Venue Network Performance Prediction for Event Congestion
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Solution Overview
Problem
Existing systems for predicting telecommunications network performance at events with large subscriber presence are laborious, time-consuming, and inaccurate due to manual processes such as diagram acquisition and network asset identification, often failing to mitigate poor connectivity issues in extreme congestion scenarios.
Innovation Solution
A performance prediction model that automates the process by using web scrapers to identify events, gather event data, and generate heatmaps based on handset-reported data to predict network performance, identifying key performance indicators (KPIs) and network access nodes (NANs) for improved network management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for diagram acquisition and network asset identification, then system complexity is reduced, but productivity decreases and measurement precision is compromised
Solution Approach 1:
The system performs self-service by automatically acquiring venue diagrams through web scraping, identifying network access nodes using automated algorithms, and generating performance predictions without requiring manual intervention. The system serves itself by collecting its own input data and executing the complete prediction workflow autonomously, thereby dramatically improving productivity while managing complexity through automation.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Web scraping tools automatically collect venue diagram data, algorithms automatically identify network access nodes from the diagrams, and computational models automatically generate performance predictions. This substitution of manual mechanical operations with automated electronic systems resolves the contradiction by improving productivity while the modular architecture manages system complexity.
2Measurement precision
If manual assessment methods are used for network performance prediction, then ease of operation is maintained, but measurement precision and reliability deteriorate
Solution Approach 1:
The system incorporates feedback mechanisms by automatically collecting actual network performance data during events and comparing it with predicted performance. This feedback loop enables continuous validation and improvement of prediction accuracy. The automated feedback process maintains measurement precision while the system handles the operational complexity of data collection and analysis, resolving the contradiction between precision and ease of operation.
Solution Approach 2:
The patent introduces an automated intermediary system that acts as a mediator between venue diagrams and performance predictions. The system uses web scraping tools as intermediaries to extract data, algorithms as intermediaries to identify network nodes, and computational models as intermediaries to generate predictions. These automated intermediaries improve measurement precision while managing operational complexity, resolving the contradiction.
3Measurement precision
If comprehensive data collection is performed for all events, then measurement precision improves, but loss of time increases due to manual processing
Solution Approach 1:
The system performs preliminary actions by proactively collecting and processing venue diagram data before events occur. Web scraping tools automatically acquire diagrams in advance, algorithms pre-identify network access nodes, and the system prepares prediction models beforehand. This preliminary automated processing enables comprehensive data collection without time loss during actual events, resolving the contradiction between precision and time consumption.
Solution Approach 2:
The patent implements continuous automated data collection and processing operations. The system continuously scrapes venue diagrams, continuously identifies network assets, and continuously updates prediction models without interruption or manual intervention. This continuous automated action enables comprehensive data collection while eliminating the time loss associated with manual processing, resolving the contradiction between measurement precision and processing time.
Data Source
AI summary
A method to predict performance of different telecommunications technologies at a venue by obtaining event data of one or more events. The method can identify a geocode and a profile of a venue. The method can generate a coverage heatmap that includes network coverage of the venue by different telecommunications network technologies. The method can identify one or more network access nodes of the different telecommunications networks configured to provide coverage to the venue. The method can predict performance of the different telecommunications network technologies for a particular event indicated in the event data based on performance of the one or more network access nodes. The method can cause display, on a display device, of a performance prediction dashboard including data about the predicted performance.