Blockchain Resource Valuation Using Class-Labeled Regression Models
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Solution Overview
Problem
Existing blockchain-based systems face challenges in accurately quantifying and evaluating digital resources.
Innovation Solution
A blockchain digital resource processing method and apparatus that determines a class label, constructs a resource quantitative evaluation model through regression fitting, and inputs evaluation characteristic factors to obtain a quantitative evaluation result for digital resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blockchain technology is used for digital resource management, then reliability and tamper-resistance are improved, but the ability to accurately quantify and evaluate digital resources deteriorates
Solution Approach 1:
The patent introduces an intermediary evaluation system that bridges blockchain's reliable data storage and quantitative assessment needs. The system uses class labels as intermediaries to categorize digital resources, regression fitting models as mathematical intermediaries to establish quantitative relationships, and characteristic factors as data intermediaries to capture resource attributes. This intermediary layer enables accurate evaluation while preserving blockchain's tamper-resistance properties.
Solution Approach 2:
The patent transforms qualitative digital resource attributes into quantitative parameters through systematic parameter changes. By identifying key characteristic factors (such as resource rarity, transaction history, holder behavior) and converting them into measurable parameters, the system enables mathematical evaluation. The regression fitting process further transforms these parameters into quantifiable evaluation scores, resolving the contradiction between maintaining data integrity and achieving measurement precision.
2Measurement precision
If regression fitting is used to construct evaluation models, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex evaluation process into distinct modular components: class label determination module, model obtaining module, characteristic factor obtaining module, and quantitative evaluation module. Each module handles a specific aspect of the evaluation process, making the overall system more manageable. The segmentation allows for independent optimization of each component while maintaining the precision benefits of regression fitting.
Solution Approach 2:
The patent performs preliminary actions by pre-construction and pre-training of regression fitting models using historical digital resource data. The models are prepared in advance with established class labels and characteristic factors, so when actual evaluation is needed, the system can directly apply these pre-built models without performing complex construction in real-time. This preliminary preparation reduces operational complexity while maintaining evaluation precision.
Data Source
AI summary
A blockchain digital resource processing method, executed by a computer device, includes: determining a class label of a blockchain-based target digital resource; obtaining a resource quantitative evaluation model that matches the class label; the resource quantitative evaluation model being constructed through regression fitting based on a historical digital resource having the same class label; obtaining an evaluation characteristic factor of the target digital resource according to an input characteristic condition of the resource quantitative evaluation model; and inputting the evaluation characteristic factor into the resource quantitative evaluation model for quantitative evaluation, to obtain a quantitative evaluation result for representing an exchange attribute of the target digital resource.