Urban drainage network optimization design method and system based on BIM model

Through multi-source data fusion and spatio-temporal graph neural network based on BIM model, combined with causal inference technology, the design of urban drainage network is optimized, and the problems of dynamic adaptability and insufficient fault diagnosis in traditional design methods are solved, and the drainage system's ability to cope with extreme weather and fault diagnosis is improved.

CN120449282AActive Publication Date: 2025-08-08SHANDONG GREEN SOURCE WATER SAVING TECHNOLOGY RESEARCH INSTITUTE CO LTD
7 Cites 7 Cited by

Patent Information

Application Number
CN202510929613.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-07
Publication Date
2025-08-08
Estimated Expiration
2045-07-07

AI Technical Summary

Technical Problem

Traditional urban drainage network design lacks dynamic adaptability, insufficient system overall analysis, limited fault diagnosis capabilities, and low data utilization efficiency, making it difficult to deal with extreme rainfall events and urban flooding.

Method used

Based on the BIM model, through multi-source data fusion, space-time graph neural network and causal inference technology, an optimization design method for urban drainage network is constructed to realize dynamic carrying capacity prediction, risk assessment and fault diagnosis, and optimize drainage system parameters.

Benefits of technology

It improves the ability of drainage systems to respond in extreme weather, reduces flooding disasters, reduces construction costs, improves the accuracy of fault diagnosis, and realizes the transformation from static design tools to dynamic decision-making platforms.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention relates to the technical field of urban drainage network optimization design, and discloses a BIM model-based urban drainage network optimization design method and system, and the method comprises the steps: collecting multi-source hydrological data, and carrying out the preprocessing of the multi-source hydrological data; modeling the drainage system into a space-time diagram structure, and predicting the bearing capacity of the drainage system; an overflow risk assessment model is constructed, key influence factors are identified, node risk degrees are quantified, and high-risk areas are identified; constructing a fault model based on a causal graph, identifying a causal chain of an overflow event, and realizing crossing from correlation analysis to causality understanding; performing drainage system parameter optimization by using the BIM model; by fusing multi-source data analysis, a time-space diagram neural network and a causal inference technology, dynamic bearing capacity prediction, accurate risk assessment and abnormal source overflow diagnosis of the drainage system are realized.
Need to check novelty before this filing date? Find Prior Art