AI Training Platform Mass File Retrieval Using Hash Buckets
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Solution Overview
Problem
Existing file retrieval methods are inefficient and resource-intensive for AI training platforms, leading to long wait times and system resource exhaustion, and are not suitable for handling the specific requirements of mass file retrieval in these platforms.
Innovation Solution
A mass file retrieval method that uses a retrieval thread flow to control service logic, combining sequential encoding with binary search and depth-first traversal to efficiently search through ordered queue folders, preventing resource overconsumption and optimizing retrieval speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If recursive loop through all files is performed for fuzzy matching comparison, then retrieval completeness is improved, but retrieval time and system resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-generating hash values for all files and organizing them into hash buckets before retrieval operations. This preprocessing step creates an indexed structure that enables efficient lookup during fuzzy matching, avoiding the need to recursively loop through all files at retrieval time while maintaining retrieval completeness.
Solution Approach 2:
The patent replaces the mechanical recursive looping system with a hash-based computational system. Instead of sequentially accessing and comparing each file, the system uses hash functions to directly locate potential matches in hash buckets, substituting linear mechanical traversal with efficient computational indexing.
2Measurement precision
If recursive loop through all files is performed for fuzzy matching comparison, then retrieval completeness is improved, but system resource consumption (CPU, memory) increases significantly
Solution Approach 1:
The system performs preliminary hashing and organization of files into buckets before retrieval operations. This upfront preparation creates an efficient data structure that minimizes CPU and memory usage during actual retrieval, avoiding the high resource consumption associated with recursive looping through all files.
Solution Approach 2:
The patent replaces resource-intensive recursive looping with efficient hash-based computation. The hash function approach requires minimal CPU cycles and memory access compared to systematic file traversal, significantly reducing system resource consumption while maintaining retrieval effectiveness.
3Adaptability or versatility
If existing file retrieval technologies are used, then general file management capability is maintained, but retrieval performance is insufficient for AI training platform requirements
Solution Approach 1:
The patent applies local quality by implementing a specialized hash-based retrieval mechanism tailored to the specific needs of AI training platforms. Rather than using generic file management approaches, the system optimizes local retrieval operations with hash functions and bucket structures that are specifically designed for efficient fuzzy matching in large-scale datasets.
Solution Approach 2:
The patent changes the fundamental parameter of file retrieval from sequential access to hash-based direct access. By transforming the retrieval mechanism and changing how files are organized and accessed (using hash values and buckets), the system achieves significantly improved retrieval performance while maintaining adaptability to various file management needs.
4Productivity
If high-configuration hardware devices are deployed for distributed file management, then retrieval capability is improved, but system complexity and resource waste increase
Solution Approach 1:
The patent replaces complex hardware-based distributed file management systems with a software-based hash function approach. Instead of relying on high-configuration hardware devices and complex distributed infrastructure, the system achieves improved retrieval capability through efficient algorithms and data structures that can run on standard hardware, significantly reducing system complexity.
Data Source
AI summary
A mass file retrieval method and apparatus based on an AI training platform, and a device. The method includes: an AI training platform acquiring a retrieval task issued by a user, the AI training platform generating a retrieval thread flow according to the retrieval task, and controlling a service logic of a retrieval process according to the retrieval thread flow; and the AI training platform, performing sequential encoding on files in a database in units of folders, generating ordered queue folders, extracting retrieval key words from the retrieval task, and performing keyword retrieval on each ordered queue folder by combining a binary search and depth-first traversal.