Digital Construction Early Warning System for Overdue Node Prediction
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Solution Overview
Problem
Current digital construction technologies lack sufficient technical support for specific details in construction period judgment, treatment, and early warning, particularly in managing construction projects effectively.
Innovation Solution
A digital construction-based intelligent construction period early warning system and method that acquires feature factors of new sections, constructs data and feature association models, predicts overdue nodes, and generates early warning information when predicted quantities exceed set thresholds, utilizing historical data to analyze and adjust predictions for accurate warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional construction period management methods are used, then management simplicity is maintained, but management precision and early warning capability are insufficient
Solution Approach 1:
The patent replaces traditional manual construction period management with an intelligent system that uses historical data, association models, and automated prediction algorithms to determine construction periods and generate early warnings, thereby improving precision while managing complexity through digitalization
Solution Approach 2:
The system automatically analyzes historical data, constructs association models, predicts construction periods, and generates early warnings without requiring manual intervention for each prediction, enabling the system to serve itself and improve consistency in management decisions
2Reliability
If detailed construction period analysis is performed, then early warning accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the construction period analysis into distinct components: acquiring feature factors, constructing data association models, predicting overdue nodes, and generating early warnings. This segmentation allows detailed analysis to be performed through modular processes, improving accuracy while managing system complexity through structured decomposition
Solution Approach 2:
The system performs preliminary analysis by acquiring historical data and constructing association models before actual prediction occurs. This preliminary action enables the system to learn from past patterns and prepare prediction frameworks in advance, improving early warning accuracy without requiring complex real-time analysis during construction
3Measurement precision
If historical data analysis is utilized, then prediction accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts relevant feature factors from historical data and focuses analysis on key parameters such as overdue nodes and deleted sections. By taking out only the essential information needed for prediction rather than processing entire datasets, the system improves prediction accuracy while reducing data processing complexity
Solution Approach 2:
The system transforms historical data into predictive parameters by changing the representation and organization of data. Historical construction period data is converted into feature factors and association models that can be directly used for prediction, improving accuracy while simplifying the processing required during actual prediction operations
Data Source
AI summary
The present disclosure discloses a digital construction-based intelligent construction period early warning system and method, which belongs to the technical field of intelligent early warning. The system includes a digital construction module, a data calling module, an initial judgment module, an association analysis module, and an adjustment early warning module. An output end of the digital fabrication module is connected to an input end of the data calling module; an output end of the data calling module is connected to an input end of the initial judgment module; an output end of the initial judgment module is connected to an input end of the association analysis module; and an output end of the association analysis module is connected to an input end of the adjustment early warning module.