Weather Sensor Anomaly Screening Using External Weather Data
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Solution Overview
Problem
Conventional remote monitoring systems for weather sensors cannot determine whether anomalies, such as those caused by snow or heavy rain, can be resolved over time, necessitating unnecessary maintenance dispatches.
Innovation Solution
A weather sensor anomaly determination device that acquires data from both the sensor and external sources to determine if anomalies can be resolved over time, using meteorological data to differentiate between temporary and permanent issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If remote monitoring system checks images to detect anomalies, then anomaly detection capability is improved, but maintenance dispatch efficiency deteriorates due to unnecessary dispatches for temporary anomalies
Solution Approach 1:
The system performs preliminary analysis by comparing sensor data with weather data before dispatching maintenance personnel. The anomaly determination unit proactively identifies whether detected anomalies are temporary (caused by weather conditions) or permanent (requiring maintenance), preventing unnecessary maintenance dispatches before they occur.
Solution Approach 2:
Weather data from external sources serves as an intermediary element that mediates between anomaly detection and maintenance dispatch decisions. By introducing this intermediate verification step using meteorological information, the system distinguishes between weather-caused temporary anomalies and equipment failures requiring maintenance.
2Reliability
If maintenance personnel are dispatched for all detected anomalies, then reliability of maintenance response is improved, but loss of time and resources worsens due to unnecessary dispatches
Solution Approach 1:
The system establishes a feedback mechanism where anomaly detection results are cross-verified with weather data before triggering maintenance dispatch. This feedback loop ensures that only anomalies requiring actual maintenance intervention result in dispatches, filtering out temporary weather-related false alarms.
Solution Approach 2:
The system enables self-service by automatically determining whether anomalies require maintenance intervention or will resolve themselves. The anomaly determination unit acts autonomously to classify anomalies, eliminating the need for human judgment in distinguishing temporary versus permanent anomalies and preventing unnecessary maintenance responses.
3Ease of operation
If image-based monitoring is used for remote observation, then ease of operation is improved, but measurement precision deteriorates because image checking cannot determine if anomalies are temporary
Solution Approach 1:
The system merges multiple data sources including sensor data, image data, and weather data into a unified anomaly determination process. By combining these diverse information streams, the system maintains the ease of remote image-based monitoring while enhancing measurement precision through multi-parameter correlation analysis to distinguish temporary from permanent anomalies.
Data Source
AI summary
A cloud server 60 includes a data acquisition unit 61 and an anomaly determination unit 65. The data acquisition unit 61 acquires from the weather sensor module 1 weather data sensed by the weather sensor module 1 and anomaly determination data that determines whether or not there is an anomaly in the weather sensor module 1. The data acquisition unit 61 acquires weather data at a location where the weather sensor module 1 is installed from the external source. The anomaly determination unit 65 determines whether or not an anomaly can be eliminated to restore a normal state over time on the basis of whether any anomaly has been acquired by the data acquisition unit 61 for each of the multiple weather sensor modules 1 in a specific area in which the weather sensor modules 1 have been installed, and the weather data acquired by the data acquisition unit 61.