ATM Surrounding Lighting Self-Diagnosis for Threshold Alerts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for ensuring adequate lighting at ATMs rely on manual assessments, which are time-consuming and inefficient, leading to delays in identifying and addressing lighting deficiencies, impacting customer experience and security.
Innovation Solution
Implementing a self-diagnosis system at ATMs that uses sensors to continuously monitor lighting levels, compare them to predefined thresholds, and generate alerts for potential issues, utilizing machine learning to enhance accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual lighting assessments are used at ATMs, then implementation simplicity is maintained, but monitoring efficiency and response time deteriorate
Solution Approach 1:
The ATM performs self-diagnosis of lighting conditions using its own integrated sensor and processing capabilities. The system automatically captures light intensity data, compares it to thresholds, and generates alerts without requiring external manual assessment, thereby improving monitoring efficiency while keeping the system relatively simple.
Solution Approach 2:
The manual mechanical assessment process is replaced with an automated electronic sensing and processing system. The light sensor and processor automatically perform measurements and evaluations that previously required human technicians, significantly improving productivity with minimal increase in overall system complexity.
2Loss of time
If periodic site visits are conducted to measure lighting levels, then measurement accuracy is maintained, but response time and operational efficiency deteriorate
Solution Approach 1:
The lighting monitoring is performed continuously or at frequent intervals rather than periodically. The sensor continuously captures light intensity data and the processor continuously evaluates compliance, ensuring immediate detection of lighting deficiencies and maintaining reliable compliance without time delays associated with periodic manual visits.
3Reliability
If manual lighting assessments are performed, then system simplicity is maintained, but customer experience and security are compromised
Solution Approach 1:
The ATM autonomously monitors its own lighting conditions and generates alerts when deficiencies are detected. This self-service capability ensures customer safety and security through reliable, continuous monitoring while maintaining relative system simplicity by using the ATM's existing processing and communication infrastructure.
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
Ensures compliance with regulatory requirements, improves operational efficiency, and enhances customer safety by proactively identifying and addressing lighting problems before they occur.
Implementation Method 1
capturing light intensity data with a sensor of the ATM
Data Source
AI summary
Systems and techniques are disclosed for automated self-diagnosis of surrounding lighting for Automated Teller Machines (ATMs). An example technique may include capturing light intensity data, with a sensor of the ATM, at a particular distance from the ATM, and comparing the light intensity data to a specified light intensity threshold of the ATM. The example technique may include determining, based on the comparison, that the light intensity data deviates from the specified light intensity threshold of the ATM, and in response to determining that the light intensity data deviates from the specified light intensity threshold of the ATM, generating an alert indicating the deviation.