Sensor-Based Wildlife Population Prediction System
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Solution Overview
Problem
Conservation efforts for wildlife populations face challenges in determining optimal harvesting quantities to maintain healthy animal populations and habitats while optimizing conservation income, as current methods fail to accurately balance harvesting with population management and revenue generation.
Innovation Solution
A system and method using sensor-based predictions that determine a predicted population of species across zones, calculate sporting recommendations for optimal harvesting, and issue corresponding licenses, prioritizing sportsmen based on data analysis and machine learning models to ensure conservation and revenue optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional wildlife population estimation methods are used, then the complexity of the system is low, but the measurement precision of population data is insufficient
Solution Approach 1:
The patent replaces traditional mechanical/manual population counting methods with sensor-based automated detection systems. Sensors continuously monitor wildlife populations, replacing human observers and manual counting mechanisms, thereby improving measurement precision while managing system complexity through automation.
Solution Approach 2:
The patent introduces sensor networks as intermediary devices between the wildlife population and the conservation management system. These sensors act as mediators that collect, transmit, and process population data, enabling accurate remote monitoring without direct human intervention in the field.
2Quantity of substance
If harvesting quantities are increased to optimize conservation income, then the revenue is improved, but the population health deteriorates
Solution Approach 1:
The patent implements a feedback loop where sensor-collected population data is continuously fed into the license issuance system. The system monitors population health metrics and automatically adjusts harvesting licenses in real-time, reducing licenses when populations are low and allowing increases when populations are healthy, thereby balancing income with population sustainability.
Solution Approach 2:
The patent transitions from static, fixed harvesting quotas to dynamic license issuance that adapts to changing population conditions. The system continuously adjusts the number and type of harvesting licenses based on real-time sensor data, making the conservation management flexible and responsive to population fluctuations.
3Stability of the object's composition
If harvesting quantities are reduced to maintain population health, then the population stability is improved, but the conservation income deteriorates
Solution Approach 1:
The feedback mechanism ensures that population stability is maintained while optimizing income by adjusting license quantities dynamically. When sensor data indicates healthy, stable populations, the system automatically increases licensing opportunities, thereby maintaining stability while maximizing revenue potential.
4Productivity
If manual population counting methods are used, then the cost is low, but the productivity of data collection is insufficient
Solution Approach 1:
The patent replaces manual, labor-intensive population counting with automated sensor systems that continuously collect data without human intervention. This substitution dramatically improves data collection productivity, gathering extensive population information across large areas and time periods at a fraction of the manual labor cost.
Data Source
AI summary
Methods and systems are described for making sensor based predictions. A predicted population for a species can be determined. The predicted population for the species can be determined based on habitat data and wildlife data received by sensors.