Human Flow Estimation with Sensor Failure-Aware Model Adjustment
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Solution Overview
Problem
Existing human flow estimation systems face significant accuracy reductions due to sensor failures, as failed sensors are not effectively processed, leading to unreliable estimation results.
Innovation Solution
A human flow estimation system with a failure detection module that adjusts the human flow state model and sensor network model in real-time, either by reducing the data weight of faulty sensors or removing them from the model, using algorithms like the Kalman filter, to maintain high precision and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are deployed to improve human flow estimation accuracy, then measurement precision is improved, but the risk of sensor failures increases
Solution Approach 1:
The system performs preliminary actions by detecting sensor failures in advance through the failure detection module, which monitors sensor data quality and identifies abnormal sensors before they completely fail. This allows the system to proactively adjust the sensor network model and human flow state model to compensate for potential failures, thereby maintaining estimation accuracy despite the presence of multiple sensors
Solution Approach 2:
The system changes parameters dynamically by adjusting the sensor network model and human flow state model based on detected sensor failures. When failures are detected, the system modifies model parameters to exclude or downweight failed sensors, ensuring that the estimation algorithm continues to produce accurate results despite the increased sensor count and associated failure risks
2Device complexity
If failed sensors are not processed, then device complexity is reduced, but estimation accuracy deteriorates
Solution Approach 1:
The system implements feedback mechanisms where the failure detection module continuously monitors sensor performance and provides feedback to the model building module. This feedback loop enables automatic detection of failed sensors and triggers model adjustments without requiring complex manual processing, thus maintaining estimation accuracy while keeping the processing system relatively simple through automated closed-loop control
3Reliability
If real-time model adjustment is implemented to handle sensor failures, then reliability is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically detecting sensor failures and adjusting its own models without external intervention. The failure detection module autonomously identifies problematic sensors, and the model building module automatically recalibrates the sensor network model and human flow state model, enabling the system to maintain high reliability through self-correcting mechanisms while avoiding the complexity of external manual processing systems
Data Source
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Figure 3
AI summary
A human flow estimation system comprises: a sensor network comprising a plurality of sensors arranged in a to-be-estimated region for detecting the human flow; a model building module configured to build a human flow state model based on arrangement positions of the sensors, and build a sensor network model based on data of the sensors; and a human flow estimation module configured to estimate the human flow and provide a data weight of the estimated human flow based on the human flow state model and the sensor network model. The human flow estimation system further comprises a failure detection module configured to detect whether each sensor in the sensor network is abnormal, and the model building module is further configured to adjust the human flow state model and the sensor network model when an exception exists on the sensor.