Construction data self-checking method and system

Abstract

The invention provides a construction data self-checking method and system, and the method comprises the steps of obtaining all construction data in a construction scene, and constructing a construction data matrix; calculating relevancy between data fields in the construction data matrix to obtain a signature matrix; performing feature extraction on the signature matrix by using a pre-trained data anomaly detection model to obtain an output matrix; calculating a mean square error between the output matrix and the signature matrix; and according to the mean square error, determining a self-inspection result of all construction data in the construction scene. According to the construction data self-checking method and system provided by the invention, the association relationship between the construction data in the same construction scene is extracted, the data anomaly detection model is constructed, automatic detection of the abnormal data is realized according to the mean square error between the input and the output of the model, false data and invalid data are prevented from existing, the self-checking precision of the construction data is effectively improved, and the self-checking efficiency is greatly improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a construction data self-inspection method and system. Background technique [0002] In highway engineering, with the continuous development of electronic informatization, the data in the construction management process needs to be entered and archived, and has been ready for acceptance inspection. Data entry needs to be accurate and objective. However, occasionally due to the limited professional level and low sense of responsibility of some construction technicians, the on-site data filling is not standardized, and false data and invalid data are prone to appear. In the actual delivery acceptance inspection, these false data or invalid data cannot truly reflect the construction quality and various process records, resulting in unqualified acceptance, which in turn leads to continuous rectification and repeated inspection of the entire construction project, seriously af...

AI Technical Summary

Problems solved by technology

However, due to the above-mentioned method based on statistical testing, the relationship between the data cannot be effectively used to detect the validity of the data, so the detection efficiency is low and the detection accuracy is poor.

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