Network-Sensitive Sensor Data Collection for Industrial IoT Bandwidth
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial environments face challenges in data collection and utilization due to complex sensor data management, limited data range, and variability in network connectivity, leading to inefficient monitoring and optimization of operations.
Innovation Solution
The implementation of methods and systems for continuous ultrasonic monitoring, self-organizing data marketplaces, and AI training based on industry-specific feedback, along with network-sensitive data collection and augmented reality interfaces, to enhance data collection, processing, and utilization in industrial IoT environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected continuously from multiple sensors in industrial environments, then the quantity and quality of data improve, but network bandwidth consumption and system complexity increase
Solution Approach 1:
The patent segments the data collection system into distributed edge computing nodes that process and filter sensor data locally before transmission. Each node independently manages its sensor inputs, performing preliminary analysis and selective data transmission based on event thresholds, thereby reducing overall network bandwidth consumption while maintaining data quality.
Solution Approach 2:
The patent introduces a hierarchical data transmission architecture that adds temporal and priority dimensions to data flow management. Critical anomaly data is transmitted immediately with high priority, while routine operational data is aggregated and transmitted during off-peak network periods, effectively managing bandwidth consumption without compromising monitoring quality.
2Reliability
If data transmission frequency is increased to improve real-time monitoring, then monitoring quality improves, but network bandwidth consumption increases
Solution Approach 1:
The patent implements periodic data transmission intervals adjusted dynamically based on operational conditions. During normal operations, data is transmitted at lower frequencies to conserve bandwidth, while the system automatically increases transmission frequency when anomaly detection algorithms identify critical patterns or threshold violations, ensuring monitoring quality without constant high-bandwidth consumption.
Solution Approach 2:
The patent dynamically changes transmission parameters including data sampling rate, compression level, and transmission priority based on operational context. The system adjusts these parameters in real-time according to equipment criticality, environmental conditions, and network availability, optimizing the balance between monitoring quality and bandwidth consumption.
3Measurement precision
If more sensors are deployed to capture comprehensive industrial data, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The patent employs multi-functional sensor platforms that can detect multiple physical quantities using the same hardware infrastructure. For example, vibration sensors are configured to detect mechanical anomalies, acoustic emissions, and thermal patterns through algorithmic processing, thereby achieving comprehensive monitoring precision without proportionally increasing sensor quantity or system complexity.
Solution Approach 2:
The patent introduces intelligent edge computing devices as intermediaries between physical sensors and central monitoring systems. These intermediaries aggregate data from multiple sensors, perform local analysis, and generate consolidated high-precision measurements, reducing the need for direct connections between each sensor and the central system, thereby simplifying overall system architecture.
4Measurement precision
If AI models are trained with extensive industry-specific feedback data, then diagnostic accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The patent pre-trains AI diagnostic models during system deployment using historical industry-specific data and feedback, creating baseline diagnostic capabilities before actual operations begin. This preliminary training establishes foundational knowledge that enables rapid real-time diagnostics without requiring extensive processing during operational decision-making, thereby reducing data processing time while maintaining high diagnostic accuracy.
Solution Approach 2:
The patent implements self-learning AI models that continuously refine their diagnostic accuracy using feedback from operational data without requiring manual retraining. The system automatically incorporates new patterns and anomalies into its diagnostic algorithms, improving accuracy over time while maintaining rapid processing speeds through incremental learning rather than complete retraining cycles.
Data Source
AI summary
The present disclosure describes systems for self-organized, network-sensitive data collection in an industrial environment. A system can include an industrial system including a plurality of components, at least one operatively coupled to a sensor, a sensor communication circuit to interpret sensor data values, and a system collaboration circuit to communicate a portion of the data values to a storage target according to a sensor data transmission protocol. A transmission environment circuit may determine transmission conditions corresponding to the communication of the portion of data values to the storage target and a network management circuit update the data transmission protocol in response to the transmission conditions.