Clustered intelligent control method for traffic tunnel devices

Abstract

The invention discloses a clustered intelligent control method for traffic tunnel devices. The clustered intelligent control method is characterized in that states of field controllers are collected, sorted, packaged and uploaded on the basis of receiving, parsing, preprocessing and issuing of control instructions of a central control layer; collected device information is filtered and is analyzed and processed according to a built-in clustered intelligent controller model, and instructions are outputted to a field controller layer after an optimized control instruction source is acquired, and are executed by the field controller layer; device attribute arrays are provided by the method, attributes of each device are arrayed, abstracted and packaged, and the devices are logically archived into different clusters according to difference of the attributes of the devices and are integrated into the various clusters instead of being particulate isolated individual devices. The clustered intelligent control method has the advantages that manual intervention can be reduced, the operational device cost and the maintenance cost of traffic tunnels can be reduced, and efforts are contributed for low-carbon life.

Description

technical field [0001] The invention relates to the technical field of a comprehensive monitoring system for traffic tunnels, in particular to a cluster-type intelligent control method for traffic tunnel equipment. Background technique [0002] Throughout the history of the development of the traffic tunnel comprehensive monitoring system, from the early local control of equipment, to the later central distributed discrete control, and now to the commonly used intelligent control layer software (configuration platform) + field controller (PLC) Objectively speaking, the integrated monitoring system developed to today has a relatively mature structure, which can basically meet the operation and maintenance requirements of general traffic tunnels, and the safety, stability and reliability of electrical equipment in traffic tunnels are also guaranteed. Higher guarantee, the personnel in the central control room can understand the real-time operation information of the entire tun...

AI Technical Summary

Problems solved by technology

[0005] First, too much manual intervention is required. Although the on-site controller has an automatic control program built in, it has great limitations and cannot meet all working conditions in the tunnel. Therefore, the operation of the electromechanical equipment in the tunnel is still dominated by manual control. Or the concept of fewer people on duty is only theoretical, which is not conducive to saving labor costs

[0006] Second, every time the equipment is started, the order is often fixed, resulting in too frequent start and stop of a single equipment, and the equipment failure information is not systematically sorted and archived, and reasonable equipment maintenance suggestions cannot be given, resulting in an increase in the probability of equipment wear and tear. Rising cost of maintenance

[0007] Third, the tunnel equipment has not achieved the optimal control, and the purpose of energy saving and emission reduction has not been achieved. For example, in the ventilation system, the adjustment of the environment in the tunnel can be achieved by activating a small amount of equipment, but in actual operation, more are activated. equipment, although the same purpose is achieved, the energy consumption has risen

[0008] Fourth, the concept of equipment group has not been formed, and the equipment with the same attribute is not considered as a whole. Although this approach is not harmful to the simple logic control requirements, if it is in complex environments and various working conditions In this case, the control logic of each individual device may be cumbersome, but the overall control efficiency is not high, which is also an important cause of the third problem

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