Intelligent monitoring and early warning system and method for project progress and cost

By combining edge computing and streaming data fusion technologies with spatiotemporal graph neural networks and Bayesian online learning, the problems of data silos and rigid prediction models in the construction project management system have been solved, enabling real-time, accurate monitoring and adaptive early warning of project progress and cost.

CN121810048APending Publication Date: 2026-04-07HANGZHOU RONGQING ENG SUPERVISION & CONSULTING CO LTD
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Patent Information

Application Number
CN202512017686.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-12-30
Publication Date
2026-04-07

AI Technical Summary

Technical Problem

Existing construction project management systems suffer from data silos, static and rigid prediction models, and fixed early warning thresholds, leading to difficulties in data fusion and insufficient early warning strategies, thus failing to achieve real-time and accurate monitoring of project progress and costs.

Method used

The system employs an edge computing data acquisition module, a streaming data fusion module, a spatiotemporal graph neural network prediction module, and an adaptive early warning module. It collects data through various IoT sensing devices, uses the Apache Flink stream processing engine and OWL ontology for time alignment and semantic unification, combines spatiotemporal graph neural networks for joint prediction, and optimizes the early warning threshold through Bayesian online learning.

Benefits of technology

It enables real-time, accurate, and adaptive monitoring of project progress and costs, improves the efficiency of data fusion and the accuracy of early warning, reduces the probability of false alarms, and provides targeted decision support.

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Abstract

The invention discloses an intelligent monitoring and early warning system and method for project progress and cost, and relates to the technical field of constructional engineering management. The streaming data fusion module adopts an Apache Flink engine and applies a dynamic time warping algorithm to carry out time alignment on multi-source asynchronous data to realize semantic unification; the space-time diagram neural network prediction module constructs a space-time association diagram, integrates global features such as weather and supply chain fluctuation, and adaptively learns an influence weight by using a graph attention mechanism; the self-adaptive early warning module adopts a Bayesian online learning framework, an early warning threshold value is dynamically adjusted according to a historical false alarm rate and a missing report rate, the method comprises the steps of data acquisition, fusion, mapping, joint prediction and self-adaptive early warning, the problem of data islands is solved, complex space-time association is accurately captured through a space-time diagram neural network, and real-time early warning is achieved. Joint prediction of progress and cost risk is realized, false report and missing report are reduced, and real-time performance, accuracy and decision-making efficiency of engineering management and control are improved.
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