Construction Task Inference Using Layered Machine Learning Models

The patent applies preliminary action by pre-training machine learning models (index inference model and task inference model) with historical construction data before actual use. The models are trained in advance on datasets containing construction images, task information, and environmental conditions, enabling accurate real-time inference without complex runtime processing. This pre-computation approach resolves the contradiction by preparing the system beforehand to achieve high precision while maintaining operational simplicity.

The patent introduces machine learning models as intermediary components between image input and task inference output. The index inference model serves as an intermediary that extracts construction indices from images, which then feed into the task inference model. This layered intermediary structure enables precise task management by transforming raw image data into meaningful task information through trained models, resolving the precision-complexity contradiction.

The patent segments the task inference process into two distinct machine learning models: an index inference model that extracts construction indices from images, and a task inference model that determines task states based on those indices. This segmentation allows each model to specialize in specific inference tasks, improving overall accuracy while enabling parallel processing and optimized computation times for each segment.

The system performs preliminary inference by the index inference model to extract construction indices before the task inference model processes task states. This staged preliminary action reduces the computational burden on the final task inference step, as the index model pre-processes and structures the data, enabling faster and more accurate task state determination without excessive processing time.

The patent implements self-service by enabling the system to automatically train and optimize its own machine learning models using historical construction data. The models continuously learn from past construction projects, improving their inference reliability over time without requiring manual reconfiguration or expert intervention. This self-improving capability maintains ease of implementation while significantly enhancing task inference reliability through data-driven learning.

The patent replaces traditional mechanical image analysis methods with machine learning-based inference systems. Instead of using rule-based or threshold-based image processing, the system employs trained neural networks that automatically learn complex patterns from construction images. This substitution dramatically improves task inference reliability by capturing nuanced relationships in the data that traditional mechanical methods cannot detect, while the models can be deployed as standard software components maintaining implementation ease.