Data Transfer Protocol Using Result Model Regeneration
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Solution Overview
Problem
Conventional data transfer protocols, such as the LISA protocol, face inefficiencies, compatibility issues, limited extensibility, and inadequate error handling, particularly when transferring large volumes of data across disparate systems, which hampers real-time data visibility and response to manufacturing exceptions.
Innovation Solution
A method and system for transferring data that involves sending a data query from a client to a server, receiving a data result model with a table block, and analyzing it to regenerate data for storage, including processing non-null and null values, thereby enhancing efficiency, reliability, and compatibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data protocols (e.g., LISA) are used to transfer data, then data transfer can be performed, but the transfer efficiency is poor and large volumes of data cannot be transferred effectively
Solution Approach 1:
The data transfer process is segmented into distinct phases: query phase, transfer phase, and error detection phase. The protocol divides data into manageable units with specific control mechanisms for each segment, allowing efficient processing and reducing overall transfer time while maintaining high productivity.
Solution Approach 2:
The protocol performs preliminary actions by establishing error detection capabilities before data transfer begins. The system prepares error handling mechanisms in advance, allowing for uninterrupted efficient data transfer without waiting for error conditions to manifest during the transfer process.
2Adaptability or versatility
If conventional data protocols are used, then data transfer is possible, but compatibility issues arise between different versions and systems
Solution Approach 1:
The data transfer protocol is designed with universal compatibility features that allow it to function across multiple system versions and platforms. The protocol incorporates standardized data structures and control mechanisms that can be universally applied while maintaining reliable data transfer across diverse environments.
Solution Approach 2:
The protocol acts as an intermediary layer between different data systems and versions, providing translation and adaptation mechanisms that ensure compatibility while maintaining transfer reliability. This mediator approach allows disparate systems to communicate effectively without compromising data integrity.
3Reliability
If conventional data protocols are used, then data can be transferred, but error handling is inadequate and requires prior knowledge of data set errors
Solution Approach 1:
The protocol implements continuous feedback mechanisms during data transfer, where error detection information is fed back to the transmitting system in real-time. This allows the system to adjust and correct errors during transfer without requiring prior knowledge of error conditions, improving reliability while managing complexity through systematic feedback loops.
Solution Approach 2:
An intermediary error detection mechanism is introduced that sits between the data source and destination, automatically detecting and handling errors without requiring complex preprocessing or prior error knowledge. This mediator simplifies the overall system complexity while enhancing error handling reliability.
Data Source
AI summary
A system and method for transferring data are provided. The method comprises: sending, from a receiving device, a data query to request data from a sending device; receiving, from the sending device, a data result model in response to the data query, the data result model including a table block; analyzing the data result model to regenerate the data for storage at the receiving device in accordance with the table block; and providing the regenerated data for storage at the receiving device.