Foreground Filtering for Network Performance Data Collection
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Solution Overview
Problem
Existing performance statistical methods in telecommunications network management lack flexibility and efficiency in data processing and usability, leading to a high burden on network resources and inefficient data mining.
Innovation Solution
A method and system for collecting performance statistical data that involves configuring a basic object, setting object sets, creating measurement tasks, and filtering data in the foreground, allowing for efficient data collection and management, with the ability to configure and combine specific objects according to user requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all collected performance data is transmitted to the network manager for processing and reporting, then complete data availability is achieved, but the burden on network resources and foreground single board increases significantly
Solution Approach 1:
The patent extracts and processes data locally at the foreground single board level rather than transmitting all raw data to the network manager. The foreground board performs local filtering, sorting, and preliminary processing to extract only the necessary performance statistics data, thereby reducing network transmission burden while maintaining data completeness for reporting.
Solution Approach 2:
The patent segments the data processing function into multiple levels: the foreground single board handles data collection, filtering, and preliminary processing; the network manager handles reporting and analysis. This segmentation allows each component to perform its specific function efficiently, reducing the overall system burden while maintaining complete data availability.
2Stability of the object's composition
If conventional performance statistical methods are used with fixed data collection and reporting processes, then system stability is maintained, but data flexibility and usability are reduced
Solution Approach 1:
The patent introduces dynamic configurability to the performance statistical system, allowing users to flexibly configure data collection parameters, filtering criteria, and reporting formats based on specific needs. The system maintains stable core processing functions while allowing adaptive configuration of data handling parameters, achieving both stability and flexibility.
3Reliability
If a large amount of collected data is transmitted and reported, then comprehensive monitoring is achieved, but the efficiency of data processing and user re-processing is reduced
Solution Approach 1:
The patent performs preliminary data processing, filtering, and organization at the foreground single board before data is transmitted to the network manager. This preliminary action reduces the volume of data requiring further processing and reporting, thereby improving overall data processing efficiency while maintaining comprehensive monitoring capabilities through selective data transmission.
Data Source
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AI summary
The disclosure discloses a performance statistical data collection method and system, both can configure a basic object according to a requirement by a network manager; the network manager sets object sets according to the basic object and sets specific objects in each object set, and synchronizes the object sets and the specific objects therein to a foreground, wherein the specific object is the specific form of the basic object; creates a measurement task according to the object sets and synchronizes the measurement task to the foreground; and counts received performance statistical data according to a requirement. According to the method and system of the disclosure, the basic object can be configured according to a requirement and the specific objects in the object sets are combined, which is convenient, fast and practical; and, the performance statistical data is further counted and managed in the network manager automatically, which is efficient and fast and provides a user a more attentive and humanized service. In addition, the data is filtered in the foreground, so that the data needs to be transmitted and transferred between memories as well as the burden of the network are reduced and a high efficiency is achieved; and at the same time, the burden of the network manager is also reduced and the usability of the network manager is improved.