| China has built the world’s largest high-speed railway network and advanced railway network.By the end of 2022,China’s railway operating mileage reached155,000 kilometers,of which the operating mileage of high-speed railway has reached42,000 kilometers.As the core node of China’s railway network,passenger stations carry important transportation functions.With the rapid development of high-speed railways,their importance has become increasingly prominent.Passenger stations mainly rely on equipment to provide convenient and efficient travel services for passengers.The normal operation and health management of equipment are particularly important for the service quality and operational safety of passenger stations,and are also important indicators to measure the intelligent level of passenger stations.The numerous types,high level of self-service,and complex operating conditions of passenger station equipment make the demand for equipment control increasingly urgent.However,due to the limitations of management mode and technical level,the issues of health management and energy waste have not been well addressed,resulting in a high failure rate of passenger station equipment and excessive operating costs,which brings great difficulties and challenges to the intelligent management of passenger stations.In recent years,with the rapid development of new generation information technology,combined with the strategic requirements of "Carbon Neutrality and Carbon Peak",the intelligent and refined management of passenger station equipment has become the main development trend.Faced with the complex operating environment of passenger stations,this article conducts in-depth research on equipment health management and intelligent control,further improving the management level of passenger station equipment,ensuring the safety and service quality of passenger station operation.Limited by the structure and focus of the article,this article focuses on key issues such as equipment status detection,health assessment,fault maintenance,and energy conservation control,mainly including the following aspects:(1)The overall architecture of the health management and intelligent management and control application of the guest station.Based on the overall blueprint of the smart guest station,according to the production organization of the smart guest station "1+4+N" and the green energy-saving business sector,the device control requirements are required.The status monitoring,health assessment,fault diagnosis and energy saving management and control of tickets such as tickets,electromechanical and other equipment can achieve the goal of changing from planned maintenance to state maintenance,and achieve the goal of safe and stable operation and energy-saving reduction of the customer station equipment.(2)Abnormal state detection method of escalator in passenger station based on video analysis.Escalator is an important channel for passengers to enter and exit the station,and its abnormal state can easily lead to safety accidents.At present,the monitoring and detection of the operation status of escalators adopts self-inspection system and manual inspection,and abnormal states such as step defects,step deformation,and foreign bodies stuck are difficult to find in time,and there are hidden dangers in long-term operation.Aiming at the problem that there are few samples of abnormal running status of escalator,a small sample target detection method based on data enhancement is proposed.Firstly,a data amplification method based on series-parallel fusion connection is designed to enhance the generalization performance of the detection method.Secondly,the target detection network based on Faster R-CNN+FPN is established to improve the feature extraction capability of small-scale targets.Then,the data enhancement strategy based on semantic feature information is optimized to extend the image semantic information on the depth feature.Finally,through verification on the open data set and the escalator defect data set of passenger stations,the small target detection accuracy and generalization ability of the proposed method are higher than other models,effectively identifying the abnormal state of the escalator steps,and providing technical support for ensuring the safety of passengers.(3)Health status assessment method of passenger station equipment based on digital-analog linkage.In the traditional management mode of "manual maintenance and fault repair",the staff cannot accurately grasp the operating status of the station equipment.Once the equipment fails,the equipment is not maintained in time,which will affect the normal passenger transportation organization of the station and increase the safety risk.Based on RAMS concept,this paper constructs a multi-level evaluation index system of equipment health status.On this basis,a health status assessment model of passenger station equipment based on digital-analog linkage was proposed.Historical operating data and experience knowledge were used to initialize and optimize model parameters,evaluate the current operating health status of equipment,determine its corresponding health level,grasp the global health status of passenger station equipment in real time,and then formulate differentiated maintenance strategies.The experimental results show that this method can grasp the health status of equipment in real time,provide scientific basis for staff to refine equipment maintenance,reduce the failure rate of passenger station equipment,and ensure convenient and efficient travel services for passengers.(4)Construction and application of passenger station equipment fault knowledge graph.The maintenance of equipment faults in passenger stations usually relies on the experience of maintenance personnel themselves,and historical maintenance data is not fully explored,making it impossible to determine the causal relationships between diverse,fuzzy,and accidental faults.There is a lack of systematic analysis of the causes of faults,resulting in long downtime for equipment maintenance and affecting the normal and safe operation of passenger stations.This paper proposes an automatic construction method of the knowledge graph of passenger station equipment failure.Combining the characteristics of passenger station equipment failure business,the ontology and data model are defined in advance,the mapping relationship and rules between ontology elements are established,and the structured and unstructured equipment failure information is automatically transformed into knowledge representation and reasoning forms to build the knowledge graph of passenger station equipment failure,Form a chain of equipment failures.Build an intelligent question answering framework based on Trans E algorithm inference,accurately locate the cause of equipment faults,provide customized maintenance and disposal plans for maintenance personnel,achieve automatic diagnosis of equipment faults and auxiliary decision-making for maintenance and disposal,and effectively improve the efficiency of fault disposal.(5)Energy-saving control method of passenger station lighting based on train arrival and departure and environmental illumination information.The energy consumption of the passenger station is huge,of which lighting accounts for about 15%.At present,the passenger station lighting has not comprehensively considered the train arrival and departure,external environment,passenger flow and other factors,using a single static rule control mode,resulting in energy waste.Taking passenger station platform area lighting as the research object,an energy-saving control strategy based on meta-heuristic algorithm to optimize fuzzy control rules is proposed.Considering train arrival and departure information,platform passenger flow and other factors,the lighting control plan of platform area based on train arrival and departure information is developed.Based on the design of station area and the illumination value of regional environment,the energy-saving control model of energy consuming equipment is established,and the energy-saving control rule and corresponding control mode are optimized by using the meta-heuristic algorithm.The experimental results show that under the premise of meeting the standard illuminance value of the passenger station area,the energy consumption of the lighting energy-saving control strategy proposed in this paper is reduced by 65.69% and 38.58% on average compared with the platform long brightness and the existing control mode,which greatly reduces the energy consumption of the passenger station platform lighting. |
