Control Module Predicts Refill Needs for Vehicle Wash Systems
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Solution Overview
Problem
Existing washing systems for vehicles face challenges in predicting the consumption of washing substances, making it difficult to ensure timely replenishment and maintain the quality of the wash process.
Innovation Solution
A method and system that utilize a control module to predict refill requirements by reading fill level data and processing planning and consumption data, outputting a refill data set to ensure sufficient washing substances are available, including the use of digital pumps and level sensors for accurate consumption calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If washing systems rely on manual monitoring of washing substance levels, then operational simplicity is maintained, but prediction accuracy and timely replenishment are compromised
Solution Approach 1:
The system automatically monitors fill levels, calculates consumption rates, and generates refill predictions without manual intervention. The control module autonomously processes data from sensors and planning systems to provide predictive refill information, eliminating the need for manual monitoring while maintaining operational simplicity.
Solution Approach 2:
Manual monitoring and calculation methods are replaced with automated electronic systems including sensors, control modules, and software algorithms. The mechanical/manual process of checking and calculating washing substance levels is substituted with electronic data collection, processing, and prediction capabilities.
2Measurement precision
If the system collects and processes multiple data types (fill level, consumption, planning data), then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The control module serves multiple functions: it collects fill level data from sensors, retrieves consumption data, accesses planning information, performs calculations, and generates refill predictions. This multi-functional approach consolidates diverse data processing tasks into a single integrated system, managing complexity through functional consolidation.
Solution Approach 2:
The control module acts as an intermediary that integrates various data sources (sensors, planning systems, consumption databases) and translates them into actionable refill predictions. It mediates between raw data collection and decision-making, simplifying the interface between multiple data types and the ultimate refill decision.
3Reliability
If real-time monitoring of washing substances is implemented, then wash quality assurance is improved, but system complexity and cost increase
Solution Approach 1:
The system performs preliminary monitoring and prediction to anticipate refill needs before washing substance levels become critically low. By continuously tracking fill levels and calculating consumption rates in advance, the system ensures wash quality is maintained by predicting refills before they are urgently needed, preventing interruptions.
Data Source
AI summary
Predicting a refill requirement for wash substances for performing a vehicle wash using a wash system for the car wash for performing the vehicle washing, wherein the control module is for providing a refill data set for predicting a refill requirement for washing substances. The control module comprises at least one measuring device for detecting current fill level data for each of the washing substances and a data link between the at least one measuring device and the control module for transmitting the detected fill level data to the control module. The control module is designed to perform a prediction function for calculating a refill data set, in which a prediction of the refill requirement is encoded, based on planning data, which represent planned vehicle washes at the washing system taking into account washing-substance-specific consumption data.