Semantic Data Transmission Using AI-Generated Character Elements
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Solution Overview
Problem
Conventional data transmission methods involve transmitting large amounts of data without preprocessing, leading to high resource usage and long transmission times.
Innovation Solution
Utilizing a target intelligence engine to process data into character elements, reducing data volume while maintaining similar meaning, thereby enhancing accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is transmitted without preprocessing, then the original data integrity is maintained, but the data transmission time and resource usage increase significantly
Solution Approach 1:
The patent applies preliminary action by preprocessing data before transmission. The data processing unit converts raw data into structured formats (such as JSON) and compresses it using algorithms like gzip before transmission occurs. This advance preparation reduces the actual transmission time and network resource consumption, directly resolving the contradiction between maintaining data integrity and reducing transmission time.
Solution Approach 2:
The patent segments the data transmission process into distinct stages: data acquisition, preprocessing (including formatting and compression), and transmission. By dividing the original data into structured elements and compressing it, the system reduces the transmission payload size while maintaining recoverability, thereby improving transmission efficiency without losing essential information.
2Productivity
If data is transmitted without preprocessing, then all original information is preserved, but the computing resources required for transmission and processing increase
Solution Approach 1:
The patent performs data compression and formatting as preliminary actions before transmission. The processing unit applies compression algorithms to reduce data size, which decreases both the transmission bandwidth required and the energy consumption during transmission. This upfront processing optimizes resource utilization across the entire data transmission pipeline.
Solution Approach 2:
The patent changes the physical parameters of the data by applying compression algorithms that reduce the byte size of the transmitted payload. By transforming the data into a more compact representation while maintaining its informational content, the system reduces both computing resources needed for transmission and network energy consumption, directly addressing the resource usage contradiction.
3Quantity of substance
If data is processed into character elements, then the data volume is reduced, but the processing complexity increases
Solution Approach 1:
The patent segments raw data into structured character elements using standardized formats like JSON. This segmentation organizes data into manageable, predictable units with defined schemas, which actually reduces processing complexity compared to handling unstructured raw data. The structured format enables efficient parsing and compression while maintaining data recoverability, thus reducing data volume without proportionally increasing processing complexity.
Data Source
AI summary
A data transmission method includes obtaining first data; calling a target intelligence engine, the target intelligence engine being used to generate data consisting of characters; processing the first data to obtain second data based on the target intelligence engine, the second data consisting of character elements; and transmitting the second data, the data volume of the second data being smaller than the data volume of the first data, and the meaning expressed by the first data being similar to the meaning expressed by the second data.