Automated Data Archiving System with AI Feature Extraction
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Solution Overview
Problem
Manual data archiving in various industries is inefficient due to logical errors and human carelessness, leading to data archiving errors, as different types of data require diverse classification and sorting methods.
Innovation Solution
A data archiving method implemented on a computing device that preprocesses data by identifying and extracting feature information using AI natural language processing and neural network algorithms, categorizing and storing data in databases based on specific rules and formats, ensuring accurate classification and sorting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data archiving is performed by staff, then flexibility in handling diverse data types is maintained, but archiving efficiency is low and errors occur due to logical errors and carelessness
Solution Approach 1:
The system enables self-service automated archiving where the data archiving system automatically categorizes and archives data without human intervention. The system extracts feature information from diverse data types, automatically determines appropriate categories, and completes the archiving process autonomously, eliminating manual errors while maintaining high efficiency
Solution Approach 2:
The patent replaces the mechanical manual sorting and categorization process with an automated information processing system. Neural networks and feature extraction algorithms substitute human staff's manual data classification work, automatically analyzing data characteristics and assigning appropriate categories without human intervention
2Measurement precision
If diverse data types are manually classified and sorted, then data categorization can be performed, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs preliminary feature extraction and analysis on data before final categorization. By pre-processing data to extract key feature information and predict appropriate categories in advance, the system reduces both the time required for final categorization and the potential for errors in the classification process
Solution Approach 2:
The patent transforms diverse data types into standardized feature information parameters that can be uniformly processed. By converting various data formats into common feature representations, the system enables efficient automated categorization without time loss and with high precision
Data Source
AI summary
A data archiving method includes obtaining a data format of data to be processed, searching for a data category corresponding to the data format in a first database, extracting feature information of the data to be processed according to a first preset rule if the data category corresponding to the data format is found and storing the feature information in the first database according to a storage rule, and searching the data category corresponding to the data format in a second database according to a second preset rule if no data category corresponding to the data format is found, extracting feature information of the data to be processed, and storing the extracted feature information in the first database according to the storage rule. The first database stores data categories of different data formats of processed data. The second database stores feature information of multiple data categories.