Transaction Data Processing Rule Matching for Billing Efficiency
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Solution Overview
Problem
Existing transaction data processing systems face high maintenance costs and long calculation times due to a large number of parameters in pricing configurations, leading to inefficiencies in transaction billing processes.
Innovation Solution
A method and apparatus that determine transaction key values and feature values to identify a rule union, matching these values with corresponding rules to obtain a rule matching result, thereby reducing the number of pricing parameters and improving billing efficiency by sequentially processing transaction data through rule enforcers configured according to priorities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pricing elements are directly derived from transaction messages to ensure complete pricing coverage, then pricing accuracy is improved, but the number of parameters increases leading to high maintenance cost and long calculation time
Solution Approach 1:
The patent extracts only the necessary pricing elements from transaction messages by introducing a pricing element selection mechanism. This selective extraction approach ensures that only relevant pricing elements are processed, reducing the overall number of parameters while maintaining pricing accuracy for the selected elements.
Solution Approach 2:
The patent creates a universal pricing parameter table structure that can accommodate multiple pricing elements and rules through a unified framework. This multi-functional design allows the system to handle various pricing scenarios using a single standardized parameter structure, reducing the need for separate parameter sets for different pricing types.
2Adaptability or versatility
If multiple pricing parameters are configured to cover all pricing scenarios, then pricing completeness is improved, but the parameter configuration workload increases
Solution Approach 1:
The patent segments the pricing configuration into modular components including pricing elements, pricing rules, and parameter tables. This segmentation allows administrators to configure and modify pricing scenarios independently without affecting the entire system, significantly reducing the complexity and workload of parameter configuration while maintaining comprehensive pricing coverage.
Solution Approach 2:
The patent introduces a dynamic pricing rule engine that can adapt to different pricing scenarios through configurable rules rather than static parameter sets. This dynamic approach allows the system to handle diverse pricing requirements through flexible rule definitions, reducing the need for extensive static parameter configuration.
3Measurement precision
If transaction information is compared with sorted pricing parameters one by one to determine fee, then pricing accuracy is improved, but calculation time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and sorting pricing parameters into organized structures before actual fee calculation. The pricing parameters are pre-arranged in a sorted format that enables efficient matching with transaction information, significantly reducing the time required during the actual calculation process while maintaining accuracy.
Solution Approach 2:
The patent implements an optimized matching process that skips unnecessary comparisons by using sorted pricing parameters and efficient search algorithms. This allows the system to rapidly identify matching pricing elements without performing exhaustive one-by-one comparisons, thereby reducing calculation time while preserving fee calculation accuracy.
Data Source
AI summary
A transaction data processing method includes: determining one or more transaction key values and one or more transaction feature values of a to-be-processed transaction according to transaction information of the to-be-processed transaction; determining a rule union corresponding to the to-be-processed transaction according to the one or more transaction key values, the rule union including a plurality of to-be-matched rules; matching the one or more transaction feature values of the to-be-processed transaction sequentially with the plurality of to-be-matched rules in the rule union to obtain a rule matching result of the to-be-processed transaction; and performing a billing process of the to-be-processed transaction according to the rule matching result.