Dispatching Cloud Edge Processing for Bandwidth and Compute Relief
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Solution Overview
Problem
The centralized data processing model in dispatching and control clouds faces significant computing and storage pressures, along with increased bandwidth demands due to repeated data uploads caused by data errors, which cannot meet the evolving data processing needs.
Innovation Solution
A decentralized data processing approach is implemented, where global scheduling tasks are decomposed and distributed to collaborative node devices for initial processing, with optimized data collection ranges and rules, followed by quality evaluation and iterative optimization to ensure high-quality data is uploaded to the pilot node device, reducing computational and bandwidth burdens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data processing is implemented at the cloud center, then data quality control and evaluation are improved, but computing pressure and storage pressure on the cloud center increase significantly
Solution Approach 1:
The patent segments the centralized data processing function into distributed edge processing nodes. Collaborative nodes perform local data processing, filtering, and preliminary quality control, while the cloud center focuses on comprehensive evaluation and coordination. This segmentation reduces the computing burden on the cloud center while maintaining data quality control capabilities.
Solution Approach 2:
The patent introduces collaborative nodes as intermediary entities between data sources and the cloud center. These nodes act as mediators that perform initial data processing, validation, and filtering, reducing the volume and quality of data that needs to be processed by the cloud center, thereby reducing computing pressure while maintaining quality control.
2Stability of the object's composition
If centralized data processing is implemented at the cloud center, then unified data management is improved, but bandwidth demand increases due to repeated data uploads
Solution Approach 1:
The patent implements preliminary data processing and filtering at collaborative nodes before data is uploaded to the cloud center. Data is pre-validated, pre-processed, and pre-filtered at the edge, reducing the need for re-uploads due to quality issues. This preliminary action maintains unified data management while significantly reducing bandwidth consumption.
Solution Approach 2:
The patent establishes a feedback mechanism where the cloud center evaluates data quality and provides guidance to collaborative nodes. This feedback loop enables continuous improvement of data processing at the edge, reducing repeated uploads and optimizing bandwidth utilization while maintaining unified data management standards.
3Ease of operation
If all data is collected and processed uniformly at the cloud center, then data standardization is improved, but processing timeliness deteriorates
Solution Approach 1:
The patent segments data processing into real-time edge processing at collaborative nodes and batch processing at the cloud center. Time-sensitive operations are performed locally with immediate effect, while standardized processing occurs centrally. This segmentation simultaneously improves processing timeliness for critical operations and maintains data standardization through centralized coordination.
Solution Approach 2:
The patent implements dynamic data processing where the processing location and method adapt based on data characteristics and timing requirements. Urgent data is processed immediately at edge nodes, while non-urgent data undergoes comprehensive centralized processing. This dynamic approach balances processing timeliness with data standardization.
Data Source
AI summary
The present application discloses a dispatching and control cloud data processing method, device and system. The method includes the following operations: pilot node device acquires a global scheduling task, decomposes the global scheduling task to obtain scheduling tasks, issues the scheduling tasks to collaborative node device, acquires data collection ranges and data processing rules of the collaborative node devices, and delivers them to the collaborative node devices; the collaborative node devices receive and execute the scheduling tasks issued by the pilot node device; receives the data collection ranges and the data processing rules issued by the pilot node device, acquires, based on the scheduling tasks, collected data in the data collection ranges, processes the acquired collected data according to the data processing rules to obtain the processed data, uploads the processed data to the pilot node device; the pilot node device receives the processed data uploaded by the collaborative node devices.