Reconfigurable Cloud Agent for Mobile Fracking Asset Data Collection
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Solution Overview
Problem
The management of mobile industrial assets in dynamic industrial operations, such as fracking stations, is challenging due to the frequent change in asset deployment and the limited access to data across geographically diverse locations, leading to difficulties in data collection and analysis.
Innovation Solution
A scalable mobile asset management system that uses an agent-based architecture to dynamically allocate and configure data collection for mobile assets, maintaining asset models that track capabilities, location, and maintenance schedules, and correlating them with operational demands to select suitable assets for scheduled operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile industrial assets are frequently deployed and relocated across geographically diverse locations, then operational flexibility and asset utilization are improved, but data collection and access become more difficult
Solution Approach 1:
The patent introduces cloud agent devices as intermediaries between mobile assets and the central management system. These agents are deployed to geographically distributed locations and maintain local connections to multiple assets, serving as mediators that enable data collection without requiring direct continuous access to each moving asset. The agents cache and forward telemetry data, resolving the data access problem caused by asset mobility.
Solution Approach 2:
The system transitions from a centralized data collection architecture to a distributed multi-dimensional architecture. Instead of all data flowing through a single central point, the patent creates a hierarchical structure with cloud agents at multiple geographic locations, each collecting data from local assets. This dimensional distribution enables simultaneous data collection from assets across different locations without requiring centralized direct access.
2Quantity of substance
If a centralized system collects data from all mobile assets, then data completeness is improved, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the centralized data collection system into distributed cloud agent components. Each cloud agent handles data collection and preprocessing for a specific geographic region or asset group, reducing the computational burden on any single system component. The segmentation allows the system to maintain complete data collection while distributing processing complexity across multiple independent agents.
Solution Approach 2:
Cloud agents perform partial data processing and filtering locally before transmitting to the central system. Instead of transmitting all raw data from every asset, agents selectively collect and preprocess telemetry data based on local criteria, reducing the volume of data requiring centralized processing while maintaining data completeness for analysis purposes.
3Loss of information
If data collection is configured for all mobile assets, then data availability is improved, but resource consumption and processing overhead increase
Solution Approach 1:
The patent implements local quality by enabling each cloud agent to independently configure data collection parameters based on local asset types, operational contexts, and priorities. Instead of uniform data collection across all assets, each agent adapts its data collection strategy to local conditions, collecting comprehensive data where needed while reducing collection intensity where sufficient data already exists or is less critical.
Solution Approach 2:
The system dynamically changes data collection parameters such as sampling frequency, data types collected, and transmission intervals based on asset operational states, location, and priority. Cloud agents adjust these parameters in real-time, collecting high-frequency data from critical assets while using lower-frequency collection for less critical assets, thereby maintaining data availability while reducing overall processing overhead and energy consumption.
Data Source
AI summary
A scalable industrial asset management system dynamically negotiates allocation of mobile industrial assets to industrial operation sites. The asset management system tracks and models the capabilities and availabilities of a pool of mobile industrial assets (e.g., truck-mounted assets or other such assets). Based on a defined demand of a scheduled industrial operation requiring mobile industrial assets (e.g., a fracking operation, a mining operation, etc.) the system selects a subset of the mobile industrial assets that are both available during the scheduled operation and are collectively capable of satisfying the demands of the industrial operation. Moreover, based on the asset models for the subset of mobile industrial assets, the system configures an on-premise cloud agent device to collect telemetry data from the mobile assets during the operation and to migrate the collected data to a cloud-based collection and analytics system.