Predictive RF Test Path for Public Safety Building Certification
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Solution Overview
Problem
Current systems for testing public safety networks in buildings require extensive manual labor and time, as users must walk to multiple test points to collect coverage data, often resulting in substantial effort and resources being spent to determine if a building meets certification standards.
Innovation Solution
Systems and methods that use predictive data from adjacent buildings to identify areas likely to fail certification, allowing for an optimized test path that focuses on the minimum number of critical test points, reducing the need for extensive data collection and streamlining the certification process by using a signal measurement device to direct users to the most critical test points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users walk to all test points to collect coverage data, then complete certification data is obtained, but time and labor resources are substantially consumed
Solution Approach 1:
The system performs preliminary actions by using predictive data from adjacent buildings to identify which test points are likely to fail certification before the actual walk test occurs. This allows the system to pre-determine an optimized test path that focuses only on critical areas, eliminating the need to visit all possible test points while still achieving complete certification assessment.
Solution Approach 2:
Instead of requiring complete coverage of all test points, the system applies partial action by selecting only the minimum necessary subset of test points that are most likely to determine certification outcome. The optimized path visits only these critical points, which is less than the full set but sufficient for making a definitive certification determination.
2Quantity of substance
If users manually record coverage data at multiple test points, then comprehensive network performance data is collected, but manual labor and effort are substantially increased
Solution Approach 1:
The system enables self-service by using predictive algorithms to automatically determine the optimized test path without requiring manual planning or analysis. The system itself identifies which test points to visit based on predictive data from adjacent buildings, eliminating the need for users to manually determine which areas need testing.
Solution Approach 2:
The system extracts only the essential and critical test points from the complete set of possible test points. By using predictive analysis, it separates the necessary test points from unnecessary ones, creating a streamlined path that collects sufficient data without requiring comprehensive coverage of all areas.
3Productivity
If predictive data from adjacent buildings is used to identify failing areas, then test path is optimized and time is reduced, but complexity of prediction system increases
Solution Approach 1:
The system applies universality by using the same predictive analysis framework across multiple buildings and test scenarios. The predictive model developed from adjacent buildings can be universally applied to determine test paths for different buildings, making the system adaptable and reusable rather than requiring custom solutions for each case.
Data Source
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AI summary
Systems and methods for selecting a worst service first optimized test path are provided. Such systems and methods can include using previously collected RF results that are relevant to a target building to calculate predictive RF results for a plurality of test points in the target building, using the predictive RF results to calculate the optimized test path through the target building, measuring a respective actual RF signal value of each of the plurality of test points in succession along the optimized test path, counting failing ones of the plurality of test points, and stopping measurements of the respective actual RF signal value of each of the plurality test points when a counted number of the failing ones of the plurality of test points exceeds a failure threshold value.