Geographic Resource Allocation via Pixel Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing and allocating inherently limited resources such as electricity, gas, water, and data network bandwidth in urban areas in a dynamic and predictive manner to meet the varying needs of different geographic sub-areas within a city.
Innovation Solution
A method that subdivides a geographic area into pixels, gathers data on resource consumption and human or device presence indicators, identifies clusters with similar trends, and allocates resources based on these trends using clustering algorithms and statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If resources are allocated uniformly across the entire geographic area, then resource distribution is simple to manage, but resource efficiency decreases because needs vary by sub-area
Solution Approach 1:
The geographic area is divided into multiple pixels or sub-areas, allowing resource allocation to be customized for each segment based on its specific needs and characteristics, thereby improving resource efficiency without requiring complex centralized management of the entire area
Solution Approach 2:
Different resource allocation strategies are applied to different sub-areas based on their unique characteristics, such as population density, resource consumption patterns, and presence indicators, enabling localized optimization while maintaining overall system manageability
2Measurement precision
If clustering algorithms are applied to partition pixels into clusters, then resource allocation precision improves by targeting high-need areas, but computational complexity increases
Solution Approach 1:
The geographic area is first subdivided into pixels before applying clustering algorithms, creating a standardized framework that simplifies subsequent clustering operations and reduces computational complexity while maintaining allocation precision
Solution Approach 2:
The patent uses clustering algorithms to create simplified representations (clusters) of complex geographic patterns, allowing resource allocation decisions to be based on these simplified models rather than processing all raw geographic data, thereby reducing computational complexity while preserving allocation precision
3Adaptability or versatility
If data from multiple sources is collected and analyzed, then understanding of human dynamics improves for better service tailoring, but data management complexity increases
Solution Approach 1:
The patent employs clustering algorithms that can handle multiple types of data sources (resource consumption, presence indicators, human dynamics) simultaneously, allowing a single analytical framework to serve multiple functions and reduce data management complexity while improving service tailoring capability
Data Source
AI summary
A method is disclosed for allocating resources in a geographic area. The method comprises: subdividing the area into a number of pixels; selecting an observation period and subdividing the observation period in a number of time sub-intervals; acquiring a data set associated with the pixels during the observation period; on the basis of a sub-set of data of the data set, the sub-set of data being associated with the identified one or more typical time-subintervals of the observation period, applying a clustering algorithm to partition the pixels into a number of clusters, each cluster comprising a respective group of pixels associated with similar trends in the resource consumption and/or in the presence indicator; and allocating resources to a cluster of the number of clusters on the basis of the trends.