Location Intelligence for Anonymous EI Job Classification
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Solution Overview
Problem
Existing systems lack accurate and timely information about energy infrastructure features and job functions of anonymous users, leading to inefficiencies in fluid transportation and management within the energy industry, particularly in oilfield regions.
Innovation Solution
A computer-implemented method that processes anonymized location data from mobile devices to determine job classifications and identify new energy infrastructure features by correlating visited locations with known facilities, enabling the determination of production, productivity, and fluid transport methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If anonymized location data is processed to determine job classifications and identify energy infrastructure features, then information accuracy and timeliness improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of energy infrastructure monitoring into distinct modules: location data collection from mobile devices, anonymization processing, job classification determination, and infrastructure feature identification. Each module handles a specific aspect of the data processing pipeline, reducing overall system complexity while maintaining high information accuracy through specialized processing at each stage.
Solution Approach 2:
The patent introduces an intermediary anonymization layer that processes location data between collection and analysis stages. This intermediary component removes personally identifiable information while preserving spatial and temporal patterns needed for job classification and infrastructure identification, thereby reducing privacy risks and simplifying compliance requirements without compromising measurement precision.
2Productivity
If comprehensive location data is collected from mobile devices, then productivity insights improve, but data privacy risks and processing overhead increase
Solution Approach 1:
The system extracts and removes personally identifiable information from location data through anonymization processing. By taking out sensitive identifiers while retaining spatial-temporal patterns, the system preserves productivity insights related to worker movements and infrastructure usage without compromising individual privacy, thus resolving the contradiction between comprehensive data collection and privacy protection.
Solution Approach 2:
The patent transforms location data parameters by converting precise personal identifiers into anonymized spatial-temporal patterns. This parameter change maintains the analytical value for productivity assessment (movement patterns, site visitation frequency) while eliminating privacy risks associated with personal identification, enabling comprehensive monitoring without data privacy loss.
3Productivity
If real-time location tracking is implemented, then operational efficiency improves, but energy consumption and device resource usage increase
Solution Approach 1:
Instead of continuous real-time tracking, the system implements periodic location data collection at strategically determined intervals. This periodic action maintains operational efficiency by capturing sufficient spatial-temporal patterns for productivity analysis while significantly reducing energy consumption and device resource usage compared to continuous monitoring, thus resolving the contradiction between efficiency and energy use.
Data Source
AI summary
Systems and methods are described for determining job classifications of anonymous users. Energy Infrastructure (EI) information associated with a known EI facility and anonymized location tracking data is obtained. The EI facility information includes a location of the known EI facility and an identification of the known EI facility, and the anonymized location tracking data includes visited locations associated with an anonymous user ID. A job classification is associated with the anonymous user ID based on a correlation between the visited locations, the location of the known EI facility, and the identification of the known EI facility. Location tracking data associated with a user of a known job classification can be used to identify a previously unknown EI facility.