Automated Data Classification via Weighted Feature Fusion
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Solution Overview
Problem
Conventional data classification and analysis methods require manual support, leading to inefficiencies and low accuracy, especially when dealing with complex data types from various sources.
Innovation Solution
A method and apparatus for data processing that utilize a service processing model to classify and process data automatically. This involves obtaining service processing instructions and virtual asset-associated data, applying an asset data classification rule, and using weight model parameters to enhance feature vectors for targeted processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data classification and analysis methods are used, then human judgment and flexibility are applied, but processing efficiency is low and time consumption is high
Solution Approach 1:
The patent implements automated data classification and analysis through a service processing model that autonomously handles data without requiring manual intervention. The system automatically processes service processing instructions, performs data classification based on asset data classification rules, and generates analysis results independently, thereby eliminating the need for human operators and significantly improving processing efficiency while reducing time consumption.
2Measurement precision
If manual data analysis is performed on complex data types from various sources, then human expertise can be applied, but it becomes difficult to find important data and accuracy decreases
Solution Approach 1:
The patent segments complex data from various sources into structured components through automated classification. The service processing model divides data according to asset data classification rules, organizing heterogeneous data types into standardized categories. This segmentation approach enables the system to systematically process complex data, identify important information, and maintain high classification accuracy despite data diversity and complexity.
3Productivity
If automated service processing model is used, then processing efficiency is improved, but model complexity and computational requirements increase
Solution Approach 1:
The patent implements a universal service processing model that handles multiple data types and classification scenarios through a unified framework. The model is designed to process various service processing instructions and apply asset data classification rules across different data sources and formats. This multi-functional design achieves high automated processing efficiency while managing model complexity through standardized, reusable components that can handle diverse classification tasks.
Data Source
AI summary
Disclosed are a method for data processing and a computer. The method includes the following. A service processing instruction and virtual asset-associated data of an aircraft are input to a target service processing model. Data division is performed on the virtual asset-associated data to obtain S unit virtual assets. Binary group classification information corresponding to each unit virtual assets is determined. Weight model parameters respectively corresponding to the S unit virtual assets are obtained. Data feature vectors are combined with the weight model parameters to obtain S fused feature vectors. A prompt text is generated, a target processing network is determined, and feature processing is performed on the S fused feature vectors and the prompt text via the target processing network, to obtain a feature processing result. The feature processing result is classified and recognized to obtain a data recognition result for responding to the service processing instruction.