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Research on Automatic Problem-Solving Technology of Olympic Mathematics in Primary Schools Based on AORBCO Model Cover

Research on Automatic Problem-Solving Technology of Olympic Mathematics in Primary Schools Based on AORBCO Model

By: Sijie Wu,  Liping Lu and  Wuqi Gao  
Open Access
|Jun 2025

Figures & Tables

Figure 1.

Principle of Resolution
Principle of Resolution

Figure 2.

Overall design of the system
Overall design of the system

Figure 3.

Matching Process
Matching Process

Figure 4.

Number of test cases passed by the system
Number of test cases passed by the system

Figure 5.

Statistical chart of batch test errors
Statistical chart of batch test errors

Figure 6.

Problem-solving through quantitative comparison chart
Problem-solving through quantitative comparison chart

Figure 7.

Knowledge weight evolution during problem-solving process
Knowledge weight evolution during problem-solving process

Figure 8.

Temporal evolution of active nodes (bars) and weight concentration (line) during problem solving
Temporal evolution of active nodes (bars) and weight concentration (line) during problem solving

THE COMPARISON OF THE PROBLEM-SOLVING SUCCESS RATES BETWEEN THIS SYSTEM AND OTHER PLATFORMS

Problem-solving systemSuccess rate of problemsolvingAverage problemsolving time
This system78.5%1min30s
Little ape search questions65%2min10s
Homework Help60%2min30s

Rule structuring

member namedata structuredescribe
labelStringunique identification of the rule
ruleTripleList<GraphTriple>regular triplet
conclusionTriplesList<GraphTriple>rule conclusion triplet
instantiatedCategoryStringRule classification
instantiatedDescriptionStringSimple description of rules
commonTextStringRegular mathematical text

COMPARISON OF PROBLEM-SOLVING EFFECTIVENESS ACROSS DIFFERENT QUESTION TYPES

Type of question categorySuccess rate of problem-solvingAverage problemsolving time
Basic Operations and Relations85%1min10s
Geometry and tree planting75%1min30s
Application problems72%1min40s
Special question types and techniques68%1min50s
Other categories80%1min20s

EXPERIMENTAL ENVIRONMENT

ComponentDetails
HardwareIntel(R) Core(TM) i7-3770 CPU
16GB RAM
1.5T hard disk
SoftwareWindows 10
Java development platform IDEA
Graph database Neo4j
Symbolic computation platform Maple

PHASE TRANSITION PARAMETERS

PhaseActive NodesWeight ConcentrationTrigger Condition
Initial Activation12→9N/AKnowledge filtering
Rule Matching9→1454%→61%Constraint identification
Cognitive Optimization14→561%→89%Path pruning activation

RULE BASE EVOLUTION METRICS

Time Interval (h)New Rules GeneratedError Rate (%)Avg. Confidence
0-1214818.20.76
12-249212.10.83
24-481659.70.88
48-721016.30.91
Language: English
Page range: 20 - 29
Published on: Jun 16, 2025
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Sijie Wu, Liping Lu, Wuqi Gao, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.