AI Development Platform Integrating Offline Data Links
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Solution Overview
Problem
The existing AI model development process is inefficient due to the need for offline communication and debugging among multiple parties, leading to low development efficiency across six independent links: data collection, annotation, training, testing, launching, encapsulation, and calling.
Innovation Solution
An AI capability research and development platform with a data management module for data processing, a tool management module for storing and executing tools, and a process management module for model training, allowing for online data collection and model acquisition without offline communication, and including features like data conversion, model storage, and automated back-flow data collection for iteration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If six independent links (data collection, annotation, training, testing, launching, encapsulation) are completed offline by multiple parties, then each step can be independently managed, but communication and debugging among multiple parties result in very low development efficiency
Solution Approach 1:
The patent merges six independent offline links into an integrated online platform that unifies data collection, annotation, model training, testing, launching, and encapsulation. This consolidation eliminates the need for multiple parties to communicate and debug separately, directly resolving the efficiency problem while managing complexity through systematic integration.
Solution Approach 2:
The online platform provides universal functionality to handle all six development links within a single system. Instead of separate offline processes requiring multiple specialists, the platform offers multi-functional capabilities that can be accessed and coordinated centrally, improving productivity while reducing coordination overhead.
2Speed
If data collection and model training are performed offline by different personnel, then resource management can be independent, but the need for offline docking and communication reduces development speed
Solution Approach 1:
The patent implements continuous online processing where data collection, annotation, and model training occur in an uninterrupted workflow within the platform. This eliminates the stop-start nature of offline processes where time is lost to communication and docking, maintaining continuous productive action across all development stages.
Solution Approach 2:
The online platform serves as an intermediary system that mediates between data collection, annotation, and model training functions. Instead of personnel directly communicating and coordinating offline, the platform acts as a central mediator that manages data flow and process coordination, eliminating communication delays.
3Ease of manufacture
If multiple parties perform targeted data collection and annotation independently offline, then each party can manage their own resources, but the docking and integration process becomes complex and time-consuming
Solution Approach 1:
The patent segments the data processing workflow into distinct but integrated modules within the online platform, including data collection, annotation, and model training. Each module can be independently configured and managed, yet they automatically integrate through the platform's unified architecture, simplifying both ease of manufacture and reducing integration complexity.
Data Source
AI summary
Embodiments of the present disclosure provide an AI capability research and development platform and a data processing method. The AI capability research and development platform includes: a data management module, a tool management module, a process management module and a model management module, where the data management module is configured to perform data processing on received data, including at least one of the following: analyzing data type of the data, converting the data according to preset data format and storing the data; the tool management module is configured to store at least one tool, each tool being used to execute a preset processing flow; the process management module is configured to perform model training according to the tool provided by the tool management module and the data provided by the data management module; the model management module is configured to store a model obtained by the model training.