Adaptive Geospatial Data Acquisition via Information Value
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Solution Overview
Problem
Current methods for acquiring geospatial data from APIs often use fixed frequencies and resolutions, leading to inefficient resource usage in stable environments and inadequate performance in unstable environments, due to the varying value of data based on context such as weather and traffic conditions.
Innovation Solution
A computer-implemented method that determines the context for geospatial data and calculates an information value for each information acquisition method based on information loss, amount, and cost, selecting the method with the highest value to optimize data acquisition frequency and spatial resolution accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed frequency and resolution are used for data acquisition, then device complexity is reduced, but information value and adaptability deteriorate
Solution Approach 1:
The patent implements dynamic data acquisition by transitioning from fixed frequency and resolution to variable parameters that adapt to environmental conditions. The system continuously monitors context factors (weather, traffic, events) and adjusts acquisition frequency and resolution dynamically, allowing the same system to optimize performance across different scenarios without requiring multiple dedicated systems.
Solution Approach 2:
The patent changes the parameters of the data acquisition system itself (frequency, resolution, spatial coverage) based on environmental context. By modifying these acquisition parameters in response to contextual factors, the system achieves adaptability while managing cost and resource usage, directly resolving the contradiction between simplicity and adaptability.
2Reliability
If high frequency and high resolution are used for data acquisition, then information quality and reliability are improved, but cost increases
Solution Approach 1:
The patent applies partial action by acquiring data at high frequency and resolution only when necessary (during unstable environmental conditions or critical events), rather than continuously. During stable periods, the system reduces acquisition intensity, thereby maintaining data quality when needed while significantly reducing overall cost and resource consumption.
Solution Approach 2:
The system dynamically adjusts acquisition intensity based on real-time contextual assessment. When environmental factors indicate stability, acquisition frequency and resolution are reduced; when instability or critical events are detected, the system automatically increases acquisition intensity, optimizing the balance between data quality and cost.
3Loss of energy
If low frequency and low resolution are used for data acquisition, then cost is reduced, but information loss increases
Solution Approach 1:
The patent implements feedback mechanisms that continuously monitor environmental context and data quality metrics. This feedback loop allows the system to detect when reduced acquisition frequency or resolution begins to cause information loss, and automatically adjust parameters upward to prevent degradation, thereby maintaining information quality while managing cost.
Solution Approach 2:
The system performs preliminary assessment of environmental stability before reducing acquisition intensity. By evaluating contextual factors in advance, the system can confidently reduce frequency and resolution only when stability is confirmed, preventing information loss while achieving cost reduction during stable periods.
4Reliability
If data acquisition frequency is increased to capture unstable conditions, then reliability is improved, but resource usage increases
Solution Approach 1:
The patent implements dynamic resource allocation where acquisition frequency automatically scales with environmental volatility. During unstable conditions, frequency increases to maintain reliability; during stable conditions, frequency decreases to improve resource efficiency. This dynamic behavior resolves the contradiction by making resource usage proportional to actual need.
Solution Approach 2:
The system changes acquisition parameters (frequency, resolution, spatial scope) based on contextual assessment of environmental stability. This parameter adaptation allows the system to maintain high reliability during critical periods while achieving improved resource efficiency during stable periods, eliminating the need for consistently high resource usage.
Data Source
AI summary
Described are techniques for acquiring geospatial data according to an information value. The techniques including determining a context for geospatial data to be used in an application, where the context is based on one or more external factors that influence variation of the geospatial data. The techniques further include calculating an information value of the geospatial data in the context for each of a plurality of information acquisition methods, where the plurality of information acquisition methods include respective data acquisition frequencies and respective spatial resolutions, and where the information value is based on an information loss function, an information amount, and a cost. The techniques further include selecting a first information acquisition method with a highest information value and acquiring the geospatial data using the first information acquisition method.