Typical driving condition construction method of automobile crane

Abstract

The invention provides an automobile crane typical driving condition construction method, and relates to the technical field of automobile cranes. The typical driving condition construction method of the automobile crane comprises the following steps: collecting operation data of the automobile crane, preprocessing the operation data, and extracting a driving speed-time slice; according to the driving speed-time slice, obtaining a short driving speed-time slice of a normal driving working condition, and determining characteristic parameters of the short driving speed-time slice; constructing a principal part sample matrix according to the characteristic parameters, and performing clustering analysis on the principal part sample matrix to obtain a short driving speed-time slice sample library; and constructing typical driving conditions of the automobile crane according to the short driving speed-time slice sample library. According to the technical scheme provided by the invention, the short driving speed-time slice data of normal driving is collected to construct the typical driving working condition of the automobile crane, so that the working condition clustering and working condition construction precision is improved.

Description

technical field [0001] The invention relates to the technical field of truck cranes, in particular to a method for constructing typical driving conditions of truck cranes. Background technique [0002] The truck crane driving condition refers to the speed-time curve of the truck crane driving under the road, which is mainly used to test the fuel consumption and emission performance of the truck crane and match the power system to achieve the purpose of balancing the power economy of the crane. [0003] In recent years, a series of studies have been carried out on the construction of passenger car driving conditions. Usually, researchers use test vehicles to drive on designated test routes, and then perform short driving segment cutting, clustering algorithms, and short driving segment segmentation on the collected data. Combined methods to obtain typical driving conditions with high accuracy. There is a big difference between the driving conditions of truck cranes and passe...

AI Technical Summary

Problems solved by technology

There is a big difference between the driving conditions of truck cranes and passenger cars. The driving process starts and stops frequently. The driving data includes normal driving conditions, driving conditions in the work area and working conditions. It is difficult to obtain commonly used driving conditions through conventional road tests. The problems caused include but are not limited to: unable to accurately locate the customer's engine load distribution, unable to optimize the engine program and accessories matching, and difficult to balance power and economy

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