Dynamic Scheduling for Green Space Treatment Devices
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Solution Overview
Problem
Existing systems lack efficient methods to determine the optimal timing for the collection, storage, and delivery of green space and cultivated area treatment devices, as plant growth varies significantly over time and location, leading to inefficient use of resources and scheduling challenges.
Innovation Solution
A method and system that automatically provide and ascertain growth characteristic variables to determine the best timing for collection, storage, and delivery based on plant growth, using sensors and models to adjust schedules dynamically, and transmit information to relevant services or operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed scheduling is used for collection, storage, and delivery of treatment devices, then operational simplicity is maintained, but resource efficiency deteriorates due to mismatched plant growth conditions
Solution Approach 1:
The system implements feedback loops where growth characteristic variables are continuously monitored and fed back to adjust collection, storage, and delivery schedules. This allows the system to adapt to actual plant growth conditions, improving resource efficiency while maintaining manageable complexity through automated decision-making based on predefined thresholds and models.
Solution Approach 2:
The system enables self-service by allowing the scheduling mechanism to automatically adjust itself based on monitored growth data without requiring manual intervention. The automated ascertainment of information and dynamic schedule adjustment reduces the need for complex human coordination while optimizing resource utilization according to actual plant needs.
2Loss of time
If manual scheduling is used for treatment device operations, then system simplicity is maintained, but time consumption increases due to lack of real-time adaptation
Solution Approach 1:
The system performs preliminary actions by pre-establishing growth models and thresholds that enable automated decision-making. This preliminary setup allows the system to quickly adapt schedules in real-time without manual intervention, reducing time consumption while implementing the necessary automation for dynamic scheduling based on plant growth conditions.
Solution Approach 2:
The patent replaces manual scheduling mechanisms with automated electronic systems that monitor growth characteristic variables and automatically adjust schedules. This substitution of mechanical/manual processes with electronic automation reduces time consumption while implementing the required level of automation for real-time adaptation to plant growth conditions.
3Measurement precision
If generic scheduling is used for all locations, then operational simplicity is maintained, but precision deteriorates due to ignoring regional plant growth variations
Solution Approach 1:
The system applies local quality by tailoring scheduling decisions to specific regional plant growth conditions. Growth characteristic variables are monitored and interpreted in the context of local environmental factors, allowing precise scheduling that adapts to regional variations in plant development patterns while maintaining manageable system complexity through localized data processing and decision-making.
Solution Approach 2:
The system implements parameter changes by adjusting scheduling parameters based on locally measured growth characteristic variables. This allows the system to optimize collection, storage, and delivery timing for each specific location based on actual plant growth rates and patterns, achieving high scheduling precision while managing complexity through parameter-based adaptation rather than completely separate systems for each location.
4Measurement precision
If frequent monitoring of plant growth is implemented, then scheduling accuracy is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic action by monitoring growth characteristic variables at optimized intervals rather than continuously. This periodic monitoring approach maintains sufficient scheduling accuracy by capturing key growth milestones while reducing energy consumption compared to continuous monitoring. The system adjusts monitoring frequency based on growth rates and seasonal patterns to balance precision requirements with energy efficiency.
Data Source
AI summary
A method and system ascertain information relating to a collection, a start and/or an end of a period spent in a workshop and/or in storage and/or a delivery of a green space and/or cultivated area treatment device. The method involves the steps of: providing a growth characteristic variable, wherein the growth characteristic variable is characteristic for plant growth on and/or in a space/area treated and/or to be treated by way of the green space and/or cultivated area treatment device; and ascertaining the information on the basis of the provided growth characteristic variable.