Edge Data Analysis Application Upgrades With Cloud Context Feedback
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Solution Overview
Problem
Industrial plants face challenges in quickly generating customized data analysis applications to improve production efficiency due to limitations in network transmission and the need for domain expertise, with existing solutions either relying on cloud-based algorithms or underutilizing edge devices' computing capacity.
Innovation Solution
A data analysis method and system that performs analysis on edge devices, collecting key performance indicators, evaluating application performance, generating upgrade requirements, and sending them to the industrial cloud for context data collection and application updates, thereby integrating domain conditions and optimizing resource utilization across both edge devices and industrial clouds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all data is sent to the cloud terminal for data analysis, then the data analysis capability is improved, but the network transmission burden increases and network connection quality requirements worsen
Solution Approach 1:
The patent segments the data analysis system into cloud terminal and edge device components. The cloud terminal performs comprehensive data analysis while edge devices perform local preprocessing and filtering, reducing the amount of data that needs to be transmitted over the network while maintaining overall analysis capability.
Solution Approach 2:
The patent implements preliminary data processing at the edge device before data is sent to the cloud. Edge devices perform initial data collection, filtering, and preprocessing operations, so that only relevant and processed data needs to be transmitted to the cloud terminal, reducing network transmission burden.
2Measurement precision
If traditional cloud-based algorithms are used, then the data analysis is comprehensive, but the response speed and flexibility worsen due to reliance on domain experts
Solution Approach 1:
The patent enables the system to automatically generate data analysis algorithms through machine learning and automated model generation, eliminating the need for manual algorithm development by domain experts. The system self-adapts to new data patterns and automatically updates analysis models, improving response speed and flexibility.
Solution Approach 2:
The patent dynamically adjusts algorithm parameters and data analysis configurations based on real-time data characteristics and performance metrics. This allows the system to adapt quickly to changing conditions without requiring manual reconfiguration by experts, improving both response speed and comprehensiveness.
3Loss of energy
If big data analysis applications are deployed on intranet edge devices, then the network transmission burden is reduced, but the computing capacity and data volume limitations worsen the analysis effectiveness
Solution Approach 1:
The patent divides the data analysis workload into segments suitable for edge device processing and segments requiring cloud terminal resources. Edge devices handle local real-time monitoring and simple analysis, while the cloud terminal performs comprehensive big data analysis, optimizing the balance between network burden and analysis effectiveness.
Solution Approach 2:
The patent introduces an intermediary architecture where edge devices act as local processing nodes that prepare and filter data before cloud transmission. This intermediary layer ensures that edge devices operate within their computing capacity while still contributing to overall analysis effectiveness through local preprocessing and real-time response capabilities.
4Measurement precision
If customized data analysis applications are developed for specific industry scenarios, then the analysis precision is improved, but the development time and complexity worsen
Solution Approach 1:
The patent creates a universal data analysis platform with standardized interfaces and modular components that can be adapted to various industry scenarios. The system provides通用的 data collection, processing, and analysis capabilities that can be configured for different applications without requiring complete custom development, reducing development time while maintaining scenario-specific precision.
Solution Approach 2:
The patent enables customization of data analysis applications through parameter configuration rather than code development. Users can adjust analysis parameters, data sources, and processing rules to fit specific industry scenarios, allowing rapid deployment of precision-tuned applications without extensive development time.
Data Source
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AI summary
A data analysis method, device and system, the edge device (100) is connected with at least one of devices (310, 320, 330), wherein, the method comprises the following steps: S1, performing data analysis on the devices (310, 320, 330) by an application by collecting at least one key performance indicator of the devices (310, 320, 330), estimating the performance of the application according to the result of the data analysis, generating an application upgrade requirement on the basis of the performance estimation result, and sending the upgrade requirement to an industrial cloud (200); S2, receiving a context data requirement generated by the industrial cloud (200) on the basis of the upgrade requirement, collecting context data from the devices (310, 320, 330) according to the context data requirement, and returning the context data to the industrial cloud (200); and S3, downloading from the industrial cloud (200) the application updated on the basis of the context data. The data analysis mechanism reduces the development process of custom and special applications by automatically upgrading to adapt to changes in field application conditions.