Server-Based Spatial Model Mapping with Adaptive Area Subdivision
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Solution Overview
Problem
Existing systems face challenges in determining the appropriate model and capacity for IoT devices in different areas, such as dense urban environments versus open fields, leading to sub-optimal spatial mapping and resource inefficiencies.
Innovation Solution
A server device and method for spatial mapping of models that adapt model allocation based on area characteristics, using iterative methods to determine optimal model sizes and capacities, and adjust model boundaries dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single model is used for a large area, then device complexity is reduced, but model performance deteriorates due to insufficient granularity for dense areas
Solution Approach 1:
The patent divides the service area into multiple smaller areas (e.g., grid cells or zones) and assigns different models to different areas based on their characteristics. This segmentation allows the system to use simpler models in uniform areas while reserving complex models for dense areas, thus resolving the contradiction between model complexity and performance accuracy.
Solution Approach 2:
The patent applies local quality by tailoring model selection to specific area characteristics rather than using a uniform model everywhere. Each area is evaluated for its density and complexity, and models are selectively allocated accordingly - simpler models for low-density areas and more complex models for high-density areas, optimizing the balance between complexity and performance.
2Measurement precision
If a high-capacity model is allocated to all areas, then model performance is maximized, but resource utilization efficiency deteriorates due to wasted computational resources in low-density areas
Solution Approach 1:
The patent segments the service area into multiple zones with different characteristics and allocates models of varying capacities to each zone. This prevents the wasteful allocation of high-capacity models to all areas uniformly, instead matching model capacity to actual area needs and reducing computational resource waste in low-density areas.
Solution Approach 2:
The patent changes the model capacity parameter dynamically based on area characteristics. Instead of using a fixed high-capacity model everywhere, the system adjusts the model capacity parameter according to area density and complexity, allocating higher capacity only where necessary and using lower capacity models in appropriate contexts to optimize resource utilization efficiency.
3Measurement precision
If the service area is divided into smaller areas, then model performance improves through better granularity, but device complexity increases due to more areas to manage
Solution Approach 1:
The patent implements segmentation by dividing the service area into smaller manageable units while establishing clear criteria for segmentation (e.g., density thresholds, geographical boundaries). This structured approach to segmentation enables improved spatial mapping precision while keeping area management complexity可控 through systematic classification and automated model allocation rules.
4Measurement precision
If model capacity is increased for better performance, then measurement precision improves, but use of energy by moving object increases due to higher computational requirements
Solution Approach 1:
The patent applies local quality by matching model capacity to local area requirements. High-capacity models are deployed only in areas where they are needed (dense areas), while lower-capacity models are used in appropriate contexts (low-density areas). This selective deployment optimizes the balance between measurement precision and energy consumption by avoiding unnecessary computational resources in areas where simpler models suffice.
Data Source
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AI summary
A server device is provided comprising memory circuitry, one or more interfaces, and processor circuitry. The server device is configured to obtain a first model for a first area. The server device is configured to obtain first operational data associated with the first area, from the one or more first electronic devices. The server device is configured to determine a second model, based on the first operational data and the first model. The server device is configured to obtain a first performance parameter indicative of a performance of the first model. The server device is configured to obtain a second performance parameter indicative of a performance of the second model. The server device is configured to determine a model performance parameter based on the first performance parameter and the second performance parameter. The server device is configured to determine whether the model performance parameter satisfies a first criterion. The server device is configured to, when the model performance parameter does not satisfy the first criterion, determine whether the second performance parameter satisfies a second criterion. The server device is configured to, when the second performance parameter does not satisfy the second criterion, determine a second area, the second area being smaller than the first area; and obtain a third model for the second area.