Driving condition data vacancy filling method and device, computer equipment and storage medium

The missing data in the vehicle driving condition data is processed through the KNN nearest neighbor filling algorithm, which solves the problem of wasted data set information and inaccurate mining results in the prior art, and improves the integrity and analysis efficiency of the data set.

CN119128401APending Publication Date: 2024-12-13CHINA FAW CO LTD
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Patent Information

Application Number
CN202411116618.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-08-14
Publication Date
2024-12-13

AI Technical Summary

Technical Problem

In the prior art, when processing vehicle driving working conditions data, the missing data processing method is limited, resulting in waste of data set information and inaccuracy and inobjectivity of data mining results.

Method used

Using the KNN nearest neighbor filling algorithm, by obtaining the Euclidean distance between the data set to be detected and the driving working condition data set, the Euclidean distance with the Euclidean distance smaller than the preset threshold is selected as the target working condition data, and according to the target working condition data The missing data in the detected data set is filled and the nearest neighbor weight is used to calculate the numerical value of the missing data.

Benefits of technology

Effectively process missing and redundant data, improve data accuracy and reliability, ensure the integrity of the data set, provide high-quality input for data analysis and model training, and improve data mining efficiency and model robustness.

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Abstract

The invention relates to the technical field of driving data processing, in particular to a vacancy filling method and device for driving condition data, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a to-be-detected data set of a driving condition; under the condition that missing data exists in the to-be-detected data set, obtaining an attribute column set and a data type of the missing data; acquiring a driving condition data set with complete data; and filling the missing data of the to-be-detected data set according to the driving condition data set, the attribute column set of the missing data and the data type. By adopting the method, comprehensive data support is provided for data analysis and model training by filling missing values and ensuring the integrity of a data set, original features and information of data are reserved to the maximum extent through an intelligent vacancy filling method, and excessive simplification or misinterpretation is avoided.
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