Distributed Measuring Station Micro-Server Scalability
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Solution Overview
Problem
Current systems for managing data from a large number of measuring devices are limited in scalability, as they can only control a restricted number of devices simultaneously, and require significant changes to system structure to accommodate increased device numbers, especially when frequent data transmission is needed from multiple devices.
Innovation Solution
A distributed system utilizing an inverted client-server model, where a client computer unit queries multiple server computer units, each equipped with micro-servers, to process and manage data from numerous measuring devices, allowing for increased scalability without altering the system structure, using Internet protocols and micro-servers for data acquisition, processing, and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a central station with a management module is used to control measuring devices, then data from measuring devices can be collected and processed, but the number of measuring devices that can be controlled simultaneously is limited
Solution Approach 1:
The system is divided into multiple autonomous measuring stations, each capable of independently controlling and processing data from measuring devices. This segmentation allows the system to scale horizontally by adding more measuring stations rather than increasing the capacity of a single central station, thereby resolving the limitation on the number of controllable devices without proportionally increasing central system complexity.
Solution Approach 2:
Instead of having a central station actively pollate and control all measuring devices, the system inverts the control model: measuring stations autonomously manage their local devices and only communicate with central infrastructure when needed. This inversion reduces the continuous communication overhead and control complexity at the central station, enabling support for larger numbers of devices.
2Productivity
If frequent data transmission from multiple measuring devices is implemented, then real-time monitoring capability is improved, but the system requires significant changes to accommodate increased device numbers
Solution Approach 1:
By distributing data collection and preliminary processing across multiple autonomous measuring stations, each station can handle frequent data transmissions from its local devices independently. This segmentation prevents any single point from becoming a bottleneck, allowing the system to scale to support more devices with frequent transmission without requiring centralized architectural changes.
Solution Approach 2:
The system transitions from a single-dimensional centralized architecture to a multi-dimensional distributed architecture where measuring stations operate autonomously at the edge and only aggregate or synchronize with the central infrastructure when necessary. This dimensional shift enables frequent local data transmission without proportionally increasing central system load or complexity.
3Quantity of substance
If more measuring devices are connected to the communication network, then system coverage is expanded, but the complexity of managing and processing data increases
Solution Approach 1:
Each measuring station operates autonomously, self-managing data collection, processing, and preliminary analysis from its connected measuring devices. This self-service capability at the edge reduces the burden on central systems, allowing more devices to be connected without proportionally increasing central data management complexity. Each station handles its own data lifecycle independently.
Solution Approach 2:
Data management responsibilities are segmented and distributed across multiple autonomous measuring stations rather than centralized. Each station manages its local data independently, filtering and processing information before potential central aggregation. This segmentation maintains ease of operation as device numbers grow by preventing central system overload.
Data Source
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Figure 3a~3b
AI summary
The invention relates to a method and distributed system for detecting and managing data from a plurality of measuring devices. In a method for detecting and managing data from a plurality of measuring devices (M) using a respectively distributed system, data from at least one measuring device (M) is detected in a measuring station (MS). The data is processed into client-capable data in a server computer unit (S) of the respective measuring station (MS) and made available. The available data from the measuring stations (MS) is retrieved via a communication network (N) in at least one client station (CS) utilizing at least one client computer unit (C) and further processed.