements of the previous work, we can find various problems existing in the current system, thus providing a reliable solution for the next deployment. Based on different majors and various benchmark reports, the rail transit accounting index system has formed 12 majors, covering passenger transport, freight, luggage, locomotives, buses, trucks, equipment, labor, materials, energy saving, environmental protection and investment. From the data indicators, the relationship between these data indicators is complex, and it is difficult to unify the data caliber, which makes the unified management of big data in rail transit system more complicated.
1.4 can't meet the new demand of rail transit statistics.
With the continuous acceleration of the commercialization of rail transit, the traditional statistical model of "reporting for reporting" is becoming increasingly unsuitable under a series of transformation and development situations, such as the transformation of modern freight organization and the transformation of high-speed rail transportation mode. With the development and management of modern rail transit, the function of rail transit statistics is gradually changing through the development mode of big data technology. Due to the application of big data technology, the functions of rail transit statistics are becoming more and more abundant. Based on big data, the demand and development of statistical activities are deeply and scientifically analyzed, modern and comprehensive rail transit statistical data are developed, and information is obtained through advanced data processing framework. The management platform maximizes the benchmark statistical data, breaking the traditional statistical business process, and the operation process of the rail transit statistical information system has undergone fundamental changes. The traditional data integration processing method replaces the basic part of the traditional station and rail transit bureau, and then gives it to the railway company. The notification method wastes a lot of time.
2. Problems and strategies of big data in the operation and management of rail transit network
2.65438+ From the perspective of rail transit companies, business and statistical information should be organically integrated to make it an effective information management platform, reshape the statistical workflow and ensure the accuracy and timeliness of the original data to the greatest extent, so as to provide a solid decision-making basis for the future reform and innovation of rail transit industry.
2.2 The data analysis strategy of rail transit business system Although some progress has been made in the informatization construction of rail transit statistical services, the overall data center of the system has not yet been established, and there is no unified statistical management method. Therefore, the decision-making content provided by the statistical system is relatively thin, and the existing statistical data have not been fully utilized. This information mainly has the following problems:
(1) The data quality is poor. The computerization level and supervision and management level of local rail transit units are not high. Statistical data errors are usually caused by manual filling, data input and operational errors. In the case of high precision and error, there is no clear statistical integration method, which greatly reduces the quality of input data. (2) The granularity of data collection is excellent. At present, the process of generating the final content of statistical analysis of rail transit data is to collect the existing indicators and get the final result through repeated summary between different levels. However, it is difficult to meet the detailed requirements of some original data and information after the detailed work is implemented in the rail transit department. With more perfect data, the content of statistical work can be improved smoothly.
(3) Low data utilization rate. Usually, the statistical methods of rail transit include processing raw data. The actual information utilization rate is not high, which may not provide the best information value. Rail transit services already have the objective conditions to use big data technology. The next step is to build an information management platform, and discover as many opportunities and values behind information as possible, so as to carry out in-depth data mining, analysis and decision-making to activate statistical services, and production reports will become a powerful goal.
2.3 Analysis Strategies for Problems in the Statistical Indicator System of Rail Transit At present, the scope of the statistical indicators of rail transit can meet the basic statistical requirements at this stage, but the problems existing in the statistical indicator system should not be underestimated. In the new era, one of the key steps in the construction of rail transit statistics informatization is how to innovate and reconstruct the statistical index system, so that it can comprehensively and scientifically reflect the comprehensive strength of rail transit enterprises.
2.4 Big data-driven business strategy Establish a complete statistical data management platform, and integrate data between different business systems in a unified, standardized and compatible way through statistical data warehouse. Gradually integrate the original data information into the information platform, and save valuable information according to the data format, storage requirements and data enjoyment. Establish a rule base, and specify categories, explanations, scales, calculation methods, etc. , improve the quality of data management. The quality of statistical data is the core, and the level of statistical data management needs to be continuously improved. Because of mastering the data processing flow, in order to verify the information of each source point, the data must be modified over time according to statistical rules to ensure the quality of statistical data. In data correction, we insist on avoiding manual preprocessing, using computerized automatic processing and correction functions as much as possible, and assisting manual processing of special problems as much as possible.
Conclusion The development of rail transit system needs a powerful statistical information service integration platform. Through the functional integration and development of data and activities, we can improve the processing level of statistical information, improve the working efficiency of statisticians and improve the management decision-making level and command ability of leaders. Due to the limitation of time and ability, the author can only discuss the advantages of big data in the network operation of rail transit conceptually, but has not conducted extensive analysis and research at the application level. Although the benefits of big data are obvious, it is still in the initial research stage, and its implementation requires high-level scientific design and reasonable development. It is believed that the big data information management system can promote the development of rail transit statistics and has a good development prospect.
References:
Wang Hongchen. Research on Smart Travel Service of Urban Rail Transit Based on Data Driving [J]. Rail Transit Equipment and Technology, 202 1(4):54-56.
[2], Liu Bing, Li, et al. Comprehensive evaluation of urban rail transit network operation safety [J]. Science, Technology and Engineering, 202 1, 2 1 (17): 7340-7347.
[3] [3] He, Xu Wen, et al. Study on the integrated model of urban rail transit network operation information [J]. Urban rail transit, 2015,28 (2): 53-56+60.