Forecasting Animate Object Activities Using ARLTMI Index
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Solution Overview
Problem
Current methods lack the ability to quickly forecast phenomena and processes that depend on weather conditions but involve animate natural components, such as animal activity or plant protection, and do not provide actionable insights for technical devices to take specific actions.
Innovation Solution
A method that collects and processes data on atmospheric and environmental parameters using satellite images and sensors, converting it into a format suitable for forecasting animate object activities, like bees, and generates the Aggregated Raster Local Temporary Meteorological Index (ARLTMI) to provide actionable recommendations for agricultural activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional weather forecast models are used, then global weather parameters can be estimated with high precision, but they cannot forecast phenomena involving animate natural components (animals, plants) or provide actionable insights for technical devices
Solution Approach 1:
The patent segments the forecasting system into multiple specialized modules: data collection from satellites and ground stations, data processing and validation, phenomenon-specific modeling (weather, animal activity, plant growth), and effector control. This segmentation allows each module to be optimized for its specific function while maintaining overall system reliability through modular validation and testing.
Solution Approach 2:
The patent introduces an intermediary layer of phenological models and activity indicators that translate raw weather data into meaningful predictions about animate object behavior. These intermediaries (e.g., plant development stages, animal activity indices) serve as bridges between atmospheric conditions and biological responses, enabling accurate forecasting of animate phenomena while maintaining scientific rigor.
2Measurement precision
If detailed environmental monitoring and processing is performed, then accurate forecasting of animate object activities is achieved, but system complexity and computational requirements increase
Solution Approach 1:
The patent implements preliminary action by pre-defining phenological models, activity thresholds, and response criteria for various animate objects. These models are developed and validated in advance, storing complex relationships between environmental parameters and biological responses in accessible formats. This preliminary preparation reduces real-time computational complexity while maintaining high measurement precision.
Solution Approach 2:
The patent transforms complex environmental datasets into simplified phenological parameters and activity indices that capture essential biological responses. By changing parameters from raw meteorological data to biologically meaningful metrics (e.g., growing degree days, plant development stages), the system maintains measurement precision while reducing data complexity for processing and decision-making.
3Productivity
If real-time data processing and forecasting is implemented, then quick forecasting of animate object activities is achieved, but computational resources and processing time are consumed
Solution Approach 1:
The patent applies partial action by processing and forecasting only the specific phenological parameters and animate object activities relevant to current agricultural needs, rather than computing all possible weather and biological variables. This selective processing achieves quick forecasting of critical parameters while reducing overall computational energy consumption.
Solution Approach 2:
The patent uses pre-computed phenological models and historical activity patterns as templates that can be rapidly applied to current conditions. Instead of performing full complex simulations in real-time, the system copies and adapts validated model structures and parameter relationships, enabling fast forecasting with minimal computational energy expenditure.
4Loss of information
If the system provides comprehensive forecasting information, then actionable insights for agricultural activities are available, but information processing and user decision-making complexity increases
Solution Approach 1:
The patent applies local quality by providing customized forecasting information tailored to specific user needs, locations, and crop types. Rather than delivering uniform comprehensive data to all users, the system adapts the information content, detail level, and presentation format to local requirements, maintaining information completeness for each user while simplifying decision-making through relevant, location-specific recommendations.
Solution Approach 2:
The system performs preliminary action by pre-processing comprehensive forecasting data into actionable recommendations and simplified decision-support outputs. Phenological models and activity forecasts are pre-analyzed to generate ready-to-use agricultural advisories, reducing the information processing burden on users while maintaining complete and accurate underlying data for informed decision-making.
Data Source
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AI summary
The object of the invention is a method for forecasting the parameters of a phenomenon or the activities of an object, particularly animate, in the given geographic area, as well as an arrangement for realising this method.