Systems and methods for tracking and scoring cleaning
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Solution Overview
Problem
Conventional cleaning robots and machines lack the ability to quantify and interpret their cleaning performance effectively, making it difficult for users to determine if an area has been successfully cleaned, requiring time and resources for manual inspection or data calculation.
Innovation Solution
A network system that monitors and scores the cleaning performance of robots or machines by obtaining operational data, computing a clean score based on various factors such as area cleaned, time spent, and cleaning goals, and providing reports through a graphical user interface or mobile application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cleaning robots collect and store operational data, then cleaning performance can be tracked and analyzed, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent introduces a server as an intermediary component that receives, processes, and analyzes cleaning data from multiple robots. This centralizes the computational complexity away from individual robots, allowing them to remain relatively simple while still enabling sophisticated performance tracking and analysis through the server's data processing capabilities.
Solution Approach 2:
The system implements feedback mechanisms where cleaning performance data is collected, analyzed, and used to generate reports and insights. This feedback loop enables continuous improvement of cleaning operations by providing actionable information about performance metrics, area cleanliness levels, and resource allocation efficiency.
2Reliability
If manual inspection methods are used to verify cleaning quality, then cleaning effectiveness can be confirmed, but time and human resources are consumed
Solution Approach 1:
The patent replaces manual mechanical inspection with automated sensor-based detection systems. Sensors on cleaning robots and fixed infrastructure continuously monitor cleaning quality metrics, eliminating the need for human inspectors to physically verify cleaning effectiveness while providing more consistent and comprehensive monitoring coverage.
Solution Approach 2:
The system enables self-verification of cleaning quality through automated sensors and algorithms that independently assess whether cleaning goals have been met. The robots and monitoring system perform self-evaluation of cleaning effectiveness without requiring external human intervention, saving time and resources.
3Loss of information
If detailed operational data is collected from cleaning machines, then performance analysis can be improved, but data interpretation difficulty increases
Solution Approach 1:
The server acts as an intermediary that aggregates raw operational data from multiple sources and transforms it into meaningful performance metrics and insights. This data mediation process handles the complexity of interpreting diverse sensor readings, operational parameters, and cleaning outcomes, presenting simplified information to users through automated reports and dashboards.
Solution Approach 2:
The system transforms raw operational parameters into standardized performance metrics that are easier to interpret and compare. By changing the representation of data from complex sensor readings to meaningful performance indicators, the system maintains complete information while reducing interpretation difficulty for users.
Data Source
AI summary
The present disclosure provides systems and methods for tracking and scoring robot or machine performance. The robot or machine performance may comprise a metric that can be computed based on operational data for the robot or machine.