Intelligent Interaction Efficiency Analysis via Fuzzy Evaluation
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Solution Overview
Problem
Existing efficiency analysis methods for intelligent interaction systems face significant uncertainty due to numerous uncertain factors, leading to ineffective precision in efficiency analysis.
Innovation Solution
The proposed method involves obtaining user information and behavior data, preprocessing and classifying the data using random forest and naive Bayes algorithms, formulating efficiency scoring criteria, determining weights using an objective method, and performing fuzzy comprehensive evaluation to obtain comprehensive scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional efficiency analysis methods are used for intelligent interaction systems, then the analysis process is simple, but the accuracy and precision of efficiency analysis deteriorates due to significant uncertainty from numerous uncertain factors
Solution Approach 1:
The efficiency analysis method is segmented into multiple distinct modules: data acquisition module, data preprocessing module (including cleaning, annotation, segmentation), classification module (using random forest and naive Bayes algorithms), scoring module (using AHP and entropy methods), and evaluation module. This segmentation allows each module to handle specific tasks independently, improving overall accuracy while managing complexity through modular design.
Solution Approach 2:
The patent employs a composite analytical approach combining multiple algorithms and methods: random forest algorithm combined with naive Bayes algorithm for classification, AHP (Analytic Hierarchy Process) combined with entropy method for weight determination, and fuzzy comprehensive evaluation for final assessment. This composite methodology leverages the strengths of each component to handle uncertainty and improve measurement precision.
2Measurement precision
If comprehensive data processing and multiple algorithms are applied to improve efficiency analysis accuracy, then the measurement precision improves, but the labor and time costs increase
Solution Approach 1:
The patent implements preliminary data preprocessing steps including cleaning unnecessary punctuation and special characters, annotating and segmenting cleaned data, and removing stop words before classification. This preliminary action prepares the data in advance, making subsequent classification and analysis more efficient and accurate, reducing the time required for the main analysis process.
Solution Approach 2:
The patent replaces manual data processing and analysis with automated computational methods: random forest algorithm and naive Bayes algorithm for automated classification, AHP and entropy methods for automated weight calculation, and fuzzy comprehensive evaluation for automated assessment. This substitution of mechanical/manual processes with computational algorithms significantly reduces labor and time costs while maintaining or improving accuracy.
3Reliability
If multiple classification algorithms and evaluation methods are used to reduce uncertainty, then the reliability of efficiency analysis improves, but the device complexity increases
Solution Approach 1:
The patent merges multiple classification approaches by sequentially applying random forest algorithm followed by naive Bayes algorithm, combining their results for more reliable classification. Similarly, it merges subjective AHP method with objective entropy method for weight determination, and combines multiple efficiency indicators through fuzzy comprehensive evaluation. This merging of multiple methods enhances reliability by cross-validation and complementary strengths.
Solution Approach 2:
The patent introduces fuzzy comprehensive evaluation as an intermediary layer between the classification results and final efficiency assessment. This intermediary handles the uncertainty and vagueness in efficiency evaluation by using fuzzy logic, allowing for more nuanced and reliable results while managing the complexity of combining multiple indicators and methods.
Data Source
AI summary
Disclosed is an efficiency analysis method for an intelligent interaction system, including: obtaining user information and behavior data generated during execution of user commands, and associating the user information with the behavior data to obtain associated data; preprocessing the associated data, selecting efficiency indicators for the intelligent interaction system based on standard indicators, and classifying the preprocessed associated data according to the efficiency indicators to obtain classified data; formulating efficiency scoring criteria based on expert opinions, and obtaining efficiency scores of the classified data by using the efficiency scoring criteria; and determining weights of the efficiency scores by using an objective weight assignment method, and evaluating the efficiency scores through fuzzy comprehensive evaluation based on the weights to obtain comprehensive scores. The method not only improves the precision of efficiency analysis but also has good interpretability, making it directly applicable to efficiency analysis methods based on intelligent interaction systems.