Tunnel event detection method based on integrated learning time sequence prediction

Abstract

The invention discloses a tunnel event detection method based on integrated learning time sequence prediction, which mainly solves the problem that similar methods fail to accurately predict values of a sensor and cannot effectively control tunnel ventilation. The method comprises the detection steps of: pre-processing acquired highway tunnel data to form a training data set; training a plurality of basic predictors according to the training data set, and forming a strong predictor by the weighted combination of the basic predictors; utilizing the strong predictor to calculate a predictive value of smoke concentration of a tunnel at t+1 time according to a value of a tunnel sensor at the current t time, and dynamically adjusting the basic predictors which take part in the integration according to prediction error; comparing the predictive value of the smoke concentration of the tunnel at t+1 time with a smoke concentration threshold, and judging whether the smoke concentration is an over-standard event; and for the over-standard event, calculating control parameters of a ventilation controller, and reducing the smoke concentration. The method has the advantages of strong prediction function and high control accuracy of the ventilation controller, and is used for operation monitoring, energy conservation and emission reduction of highway tunnels.

Description

technical field [0001] The invention belongs to the technical field of detection, in particular to a detection method related to highway events, which can be used for judging tunnel events and realizing ventilation control with predictive function. Background technique [0002] In the tunnel event detection and frequency conversion ventilation control system, the detection values ​​of carbon monoxide concentration and smoke concentration are the main basis for frequency conversion ventilation control. The detection of carbon monoxide concentration and smoke concentration is a large time-delay system and is easily affected by uncertain factors. In order to ensure the timely effectiveness of the tunnel ventilation control system, the relationship between tunnel events and ventilation control should be quickly analyzed. In the study of ventilation control quantification, the correlation between carbon monoxide concentration, smoke concentration and tunnel events is the premise ...

AI Technical Summary

Problems solved by technology

[0005] 1) The energy consumption is large, and the purpose of energy saving cannot be achieved

[0006] 2) Poor real-time and accuracy of ventilation control

[0007] Because tunnel frequency conversion ventilation control is a complex time-delay control process, and the existing control methods are mostly based on the behavior characteristics of the system, which belongs to post-event control, it is difficult to achieve complete real-time, accuracy and adaptability. sex

[0008] 3) It is not easy to establish an accurate mathematical model of the control object

However, because it is difficult to grasp the principle of the control object, and the established mathematical model of the control object is complex and changeable, it is difficult to solve the problem of time lag and accuracy by using the general method of establishing the mathematical model of the control object.

[0010] 4) It is difficult to obtain an optimal sensor data predictor

[0011] The existing sensor data prediction is to build a single complex prediction function within the range of dynamic system behavior, that is, to build a control model that adapts to the global scope, and such a model is difficult to obtain

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