Research on data processing methods for intelligent testing system of automatic instrument for weighing road vehicles in motion

This article is based on the intelligent testing system, and studies the collection status parameter analysis, classification rules and processing methods of abnormal weighing data of Automatic Instrument for Weighing Road Vehicles in Motion, hereinafter referred to as “WIM instrument”, collected online. The system collects weighing data of WIM instrument online, records the operating status parameters of the weighing equipment at the time of data generation, such as vehicle acceleration, output of each weighing sensor, etc. Firstly, combining the data tolerance range and statistical theory methods specified in the metrological technical specifications JJG907-2006, OIML R 134–1, etc., the data is analysed as a whole and anomalies are identified to form an anomaly dataset. Then, based on the abnormal weighing data collected from the Internet of Things, including the operating status parameters, historical verification status, fuel consumption, etc. of the vehicle, the abnormal weighing data is further classified into two categories: abnormal driving weighing data and normal driving abnormal weighing data. Then, the abnormal data is removed for supplementary verification or corrected according to the abnormal driving correction rules to improve the effectiveness of intelligent verification date.