Human Flow Estimation with Sensor Failure-Aware Model Adjustment
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Solution Overview
Problem
Existing human flow estimation systems face accuracy reduction 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 estimation.
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 implements a feedback mechanism where the failure detection module continuously monitors sensor status and provides information to the model building module. When sensor failures are detected, the system automatically adjusts the sensor network model by removing failed sensors or reducing their data weights, thereby maintaining reliable human flow estimation despite the presence of multiple sensors that may fail
Solution Approach 2:
The system dynamically changes parameters in the sensor network model based on sensor health status. When sensor failures are detected, the model building module modifies the sensor network model by adjusting data weights or removing failed sensors from the model, allowing the system to maintain accurate human flow estimation while accounting for sensor failures
2Reliability
If failed sensors are removed from the sensor network model, then reliability is improved, but device complexity increases due to real-time model adjustment
Solution Approach 1:
The system implements self-service through automated failure detection and model adjustment. The failure detection module automatically identifies failed sensors, and the model building module automatically adjusts the sensor network model without requiring manual intervention. This self-service mechanism maintains reliable estimation results while managing complexity through automation rather than manual processes
3Reliability
If real-time sensor failure detection and model adjustment is implemented, then reliability is improved, but use of energy increases
Solution Approach 1:
The system applies partial action by selectively adjusting only the portions of the sensor network model affected by sensor failures. Rather than reprocessing the entire model, the model building module focuses computational resources on adjusting data weights or removing specific failed sensors from the model, thereby maintaining system stability while reducing unnecessary energy consumption
Data Source
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.