Cloud-End Load Identification via Segmented IoT Meter and Master Station
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Solution Overview
Problem
Existing load identification devices have limited capabilities in identifying various user electrical devices due to outdated local load characteristic libraries, leading to difficulties in real-time identification and synchronization, especially when new devices are introduced, which restricts their usage scenarios.
Innovation Solution
A cloud-end collaborative load identification system comprising an intelligent Internet-of-Things watt-hour meter module, a user power information pre-loading/acquisition module, and a master station load identification module, which extracts characteristic vector data, matches it with a load characteristic library, and determines target load characteristic data for online identification, enhancing the device's identification capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a local load characteristic library is used for device identification, then the identification process is fast and autonomous, but the types of electrical devices that can be identified are limited and the library cannot be updated in time
Solution Approach 1:
The system divides the load identification function into two segments: local rapid matching using a lightweight load characteristic library in the watt-hour meter module, and cloud-based comprehensive identification using a large-scale second load characteristic library. This segmentation allows the local system to maintain fast autonomous identification for common devices while the cloud system provides comprehensive updates and identification for new device types, resolving the contradiction between speed and adaptability.
Solution Approach 2:
The master station load identification module acts as an intermediary between the local watt-hour meter module and the cloud-based second load characteristic library. It receives target load characteristic data from the local module, performs data orientation processing, matches with the comprehensive cloud library, and feeds back optimal matching solutions to update the local library. This intermediary mechanism enables timely updates of device identification capabilities without compromising local rapid identification performance.
2Adaptability or versatility
If the local load characteristic library is updated frequently to include new devices, then the identification capability improves, but the device complexity and storage requirements increase
Solution Approach 1:
The load characteristic library is segmented into two versions: a first load characteristic library stored locally in the watt-hour meter module with limited size for fast autonomous operation, and a second load characteristic library stored in the master station with comprehensive device types for thorough identification. This segmentation allows the local device to maintain low complexity while the cloud-based comprehensive library provides extensive device coverage.
Solution Approach 2:
The system transitions from a single-dimension local library to a two-dimensional architecture combining local and cloud-based libraries. The local first load characteristic library handles rapid identification of common devices, while the cloud-based second load characteristic library provides comprehensive identification for all device types. This dimensional expansion allows the system to achieve high adaptability without increasing local device complexity.
3Measurement precision
If cloud computing resources are used for load identification, then the identification accuracy and device coverage improve, but the system complexity and data transmission requirements increase
Solution Approach 1:
The cloud-end collaborative system is segmented into distinct functional modules: the intelligent Internet-of-Things watt-hour meter module for data collection and preliminary matching, the user power information pre-loading/acquisition module for data transmission, and the master station load identification module for cloud-based comprehensive identification. This modular segmentation manages system complexity by clearly defining responsibilities at each level while achieving high identification accuracy through cloud computing resources.
4Extent of automation
If all load characteristic data is processed locally, then the system operates autonomously, but the identification accuracy for new devices decreases
Solution Approach 1:
The system performs preliminary autonomous identification locally using the first load characteristic library before cloud-based verification. When a new device type is detected, the local module autonomously identifies it as a target load characteristic data candidate, then the master station performs preliminary matching with the second load characteristic library to confirm the device type. This preliminary action at both local and cloud levels maintains autonomous operation while improving new device identification accuracy.
Solution Approach 2:
The system implements a feedback mechanism where the master station load identification module sends optimal matching solutions back to the local watt-hour meter module to update the first load characteristic library. This feedback loop enables the local autonomous system to continuously learn and improve its identification capability for new devices without compromising its autonomous operation, as the cloud provides periodic updates rather than continuous control.
Data Source
AI summary
A system for load identification in collaboration with a cloud end includes: a smart Internet of Things electricity meter module, used for matching extracted feature quantity data with a first load feature library of the smart Internet of Things electricity meter module, and determining load feature data corresponding to unmatched feature quantity data in the feature quantity data as target load feature data; a use information front-end/acquisition module, used for calling the target load feature data and transmitting the target load feature data to a main station load identification module; and a main station load identification module, used for receiving the target load feature data, performing data direction processing on the target load feature data, determining an optimal matching strategy matching the feature data to be identified, and identifying an optimal matching solution corresponding to the feature quantity data to be identified in a second load feature library.