Raw Milk Price Prediction Using Adaptive Clustering and Game Theory
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Solution Overview
Problem
Existing clustering algorithms lack a dynamic adjustment mechanism, failing to adapt to variable market data distribution, and traditional models ignore complex interactions among market participants, leading to inaccurate price predictions in the raw and fresh milk market.
Innovation Solution
A prediction system incorporating an adaptive clustering algorithm and game theory modeling with Nash equilibrium analysis to dynamically adjust market states and optimize strategy combinations, considering the interactions of producers, wholesalers, and consumers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed clustering algorithms are used, then the algorithm structure is simple, but the algorithm cannot adapt to variable market data distribution
Solution Approach 1:
The patent implements dynamic clustering by allowing the clustering algorithm to automatically adjust the number of clusters and recalculate cluster centers based on current market data distribution. This transforms the static fixed-cluster approach into a dynamic adaptive system that evolves with market conditions, resolving the contradiction between adaptability and structural simplicity.
Solution Approach 2:
The system incorporates feedback mechanisms where clustering results are continuously evaluated and used to adjust subsequent clustering parameters. The algorithm monitors data distribution changes and feeds this information back into the clustering process, enabling automatic adaptation without requiring complex manual intervention or predetermined structures.
2Measurement precision
If traditional linear prediction models are used, then the model is simple, but the model cannot accurately capture nonlinear price fluctuations
Solution Approach 1:
The patent employs a composite modeling approach that integrates multiple prediction models (including linear models and nonlinear models such as neural networks or polynomial regression). This composite model leverages the strengths of different model types to accurately capture both linear trends and nonlinear fluctuations in milk prices, achieving high prediction accuracy while managing complexity through model integration.
Solution Approach 2:
The prediction model dynamically adjusts its parameters and structure based on market conditions and data characteristics. Rather than using a fixed linear model, the system adapts its modeling approach to capture evolving nonlinear patterns in price fluctuations, transforming the static model into a dynamic predictive system.
3Reliability
If traditional market analysis models are used, then the analysis process is simple, but the models ignore complex interactive relationships among market participants
Solution Approach 1:
The patent merges multiple modeling approaches including clustering analysis, game theory models, and predictive analytics into an integrated comprehensive analysis system. This integration allows the system to simultaneously consider interactions among producers, wholesalers, retailers, and consumers, capturing complex market dynamics while maintaining a structured analytical framework.
Solution Approach 2:
The system segments the market analysis into distinct components (participant behavior analysis, interaction modeling, clustering-based market state identification, and prediction modules). This segmentation allows each aspect of market complexity to be addressed by specialized analytical techniques while maintaining overall system coherence and manageability.
Data Source
AI summary
Disclosed is a prediction system for a price of raw and fresh milk based on large data, including: collecting and preprocessing original market data to obtain market data; implementing an adaptive clustering algorithm on the market data; constructing a game theory model and introducing Nash equilibrium analysis of mixed strategies to calculate an optimal strategy combination of each market stage; predicting a price of raw and fresh milk using the optimal strategy combination; and modifying the game theory model, predicting the price of raw and fresh milk using the modified game theory model, and visually displaying a prediction result. In the present disclosure, the problems of a fixed algorithm structure mostly adopted in the existing clustering algorithms and lack of a dynamic adjustment mechanism are solved.