Data Transmission System Using NLP and Dual Cloud Frameworks
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Solution Overview
Problem
Current data transmission systems face challenges in efficiently handling real-time and non-real-time information demands, leading to a heavy operational burden on servers when connected to various electric devices, as they often require frequent device-side agreement docking and lack effective unification of different data interfaces.
Innovation Solution
A data transmission system utilizing a Natural Language Processing (NLP) engine to classify text messages as real-time or non-real-time demands, with a service cloud framework for real-time information and a buffer cloud framework for non-real-time information, allowing for efficient data retrieval and reducing server load by proactively updating data from third-party servers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a web crawler is used to search massive network information and save to the cloud, then high quality search services can be provided, but the operational burden of the server becomes heavy
Solution Approach 1:
The patent segments the information retrieval system into two distinct frameworks: a service cloud framework for real-time information and a buffer cloud framework for non-real-time information. This segmentation allows each framework to handle specific types of queries efficiently, reducing the operational burden on the server while maintaining high search quality through specialized processing paths.
Solution Approach 2:
The buffer cloud framework performs preliminary actions by proactively downloading and storing non-real-time information from third-party servers in advance. This preliminary data collection reduces the need for frequent real-time server queries, thereby lowering operational burden while ensuring high-quality search results are available when needed.
2Adaptability or versatility
If the device side does the docking of the agreement with different clouds, then device flexibility is maintained, but the operational burden of the third-party server remains heavy
Solution Approach 1:
The patent introduces cloud frameworks as intermediaries between devices and third-party servers. The service cloud framework and buffer cloud framework act as mediators that handle the complexity of docking agreements with different clouds, allowing devices to maintain flexibility while the cloud frameworks manage the operational burden by centralizing the docking logic.
Solution Approach 2:
The cloud frameworks provide universal interfaces that can handle multiple types of information retrieval requests from different devices. The service cloud framework handles real-time queries while the buffer cloud framework handles non-real-time queries, creating a multi-functional system that reduces server operational burden through standardized processing.
3Reliability
If real-time information is always queried from third-party servers, then up-to-date information is ensured, but server load increases
Solution Approach 1:
The buffer cloud framework performs preliminary action by proactively downloading and storing non-real-time information from third-party servers in advance. This allows the system to serve non-real-time queries from cached data, reducing the frequency of server queries and lowering server load while ensuring information availability.
Solution Approach 2:
The patent segments information retrieval into real-time and non-real-time paths. Real-time information queries go through the service cloud framework that contacts third-party servers when needed, while non-real-time queries use the buffer cloud framework that serves from pre-downloaded data, optimizing server resource allocation.
Data Source
AI summary
The present invention provides a data transmission device and method thereof. A data transmission system includes: an electric device generating an input message; a Natural Language Processing (NLP) engine connected to the electric device, receiving the input message, and judging whether an output message corresponding to the input message is available on a real-time basis or on a non-real-time basis; a plurality of third-party servers; a service cloud framework connected to one of the NLP engine, the electric device, and the plurality of third-party servers to obtain a real-time information; and a buffer cloud framework connected to one of the NLP engine, the electric device and the plurality of third-party servers, proactively downloading at least one updated data from the plurality of third-party servers once in a predetermined period of time, automatically saving the at least one updated data therein, and obtaining a non-real-time information therefrom.