Digital auditing method for vehicle

Abstract

The invention discloses a digital auditing method for a vehicle, and the method comprises the following steps: A, extracting a vehicle driving track and refueling bill data to the local, processing the data, and importing the data into a database template; B, recording by using a vehicle GPS system, and performing integral operation and weighted calculation on longitude and latitude data difference values; and C, establishing a logic analysis model by taking the driving kilometers and the refueling cost as driving data, editing a logic judgment program, and finally performing vehicle fuel consumption comparison and refueling authenticity verification. The method has the advantages of active sensing, instant early warning and auxiliary decision making; the auditing work efficiency can be greatly improved. Meanwhile, the oil consumption condition of the vehicle can be dynamically monitored. Longitudinal comparison on a time axis and transverse comparison among different drivers are formed, so that the burden of auditors is reduced, human errors can be reduced, the auditing quality is improved, and the problems of irrelevance of vehicle management mass data, non-intuitiveness of dataanalysis results and the like in the supervision work are solved.

Description

technical field [0001] The invention relates to the field of Internet of Things and deep learning, in particular to a method for digital auditing of vehicles. Background technique [0002] For a long time, the supervision of vehicle operation and expenses in the vehicle system has been a pain point for supervisors and auditors. According to statistics, in 2000, the database in the system had only about 3TB of data, and it was all unstructured. In 2010, this data has grown to 96TB. Today, the data volume in the system has reached 103,000 TB, and there is a data increment of 10TB every day, and the vehicle operation data has the characteristics of poor correlation. The supervision and review method of manually reviewing data has low efficiency and small coverage, and the quality of work is highly dependent on the work experience and quality of the inspectors. The analysis results are not intuitive, and it is difficult to quickly find problems, identify problems, and analyze th...

AI Technical Summary

Problems solved by technology

[0002] For a long time, the supervision of vehicle operation and expenses in the vehicle system has been a pain point for supervisors and auditors. According to statistics, in 2000, the database in the system had only about 3TB of data, and it was all unstructured. In 2010, this data has grown to 96TB. Today, the data volume in the system has reached 103,000 TB, and there is a data increment of 10TB every day, and the vehicle operation data has the characteristics of poor correlation. The supervision and review method of manually reviewing data has low efficiency and small coverage, and the quality of work is highly dependent on the work experience and quality of the inspectors. The analysis results are not intuitive, and it is difficult to quickly find problems, identify problems, and analyze the reasons from dynamic data of different dimensions. , creating a vacuum in the field of vehicle regulation

[0003] Vehicle operation management has the characteristics of huge data volume, different calibers, and poor correlation. According to market research, enterprises, government agencies and other units still use traditional manual sampling methods for verification of vehicle management. Analysis, work quality and efficiency are difficult to be guaranteed

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