Network Data Rollback via Feature Data Segmentation
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Solution Overview
Problem
Current network data rollback methods roll back all different data simultaneously, which is inefficient and can cause unnecessary impact on the network, especially when physical hardware configuration and data configuration changes occur simultaneously, leading to potential hardware modifications during rollback.
Innovation Solution
The method allows selective rollback of feature data by comparing snapshots at different time stamps and applying a preset rollback rule, enabling partial or complete network data rollback using feature data as the unit, thereby reducing the overall impact and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If all different data is rolled back in a unit of entire different data, then the rollback operation is simple to implement, but the rollback efficiency is low and the impact on the network is large
Solution Approach 1:
The patent segments the entire different data into multiple feature data units (e.g., feature data 1, feature data 2, ..., feature data n). Each feature data represents a specific functional module or configuration element. By dividing the rollback operation into discrete feature data segments, the system can selectively rollback only the necessary portions rather than the entire dataset, thereby improving rollback efficiency while maintaining manageable operational complexity through structured organization.
Solution Approach 2:
The patent implements partial action by allowing selective rollback of specific feature data units based on predefined rollback rules. Instead of performing a complete rollback of all different data, the system identifies and rolls back only the feature data that meets the rollback criteria (e.g., feature data associated with deteriorated network indices). This partial approach reduces the scope of rollback operations, improving efficiency while avoiding unnecessary complexity from handling the entire dataset.
2Ease of operation
If all different data is rolled back simultaneously, then the rollback process is straightforward, but the data volume for rollback is large causing network impact
Solution Approach 1:
The patent divides the large volume of different data into smaller feature data units that can be independently identified and processed. Each feature data unit represents a manageable portion of the overall dataset. This segmentation allows the system to apply rollback rules to specific units rather than processing the entire large dataset simultaneously, reducing the effective data volume subject to rollback while maintaining operational simplicity through automated rule-based selection.
Solution Approach 2:
The patent extracts specific feature data units from the entire different data set based on rollback rules. By identifying and separating the feature data that requires rollback (e.g., feature data corresponding to problematic configurations) from the rest of the data, the system reduces the quantity of data that needs to be processed during rollback. This extraction approach maintains ease of operation through automated identification while significantly reducing the data volume that impacts the network.
3Adaptability or versatility
If feature data is used as the unit for rollback, then the rollback flexibility is improved, but the data comparison and selection process becomes more complex
Solution Approach 1:
The patent segments data into standardized feature data units with defined structures and attributes. Each feature data unit represents a specific functional element (e.g., configuration parameter, network element) with identifiable characteristics. This segmentation enables flexible selection and combination of different feature data units for rollback operations while managing complexity through consistent unit structures that simplify comparison and selection processes.
Solution Approach 2:
The patent implements flexible rollback by allowing dynamic adjustment of rollback rules and parameters. The system can modify which feature data units are selected for rollback based on changing network conditions, performance metrics, and operational requirements. Parameters such as rollback thresholds, time windows, and selection criteria can be adjusted to achieve the desired level of flexibility without permanently increasing system complexity, as the underlying feature data structure remains consistent.
Data Source
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AI summary
The present invention relates to the field of communications, and in particular, to a network data rollback method and a rollback device, where the method includes: when data rollback needs to be performed, comparing a data snapshot at a second time stamp with a data snapshot at a first time stamp to find different feature data; and selecting, from the different feature data, feature data that needs to be rolled back, and performing data rollback according to a preset rollback rule, so as to roll back a part or all of network data at the second time stamp to a network data state at the first time stamp. In the present invention, a part or all of different data of data snapshots at two time stamps may be rolled back in a unit of feature data.