Industrial Sensor Data Storage With Adaptive Resolution Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial environments face challenges in data collection and utilization due to complex sensor configurations, variable network conditions, and the need for real-time data management, which limits the effectiveness of monitoring and optimization in heavy industrial settings.
Innovation Solution
The implementation of a system for continuous ultrasonic monitoring, self-organizing data marketplaces, and AI training based on industry-specific feedback, along with a self-sufficient data acquisition box that dynamically reconfigures data transmission and storage, enables improved data collection and utilization in industrial IoT environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected continuously from multiple sensors in industrial environments, then data completeness and monitoring capability are improved, but data management complexity and network load increase
Solution Approach 1:
The patent segments the data collection and management system into distributed edge computing nodes that process data locally before transmission. Each node handles specific sensor inputs independently, dividing the complex data management task into manageable segments that reduce overall system complexity and network load.
Solution Approach 2:
The patent introduces a hierarchical data management architecture that adds temporal and spatial dimensions to data processing. Data is organized by time windows and geographic locations, creating a multi-dimensional structure that simplifies querying and management of large datasets from multiple sensors.
2Speed
If data transmission frequency is increased for real-time monitoring, then response time is improved, but network bandwidth consumption and energy use increase
Solution Approach 1:
The patent implements periodic data transmission with adaptive intervals based on event significance. Routine sensor data is transmitted at optimized intervals rather than continuously, while anomaly detection triggers immediate transmissions. This periodic approach maintains real-time monitoring capability while significantly reducing network energy consumption.
Solution Approach 2:
The system dynamically changes transmission parameters including frequency, data volume, and compression level based on operational conditions. During normal operations, transmission frequency is reduced to conserve energy, while critical events trigger high-frequency transmissions with full data detail, optimizing the balance between response time and energy use.
3Loss of information
If data storage capacity is increased to retain more sensor data, then data analysis capability is improved, but storage cost and data processing time increase
Solution Approach 1:
The patent extracts and stores only the most valuable data features and patterns rather than retaining all raw sensor data. Edge computing nodes perform preliminary data processing to extract relevant parameters, storing compressed representations that maintain analytical capability while reducing storage requirements and processing time by orders of magnitude.
Solution Approach 2:
The system implements selective data retention policies where redundant or expired data is discarded while critical data is preserved. Data is annotated with metadata indicating its value and retention requirements, allowing the system to automatically discard low-value data and recover or retrieve critical data when needed, optimizing the balance between retention capability and processing efficiency.
Data Source
AI summary
Systems for data collection and self-organizing storage including enhanced resolution are disclosed. An example system includes an industrial system with a plurality of components, a subset of which is operatively coupled to a plurality of sensors which provide sensor values. An example system may further include self-organizing storage for at least a portion of the plurality of sensor values and a means for enhancing resolution of the sensor values in response to an enhanced data request value or an alert value. Enhanced resolution may include an enhanced spatial resolution, an enhanced time domain resolution, a greater number of the plurality of sensor values than a standard resolution of the plurality of sensor values, or a greater precision of at least one of the plurality of sensor values than the standard resolution of the plurality of sensor values.