A bridge tower construction progress intelligent identification system and method based on adaptive weight

By using an adaptive weighted multi-source data fusion method, the shortcomings of a single data source in bridge tower construction progress monitoring are addressed, achieving high-precision and reliable construction progress identification and supporting intelligent management of bridge construction.

CN121640336BActive Publication Date: 2026-07-21CCCC SECOND HARBOR ENGINEERING CO LTD +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
CCCC SECOND HARBOR ENGINEERING CO LTD
Filing Date
2025-11-26
Publication Date
2026-07-21

AI Technical Summary

Technical Problem

Existing methods for monitoring the construction progress of bridge towers rely on a single data source, resulting in low accuracy, strong human subjectivity, poor environmental adaptability, and a lack of cross-validation and anomaly detection mechanisms, leading to unreliable identification results.

Method used

An adaptive weighted multi-source data fusion method is adopted. By synchronously collecting and preprocessing sensor data and machine vision data, a confidence evaluation model is established, the weights are dynamically adjusted, and combined with environmental condition scores, hierarchical decision-making and anomaly detection are carried out to achieve collaborative discrimination of multimodal data.

Benefits of technology

It improves the accuracy and reliability of construction progress identification, maintains high precision in complex environments, reduces manual monitoring workload, lowers costs, supports the establishment of digital progress archives, and provides intelligent management support for bridge construction.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application discloses a kind of intelligent identification methods for bridge tower construction progress based on adaptive weight, comprising the following steps: S1, sensor data acquisition and preprocessing;S2, machine vision data acquisition and analysis;S3, confidence evaluation model is established;S4, adaptive weight fusion;S5, multi-modal data fusion decision;S6, anomaly detection and error correction.The application fully utilizes the complementary advantages of different data sources through double verification of sensor data and visual data, which can improve the system accuracy compared to single data source method, and effectively prevents the influence of single point failure on the whole system through anomaly detection and cross-validation mechanism.
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