Cloud-Edge Resistivity Sensing Network for Urban Underground Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current urban underground resistivity sensing systems face challenges in long-term monitoring due to complexity of urban environments, electromagnetic interference, limited electrode placement, and reliance on municipal facilities, leading to inefficiencies in data collection and lack of smart sensing capabilities.
Innovation Solution
An urban underground space resistivity sensing system based on cloud-edge-end collaboration, utilizing a distributed network of resistivity sensing nodes connected to edge servers and a central cloud computing platform for data processing and anomaly detection, enabling flexible, real-time, and intelligent data collection and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional centralized control console is used for resistivity sensing system, then system management is simplified, but network congestion and time delays occur due to all nodes being centrally managed
Solution Approach 1:
The patent divides the centralized control console into multiple distributed edge servers, each managing a specific region or group of sensing nodes. This segmentation allows parallel processing of commands and data across multiple servers, eliminating the single-point bottleneck and reducing network congestion and transmission delays while maintaining simplified management within each distributed unit.
2Device complexity
If centralized control console manages all sensing nodes, then system architecture is simple, but hardware and software resource demands on the central console become excessive
Solution Approach 1:
The patent segments the centralized control functionality into multiple distributed edge servers, each handling a portion of the sensing nodes. This distribution reduces the hardware and software resource demands on any single server while maintaining overall system functionality. Each edge server operates with reduced resource requirements compared to a fully centralized system, and the modular architecture allows for scalable resource allocation.
3Ease of manufacture
If serial wired transmission network is used for data collection, then system implementation is straightforward, but time delay significantly impacts synchronization of power supply and potential measurements
Solution Approach 1:
The patent transitions from static serial wired transmission to dynamic wireless communication for data transmission between sensing nodes and edge servers. This dynamic approach allows for flexible, real-time data exchange without the time delays and synchronization issues of serial wired networks. The wireless communication system adapts transmission parameters dynamically to maintain synchronization between power supply and potential measurements while simplifying system implementation.
4Measurement precision
If contact sensors are used for in-situ measurements, then parameter monitoring is effective, but penetrating imaging capabilities for long-term remote sensing are lacking
Solution Approach 1:
The patent implements a hybrid sensing system that combines contact sensors for in-situ measurements with wireless resistivity sensing nodes for remote penetration imaging. The resistivity sensing nodes can measure electrical properties through the ground without direct contact, providing both precise parameter monitoring and remote imaging capabilities. This multi-functional system allows the same network to perform both contact-based monitoring and non-contact imaging tasks.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system achieves hierarchical data processing and storage, enhances data collection efficiency, and enables intelligent risk prediction and assessment, reducing construction costs and improving scalability and adaptability.
Implementation Method 1
Changes in underground structures and the surrounding geological environment correspondingly alter the physical parameters of underground media, such as density, elastic wave velocity, and resistivity
Data Source
AI summary
An urban underground space resistivity sensing system and data collection method based on cloud-edge-end collaboration is disclosed. Employing an advanced cloud-edge-end architecture design, data collection tasks are decentralized to distributed edge nodes and sensing nodes. Computational tasks intensive in data processing and data mining are deployed on a central cloud computing platform, ensuring real-time and efficient data collection. Simultaneously, a three-dimensional spatial arbitrarily distributed sensing network is jointly constructed with well structures in the ground. Leveraging favorable conditions such as embedded horizontal cables and longitudinally drilled holes on both sides of roads, the system flexibly deploys a three-dimensional resistivity sensing network traversing streets, addressing the limitations of singular surface exploration and achieving detailed imaging of subterranean targets beneath urban streets.