Method for verifying integrity of investigation information

Abstract

The invention provides a method for verifying the integrity of investigation information. The method includes the steps that investigation information is read from a database; the investigation information is preprocessed based on a preprocessing rule base, and feature extraction is carried out to obtain feature vectors; the obtained feature vectors serve as input vectors of a fuzzy inference machine, and a feature-level integrity verification result of the feature vectors is obtained; comprehensive decision-making processing is carried out on the feature-level integrity verification result, and an investigation information integrity verification result is obtained. The method suitable for verifying the integrity of the investigation information is designed, and the investigation information is preprocessed three times in a progressive mode; besides, the processing process can be backtracked, so that processing rules are easy to modify, and the investigation information processing efficiency and processing precision are improved; moreover, integrity verification is carried out based on the feature level first, and then integrity verification is carried out after feature-level verification is modified according to conflicts and the Euclidean distance, so that the precision of investigation information integrity verification is improved, and the processing speed of investigation information integrity verification is improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method for verifying the integrity of investigative information. Background technique [0002] When building an information system, even with good design and planning, there is no guarantee that the quality of the stored data will meet the user's requirements in all cases. There is a need for metadata to represent data quality. The four indicators of data consistency, correctness, completeness and minimality are defined in a formal method. According to the extent to which these indicators are satisfied in the information system, the data quality analysis and model in data engineering are put forward, and it is believed that there are many candidate data quality measurement indicators. Users should choose some of them according to the needs of the application. Indicators are divided into two categories: data quality indicators and data quality parameters. The former i...

AI Technical Summary

Problems solved by technology

[0006] Based on the above analysis results, it can be seen that the data preprocessing method in the existing political and legal system only preprocesses the data once, but cannot preprocess the data progressively and retrospectively. The data processing efficiency is low and the accuracy is poor. Moreover, it is difficult to modify the processing rules, and it is impossible to verify the integrity of the extracted massive data, which makes the massive data difficult to use

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