WO2016004775A1 - 一种基于云计算的轨道交通信号综合运维方法及系统 - Google Patents

一种基于云计算的轨道交通信号综合运维方法及系统 Download PDF

Info

Publication number
WO2016004775A1
WO2016004775A1 PCT/CN2015/075007 CN2015075007W WO2016004775A1 WO 2016004775 A1 WO2016004775 A1 WO 2016004775A1 CN 2015075007 W CN2015075007 W CN 2015075007W WO 2016004775 A1 WO2016004775 A1 WO 2016004775A1
Authority
WO
WIPO (PCT)
Prior art keywords
data
railway
station
index
real
Prior art date
Application number
PCT/CN2015/075007
Other languages
English (en)
French (fr)
Chinese (zh)
Inventor
鲍侠
Original Assignee
北京泰乐德信息技术有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 北京泰乐德信息技术有限公司 filed Critical 北京泰乐德信息技术有限公司
Publication of WO2016004775A1 publication Critical patent/WO2016004775A1/zh

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism

Definitions

  • the invention provides a railway signal comprehensive operation and maintenance method and system based on cloud computing, relating to railway signal data, railway communication data, railway knowledge data, system alarm data, machine learning, vehicle equipment, station equipment, central equipment, trackside equipment And other technical fields to solve the problems faced by the comprehensive operation and maintenance of railway signals.
  • CSM Signal Centralized Monitoring System
  • equipment maintenance machines and communication network management systems for ground signal equipment.
  • TJWX-I and TJWX-2000 and other continuously upgraded signal centralized monitoring CSM systems In order to improve the modern maintenance level of China's railway signal system equipment, since the 1990s, it has independently developed TJWX-I and TJWX-2000 and other continuously upgraded signal centralized monitoring CSM systems.
  • most stations use a signal centralized monitoring system to realize the real-time monitoring of the status of the station signal equipment, and provide the electrical department with the current status of the equipment and the accident analysis by monitoring and recording the main operational status of the signal equipment.
  • the basic basis has played an important role.
  • centralized monitoring CSM system is also widely deployed in urban rail centralized stations/vehicle sections, etc., for urban rail operation and maintenance.
  • vehicle signal equipment there is a DMS system for dynamic monitoring of the vehicle equipment.
  • high-speed railway-specific RBC system, etc. is also faced with the need to incorporate the signal centralized monitoring system, and also faces the need to improve its monitoring capabilities, operation and maintenance capabilities, and equipment self-diagnosis capabilities.
  • the railway system Faced with more and more communication data, signal data, and current network conditions, the railway system generates a large amount of various types of data, but does not make good use of this data. Most of the data is currently stored at each station, and the station simply stores the data, and when the fault occurs, view the relevant data to analyze the cause of the failure. This situation has led to the inability of various types of data generated by the railway to be effectively stored and utilized, and the analysis of faults still relies on manual experience analysis. In many cases, faults can be found in the event of an accident, which not only leads to the manual diagnosis of the railway. Technical problems such as large workload, fault monitoring and low diagnostic efficiency in signal system failures also increase the risk of driving. Therefore, it is an urgent need in the field of rail transit to improve the comprehensive management and operation capability of railway signals, to identify hidden dangers, to cure hidden dangers, to promote the repair of fault repair status, and to ensure safe driving and improve transportation capacity.
  • the invention provides a cloud computing based signal integrated operation and maintenance method and system.
  • the system includes three types of hardware: data acquisition machine, terminal and data center.
  • the software includes a data acquisition subsystem, a data storage subsystem, a data preprocessing subsystem, a data real-time analysis subsystem, an offline data mining subsystem, a presentation subsystem, and a data query system.
  • a cloud computing-based integrated operation and maintenance system for rail transit signals comprising:
  • a data center connected to the data collection machine, including a cloud security layer, a basic resource layer, a virtualization layer, a data storage layer, a computing engine layer, a component layer, and a Web cluster;
  • the cloud security layer passes data backup, data restoration, and access Control, guarantee the security of the system;
  • the basic resource layer is the hardware platform of the data center;
  • the virtualization layer realizes the virtualization of the hardware through the virtualization software, and shields the underlying hardware differences;
  • the data storage layer includes the distributed file system, the column database, Relational database, which is used to store unstructured, semi-structured, and structured data;
  • the computing engine layer uses cloud computing processing;
  • the component layer is a component that specifically handles various services; and
  • the Web cluster is a load-balanced service publishing layer.
  • the terminal connects the data collector and the data center to display data analysis results.
  • the data center, the data acquisition machine, and the terminal are separately deployed in the railway company and the railway bureau, and only the data acquisition machine and the terminal are deployed in the electric power station and the station.
  • Real-time data analysis components are deployed in stations, power stations, railway bureaus, and railway companies.
  • the real-time data analysis components of stations and power stations are deployed in data acquisition machines.
  • the real-time data analysis components of railway bureaus and railway companies are deployed in data. Inside the center.
  • the computing engine layer includes an offline data processing architecture MapReduce, a real-time distributed processing architecture spark, a data movement engine sqoop, and an underlying resource management system yarn, Yarn for allocating storage resources and computing resources according to tasks submitted by the user for processing the task.
  • MapReduce offline data processing architecture MapReduce
  • real-time distributed processing architecture spark a real-time distributed processing architecture spark
  • data movement engine sqoop a data movement engine sqoop
  • underlying resource management system yarn Yarn for allocating storage resources and computing resources according to tasks submitted by the user for processing the task.
  • the component layer includes a data management component, a data analysis component, a fault diagnosis component, and the like.
  • the first comprehensive signal operation and maintenance platform needs to solve the problem of data acquisition.
  • railway bureaus, electric power stations and stations used by railways data collectors are also distributed at these four levels. Collect different types of signal data and perform different processing on the data; deploy data centers, data acquisition machines and terminals in railway companies and railway bureaus respectively, and only deploy data acquisition machines and terminals in the electricity service stations and stations;
  • the electric power segment, the railway bureau, and the railway company all deploy real-time data analysis components.
  • the real-time data analysis components of the station and the electric service segment are deployed in the data acquisition machine, and the real-time data analysis components of the railway bureau and the railway company are deployed in the data center;
  • each railway internal signal system uses a 2M bandwidth wired network for data transmission.
  • the signal data collected per second at a single station.
  • an electric service segment has about tens to hundreds of stations; this makes it impossible for the station data to be completely transmitted to the electricity section; according to this situation, the monitoring data is stored locally by the collector.
  • Strategy only part of the data is uploaded to the electricity section and the railway bureau, railway company;
  • the data acquisition machine of the station collects the real-time data of the station, and uses the real-time data analysis component deployed locally to perform pre-processing, feature extraction and feature selection on the signal data, and then analyze the feature data in real time by using the analysis model to obtain the current signal system. Operation status, and will transmit corresponding data (including extracted partial features and analysis results, including raw data of each station) to the electricity segment;
  • the data acquisition machine of the electric service segment accepts the data transmitted from the internal stations, and analyzes the data of the different stations received by the real-time data analysis component deployed locally, and obtains the analysis result of the entire electric service segment, and analyzes The result is transmitted to the data acquisition machine of the railway bureau;
  • the data acquisition machine of the railway bureau receives the data from each electrical segment and transfers the data to the data center. Then, the data is processed and analyzed by the real-time data analysis component deployed in the data center, and the analysis results and some features are analyzed. Data acquisition machine transmitted to the railway company;
  • the data acquisition machine of the railway company receives the data sent by the railway bureau, and transfers the data to the data center, and analyzes and processes the real-time data analysis component of the data center to obtain the global analysis result of the railway company;
  • the method further includes a step 7), which indexes the data stored by the railway company, the railway bureau, the electric power station and the station, and quickly acquires various types of signal data of the relevant time period and position according to the index when the fault occurs, so as to Quickly analyze, locate and resolve faults.
  • the data collector can reuse existing machines, and only needs to install corresponding data storage components, data preprocessing components, real-time data analysis components, and data transmission components.
  • the data collection machine of the railway company and the railway bureau only needs to deploy the data transmission component, and does not need to complete the functions of data storage and processing.
  • the data storage of the data acquisition machine adopts a hierarchical storage strategy. When the bandwidth of the railway network is sufficient, the extracted feature data can be directly transmitted to the upper layer, so that the collection machine does not need to store the signal data locally.
  • a major innovation of the present invention is a hierarchical data distributed storage method, which is not a simple distributed storage, but a new hierarchical data storage architecture designed based on the network restriction of the railway, that is, the original data is stored separately.
  • the collected layer in addition to storing the data of this layer, also needs to store the metadata of the lower layer (the metadata here mainly refers to the index data, that is, the storage information of the lower layer real data, such as where some data is stored) It is used to quickly locate the specific location of the data, and the upper layer manages the underlying data through metadata.
  • the electricity segment includes two types of data, one is the data collected by the electricity segment itself, and the other is the metadata of each station in the pipe area, including the station number, equipment number, time interval, storage location, etc. Positioning data. This can be pushed up.
  • the data acquisition machine of the station in step 2) obtains the signal data through the signal station machine of the station, and the station machine first determines whether the transmission needs to be performed through the existing rules, thereby performing compression processing on the signal data. If the value of the voltage (or other signal value) does not exceed 20% (or other threshold), the data does not need to be transmitted if it is considered that the data has not changed.
  • the data acquisition machine establishes a socket connection with the signal station machine of the station, and the signal station machine of the station transmits the compressed data to the data acquisition machine, and the data acquisition machine uses the existing rules to restore the data.
  • the real-time data analysis component described in steps 2) to 5) mainly performs fault prediction based on the obtained feature data, and classifies the feature by using the classification model to determine whether a fault occurs.
  • the real-time data analysis is based on the historical signal data that has been marked for mining to obtain a classification model for various types of faults.
  • the data analysis algorithms used by the real-time data analysis component include support vector machine, Bayesian classifier, rough set, decision tree, neural network, etc.
  • the algorithm uses the algorithm to uniformly process the collected signal data in the data center to mine fault identification.
  • the model is provided for use in real-time analysis.
  • step 6) the terminal of the station is mainly used to display various analysis results inside the station, including real-time operating status, and various failure analysis results; the terminal of the electric service segment is mainly used to display the The overall operating state in the electricity section, when the station in the pipeline area fails, the terminal also needs to display the corresponding fault information; the terminal of the railway bureau is used to display the operating status of the railway bureau level, various fault alarm information, etc.
  • the terminal of the railway company is used to show the operating status of the entire railway company, and it needs to be quickly displayed and tracked when the station fails.
  • step 7 needs to first establish an index according to the stored data at each station, and then transfer the index file of the station to the electricity segment, and the electricity segment establishes the locally collected data and the received station index file.
  • the secondary index is then transmitted to the data center of the railway bureau.
  • the data center of the railway bureau establishes a three-level index on the various types of data received, and then transmits the index to the data center of the railway company, and the data center of the railway company receives the data.
  • Various types of data establish a four-level index to form a four-level index of the railway company, railway bureau, electricity section, and station.
  • the index can be established according to the railway bureau, the electricity section, the station, and the equipment. Further, by means of the established index file, specific signal data can be directly obtained according to the time and location information of the fault.
  • the invention unifies the comprehensive management of railway signal data, including data collection, data storage, data analysis and Fruit display.
  • the life cycle of the entire signal data is managed uniformly to form an organic whole, which improves the utilization of signal data.
  • the invention collects and processes various signal data dispersed in each electric power station and station through the data acquisition system, thereby changing the status quo of isolation of various signal data. It provides a good acquisition platform for further mining of signal data in the future.
  • the invention adapts to the four-level management mode of the railway company, the railway bureau, the electricity section and the station through the hierarchical deployment mode, and the hierarchical deployment mode is also applicable to the current network status of the railway, and the data is optimized.
  • the centralized approach greatly reduces the amount of data transferred to the electricity segment and the railway company.
  • the invention saves a large amount of labor cost by using the model to identify the fault, no need to manually observe the monitoring information and then perform fault identification and analysis; can improve the accuracy of the fault identification of the rail transit monitoring data, shorten the fault repair time, and greatly improve The efficiency of fault handling of rail transit improves the operation and maintenance capabilities.
  • the invention can realize real-time analysis of the signal data, improve the real-time performance of the data analysis, and the analysis result can be directly displayed locally.
  • the invention realizes the storage requirement for the increasing signal data through the hierarchical storage and indexing strategy, and realizes the rapid positioning of the fault through the four-layer index structure, and the relevant personnel can quickly obtain the information through the time, type, location and the like of the fault occurrence.
  • the signal data corresponding to the occurrence of the fault is retrieved. This allows technicians and managers to locate faults more quickly and directly, and propose solutions.
  • Figure 1 is a schematic diagram of the organization structure of the railway signal system.
  • FIG 2 is an overall architectural diagram of the integrated operation and maintenance system of the present invention.
  • FIG 3 is an overall deployment diagram of the data acquisition machine, data center, and terminal of the present invention.
  • FIG. 4 is a diagram of a data center architecture of the present invention.
  • Figure 5 is a schematic diagram of the operation of the integrated operation and maintenance system of the present invention in a digital railway "integrated" platform.
  • Figure 6 is a flow chart showing a 25 Hz phase sensitive track circuit for distinguishing between indoor and outdoor faults in the embodiment.
  • the integrated operation and maintenance scheme of the railway signal of the invention is used for solving the technical problems of the signal data dispersion processing, the large workload, the low efficiency, the high risk, and the difficulty of fault inquiry in the prior art.
  • the user objects of the present invention are mainly state-owned railways and domestic Large enterprise railways, such as urban rail transit.
  • the railway signal system is divided into four levels: the railway head office, the railway bureau, the electric power station and the station. The following describes the internal departments of the national railway signal system and their corresponding functions in conjunction with Figure 1.
  • the railway company's electrical department is in charge of the railway signal equipment of all roads in the country. It is the final gathering point of all monitoring data. It has the widest coverage, the most comprehensive types of equipment, and the largest amount of data for analysis.
  • the main tasks include: (1) Equipment angle: Keep track of the working status of all signal equipment of each road station; use the statistical report function to understand the failure rate, fault type, fault impact and other information of various signal equipment in actual application; Maintenance of signal equipment of each road station; through the statistical report function, compare the comprehensive performance of different manufacturers, different types, different types of signal equipment, provide data support and quantitative reference for equipment procurement, operation, maintenance, etc.; through data integration Analysis and discovery of weak links in signal equipment, providing targeted recommendations for equipment upgrades and introduction of new systems and new equipment; (2) Operation and maintenance perspective: mastering the operation and maintenance of railway bureaus, and arranging inspection priorities in a targeted manner, Focus on rectification and prevent problems before they happen.
  • the types of signal equipment of each road bureau are relatively uniform.
  • the main tasks include: (1) equipment angle: check the alarm information of each signal equipment; use the statistical report function to understand the failure rate of various signal equipment in practical applications, Information such as the type of fault and the impact of the fault; timely understand the fault handling situation; monitor the working status of the signal equipment in charge; when the fault occurs, comprehensively compare the alarm information and status monitoring information of the vehicle equipment and the ground equipment to determine the root cause of the fault.
  • equipment angle check the alarm information of each signal equipment
  • use the statistical report function to understand the failure rate of various signal equipment in practical applications, Information such as the type of fault and the impact of the fault; timely understand the fault handling situation; monitor the working status of the signal equipment in charge; when the fault occurs, comprehensively compare the alarm information and status monitoring information of the vehicle equipment and the ground equipment to determine the root cause of the fault.
  • Through statistical reports and data analysis functions compare the equipment of different manufacturers, and provide corresponding requirements and opinions to the equipment manufacturers
  • Operation and maintenance angle master the operation and maintenance of each electrical service
  • the station includes ordinary workshops, vehicle workshops, and transportation workshops, and some stations also include high-speed workshops. They are used to manage different monitoring devices, including in-station signal equipment and interval signal equipment, vehicle equipment, CTC, RBC, TDCS and other equipment.
  • the fault is detected and the cause of the fault is identified through the integrated monitoring system. Identify the location, equipment, and other information of the fault through various statistical reports, perform key monitoring on the location where the fault occurrence rate is high, and perform statistical analysis on the equipment that is prone to failure, providing a basis for equipment replacement and procurement.
  • the hierarchical data storage and processing method of the invention is very suitable for the classification characteristics of the national railway and the data analysis of each level. demand.
  • the invention adopts a four-level mode of a railway company, a railway bureau, an electric power section and a station, and respectively deploys a data center, a data acquisition machine and a terminal in a railway company and a railway bureau, and only deploys a data acquisition machine and a terminal in an electric power section and a station.
  • Real-time data analysis components are deployed at stations, power stations, railway bureaus, and railway companies.
  • the real-time data analysis components of stations and power stations are deployed in data acquisition machines.
  • the real-time data analysis components of railway bureaus and railway companies are deployed in data centers.
  • the railway Administration and the railway Corporation also need to deploy an offline data mining system in the data center to analyze and mine the models used in real-time data analysis.
  • the solution of the present invention mainly includes the following six parts:
  • Railway signals include many types, such as communication data, vehicle data, trackside data, station data, etc.
  • the resulting location also includes stations, electricity stations, railway bureau management centers, railway company centers, etc., so the data acquisition subsystem also needs It is deployed at the station, the electricity section, the railway bureau and the railway company signal center.
  • the data acquisition subsystem needs to receive and parse the signal data according to the specifications of the signal data.
  • the signal acquisition subsystem transmits the collected signal data to the data preprocessing subsystem to analyze and process the signal data.
  • the data preprocessing subsystem receives the signal data collected from the data acquisition subsystem, finds the signal data related to the fault according to different faults, and then performs preprocessing such as deduplication and denoising on the data to ensure the validity of the data.
  • the signal data the data is transformed into a space vector model, and feature extraction algorithms such as information gain algorithms are used to extract, de-duplicate and select features according to the needs, and find features useful for fault classification, and finally generate suitable for model training and real-time.
  • the eigenvector of the analysis is used to extract, de-duplicate and select features according to the needs, and find features useful for fault classification, and finally generate suitable for model training and real-time.
  • the collected data is first manually labeled, and various types of fault data are obtained. Through the training of these data, the corresponding classification model and parameters can be generated for the next step of signal data analysis.
  • the subsystem analyzes the historically monitored data by manual data, firstly pre-processes the data already marked, generates feature vectors with labeled categories, and then selects appropriate features and initial parameters to train the data, thereby Get the model of the fault classification.
  • the subsystem receives the real-time signal data processed by the data pre-processing subsystem, and uses the classification model obtained by the classification model mining subsystem to analyze and calculate the data to obtain the current operating state of the system. If there is no fault, the normal display will be performed. When the fault occurs, the relevant personnel should be reminded by warning.
  • the subsystem is a variety of display terminals, deployed in the signal center of the station, the electricity section, the railway bureau and the railway company, for real-time display of the operational status of the jurisdiction, timely reminding of alarm information, query signal data, etc., provided to the site Used by technicians and managers. It is convenient for relevant personnel to understand the current running status of the system more intuitively.
  • the results and scope of the signal data analysis for different levels are also different.
  • the station is real-time display of the real-time analysis of the station; the electricity section shows the jurisdiction Part of the analysis results of all stations and all fault information; the railway company station section shows the operating status and analysis results of all the electric power stations and stations in the company.
  • steps (2) ⁇ (4) because there is no data center deployed at the station and the electricity section, it is completed in the collection machine (ie real-time data analysis component); in the railway bureau and railway company Completed in the data center.
  • the railway bureau and the railway company can also directly deploy the data acquisition component on the central server without the need of a special data acquisition machine to realize the function of the data acquisition machine.
  • the railway signal system departments only query and analyze the signal data in the jurisdiction, so the data requirements have obvious locality.
  • the signal data storage is carried out in a hierarchical manner.
  • a hierarchical data indexing strategy is adopted. That is to say, different data index structures are used in stations, electric power stations, railway bureaus and railway companies to ensure the speed of data query.
  • the concept of the data block (Block) is introduced here, which has a fixed storage space of 64M, and the signal data storage devices of the entire network are uniformly numbered by the data center server of the railway company.
  • Deviceid the number of the monitoring equipment in the workshop
  • Blockid the data block number, this is the global unique number, the specific location of the data storage, can be directly mapped to the corresponding physical storage location through this id;
  • the signal data is at the beginning of the data block
  • Length the size of the signal data, which is also the storage length occupied by the data
  • the index data of the station is generated during data storage and stored in the local machine.
  • the station terminal performs data query, it first reads the index file into the local machine, and then reads the actual data according to the data block and the offset.
  • the electrical segment stores two kinds of data, which are the actual signal data collected and the index data.
  • Index data is also divided into two categories: index of station index data; index of local data.
  • the local data index is similar to the station data, except that the workid becomes the depotid representing the electricity segment, as shown in Table 2.
  • Table 3 is the data structure of the index of the station index data:
  • the number of the station is unique within the entire road;
  • the index adds a station number to the station index to identify the station.
  • An index record represents the specific location of the index file storage of a station work area, and the length of the index file.
  • the railway bureau is similar to the electricity section.
  • the stored data is divided into two categories: the data collected by the railway bureau itself and the index data.
  • the index data is divided into a local data index and an index of the electrical segment index in the pipe area.
  • the local data index data structure is shown in Table 4:
  • Officeid is the number of the railway bureau used to identify the unique code of the railway bureau.
  • the index data structure of the electrical segment index is shown in Table 5:
  • the index is added with an electrical segment number relative to the index of the electrical segment to identify the electrical segment.
  • An index record represents the specific location of the index file storage of an electrical segment, and the length of the index file.
  • the railway company is similar to the railway bureau.
  • the stored data is divided into two categories: the data collected by the railway company itself and the number of indexes. according to.
  • the index data is divided into a local data index and an index of each railway station index data.
  • the local data index data structure is shown in Table 6:
  • the index data structure of the railway bureau index is shown in Table 7:
  • the index adds a railway bureau number to the railway bureau's index to identify the railway bureau.
  • An index record represents the specific location of the index file storage of a railway bureau, and the length of the index file.
  • the invention realizes centralized management of railway signal data by using the integrated operation and maintenance platform, and realizes unified collection and centralized storage of various signal data of the railway.
  • the data preprocessing subsystem performs unified preprocessing on the collected monitoring data to extract useful features for data mining and real-time analysis.
  • the classification model mining subsystem analyzes and processes the labeled data, finds the appropriate classification model and parameters for the specific fault problem, and transmits the mining result to the real-time data analysis subsystem.
  • the data analysis subsystem uses the classification model to classify the real-time signal feature data after pre-processing, obtains the fault analysis result, and transmits the result to the result display subsystem, and the result display subsystem displays the corresponding analysis result according to the position where it is located. This includes the interior of the station, the interior of the electricity section and the operation of the entire railway company.
  • the specific process of the method is specifically illustrated by the following examples and examples:
  • the 2 is an overall architecture diagram of the system, which includes a data acquisition system (ie, a data acquisition machine), a data center, and a terminal.
  • the data center includes structured data storage, semi-structured data storage, unstructured data storage, and offline data processing. , real-time big data processing, statistical analysis, data mining, fault warning, query engine, push engine and other modules.
  • the terminal can be various types of receiving terminals, including PC computers, notebooks, pads, smart phones, and the like.
  • Figure 3 is a schematic diagram of the deployment of the system, deployed in four levels, namely the railway company, the railway bureau, the electricity section and the station.
  • the specific architecture of the data center refers to Figure 3. Because this data center needs to manage and process all kinds of signal data of all the electric power stations and stations in the entire railway company, it requires large storage and computing power.
  • the data acquisition machine is mainly used to receive data sent by various railway bureaus. To deploy the data acquisition subsystem and the data preprocessing subsystem, it is responsible for pre-processing the collected signal data and transmitting it to the data center; the terminal is connected with the data center to display the running status of the entire company, including real-time signal display and signal. Data query and fault alarm.
  • the railway bureau needs to deploy a small data center, data collector and terminal.
  • the data center of the railway bureau is used to receive and process the signal data uploaded by the internal electricity section. It needs to deploy components including distributed file system, parallel processing architecture, real-time data analysis, etc.
  • the data acquisition machine is mainly used to collect the various generated at the railway bureau level. Class signal data, and preprocess the data and transmit it to the data center.
  • the terminal is used to display, query and alert various types of signal data in the railway bureau.
  • the electricity section and the station do not need to deploy a data center, only data collectors and terminals are required. In addition to the functions of signal acquisition, these collectors also have the ability of data preprocessing, real-time data analysis, data storage and data transmission. After the data acquisition machine collects the data, the data is pre-processed and analyzed in real time, and the analysis result is displayed through the local terminal, and part of the analysis result and signal data are transmitted to the data center of the railway bureau.
  • the data acquisition system of the electrical segment receives signal data and analysis results transmitted from various stations in the pipe area.
  • the terminal is used to display and query various signal data and analysis results.
  • the station only needs to deploy the data acquisition machine and terminal as the electricity service segment. Its function is similar, except that the data acquisition machine of the station only needs to collect the signal data of the station, and pre-process and analyze the data in real time, and according to the demand. Part of the data and analysis results are transmitted to the collector of the electricity segment.
  • the terminal is mainly used to display various signal data and real-time analysis of data in the station.
  • the cloud security layer ensures system security through data backup, data restoration and access control.
  • the base resource layer builds the hardware platform of the data center, which can reuse existing server devices.
  • the virtualization layer virtualizes the hardware through virtualization software, shields the underlying hardware differences, and forms a highly available, scalable, and scalable cluster.
  • the data storage layer is composed of a distributed file system, a columnar database, and a relational database for storing unstructured, semi-structured, and structured data, respectively.
  • the computing engine layer includes the offline data processing architecture MapReduce, the real-time distributed processing architecture spark, the data movement engine sqoop and the underlying resource management system yarn. Yarn is used to allocate storage resources and computing resources for processing tasks based on tasks submitted by users.
  • the component layer is a component that specifically handles various services, including data management components, data analysis components, and fault diagnosis components. The results of various data collection and analysis need to be published through the architecture of B/S or C/S.
  • the Web cluster is a service publishing layer based on load balancing.
  • the cloud computing-based integrated operation and maintenance system of the present invention can be integrated with other processing platforms.
  • a digital railway integration platform is built using "general components” and “business plug-ins”.
  • the "data center and big data management & data mining analysis platform” can be realized based on the invention of a common component, which is the core of data acquisition, storage and data processing, and with the equipment comprehensive monitoring platform, transportation platform, operation and maintenance. Platforms, scheduling and collaboration platforms are integrated to form a digital railway integration platform.
  • Figure 6 is a flow chart of a 25 Hz phase sensitive track circuit that distinguishes between indoor and outdoor faults.
  • the analysis of the cause of track circuit failure is a classification problem, which is very suitable for data mining.
  • the signal acquisition associated with it comes from the station equipment.
  • the first is the model training phase, where the existing data is manually labeled to identify those that are faulty. In this way, a training set is formed.
  • the data mining subsystem is used to train these data sets, and the Bayesian classifier is selected as the training model to obtain corresponding model parameters.
  • These classification models are then deployed in the station's data acquisition machine.
  • the data acquisition machine first receives the monitoring data from each monitoring device and then preprocesses the data to obtain the feature vector for analysis.
  • the real-time data analysis component uses the obtained classification model to analyze and calculate the real-time data to obtain whether the system is currently faulty.
  • the results of the failure analysis are then presented through the display system, and the relevant analysis results and features are transmitted to the electricity section, the railway bureau, and the railway center for analysis at a higher level.
  • Junction box receiving terminal voltage Open the outdoor side voltage of the cable terminal Send terminal voltage Data acquisition timestamp 25.00 25.00 25.00 521365 24.00 24.00 25.00 521365 27.00 27.00 27.00 521365 0.00 0.00 0.00 521365 ... ... ... ... ...
  • the first column of numbers represents the type of fault:
  • ⁇ 1 indicates that the fault is indoors
  • ⁇ 2 indicates that the fault is outdoors

Landscapes

  • Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Economics (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Primary Health Care (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Train Traffic Observation, Control, And Security (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
PCT/CN2015/075007 2014-07-07 2015-03-25 一种基于云计算的轨道交通信号综合运维方法及系统 WO2016004775A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201410321145.1A CN104077552B (zh) 2014-07-07 2014-07-07 一种基于云计算的轨道交通信号综合运维方法及系统
CN201410321145.1 2014-07-07

Publications (1)

Publication Number Publication Date
WO2016004775A1 true WO2016004775A1 (zh) 2016-01-14

Family

ID=51598802

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2015/075007 WO2016004775A1 (zh) 2014-07-07 2015-03-25 一种基于云计算的轨道交通信号综合运维方法及系统

Country Status (2)

Country Link
CN (1) CN104077552B (ja)
WO (1) WO2016004775A1 (ja)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018090605A1 (zh) * 2016-11-18 2018-05-24 中兴通讯股份有限公司 一种数据中心的管理方法及系统

Families Citing this family (24)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10282676B2 (en) * 2014-10-06 2019-05-07 Fisher-Rosemount Systems, Inc. Automatic signal processing-based learning in a process plant
CN104077552B (zh) * 2014-07-07 2017-08-25 北京泰乐德信息技术有限公司 一种基于云计算的轨道交通信号综合运维方法及系统
CN104504956A (zh) * 2014-12-22 2015-04-08 中国神华能源股份有限公司 一种基于虚拟现实的铁路电务训练系统
CN104890702A (zh) * 2015-05-27 2015-09-09 中国铁路总公司 一种铁路信号车地综合分析监测系统
CN104908783B (zh) * 2015-05-27 2017-01-18 中国铁路总公司 铁路电务综合监测维护系统体系架构
CN105574593B (zh) * 2015-12-18 2020-05-05 中南大学 基于云计算和大数据的轨道状态静态检控系统及方法
CN105676842B (zh) * 2016-03-14 2019-06-18 中国铁路总公司 一种高铁列控车载设备故障诊断方法
CN106250429A (zh) * 2016-07-26 2016-12-21 浪潮软件股份有限公司 一种基于sqoop的数据抽取方法
CN106341467B (zh) * 2016-08-30 2019-11-29 国网江苏省电力公司电力科学研究院 基于大数据并行计算的用电信息采集设备状态分析方法
CN108268023B (zh) * 2016-12-30 2022-04-26 上海嘉成轨道交通安全保障系统股份公司 一种轨道交通站台门远程故障诊断方法及系统
CN107316158A (zh) * 2017-07-05 2017-11-03 云能服(北京)科技有限公司 一种多维作业数据处理方法和装置
CN110254471A (zh) * 2018-03-12 2019-09-20 上海铁鑫电气科技有限公司 一种铁路信号轨道电路引接线断线实时监测设备
CN109377090A (zh) * 2018-11-22 2019-02-22 湖南铁路科技职业技术学院 一种基于云服务的铁路运输数据通讯支撑平台
CN109889601A (zh) * 2019-03-12 2019-06-14 湖南铁路科技职业技术学院 铁路信号微机监测系统
CN110096383B (zh) * 2019-04-10 2022-08-30 卡斯柯信号有限公司 一种信号设备维护信息自动分类方法
CN110032090A (zh) * 2019-04-15 2019-07-19 深圳众维轨道交通科技发展有限公司 一种基于bim+ai实现有轨电车远程监控和授权的方法
CN110182244A (zh) * 2019-04-23 2019-08-30 深圳众维轨道交通科技发展有限公司 一种基于云计算和ai智能的有轨电车云平台
CN110329319B (zh) * 2019-06-28 2021-09-03 卡斯柯信号有限公司 一种面向智慧城轨的全自动运行系统
CN110481605B (zh) * 2019-08-21 2022-02-08 沈阳风驰软件股份有限公司 一种铁路运输生产管控系统
CN111106968A (zh) * 2019-12-31 2020-05-05 国网山西省电力公司信息通信分公司 一种构建信息通信智能调度指挥沙盘的方法
CN111332340A (zh) * 2020-03-06 2020-06-26 东莞理工学院 一种轨道交通监测数据的存储和处理方法及系统
CN111292218A (zh) * 2020-03-10 2020-06-16 上海申铁信息工程有限公司 一种铁路车站设备智能化监控系统的构建方法与装置
CN111369022A (zh) * 2020-03-10 2020-07-03 上海申铁信息工程有限公司 一种铁路车站运维监控平台与装置
CN115374101A (zh) * 2021-05-21 2022-11-22 交控科技股份有限公司 轨道交通站段级数据管理系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012119879A1 (de) * 2011-03-07 2012-09-13 Siemens Aktiengesellschaft Eisenbahnleitsystem
CN103264717A (zh) * 2013-05-21 2013-08-28 北京泰乐德信息技术有限公司 一种轨道交通综合监控调度协同与运维信息化系统
CN103338261A (zh) * 2013-07-04 2013-10-02 北京泰乐德信息技术有限公司 一种轨道交通监测数据的存储和处理方法及系统
CN103391185A (zh) * 2013-08-12 2013-11-13 北京泰乐德信息技术有限公司 一种轨道交通监测数据的云安全存储和处理方法及系统
CN104077552A (zh) * 2014-07-07 2014-10-01 北京泰乐德信息技术有限公司 一种基于云计算的轨道交通信号综合运维方法及系统

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101917237B (zh) * 2010-07-27 2013-02-20 北京全路通信信号研究设计院有限公司 一种铁路信号监测方法和系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012119879A1 (de) * 2011-03-07 2012-09-13 Siemens Aktiengesellschaft Eisenbahnleitsystem
CN103264717A (zh) * 2013-05-21 2013-08-28 北京泰乐德信息技术有限公司 一种轨道交通综合监控调度协同与运维信息化系统
CN103338261A (zh) * 2013-07-04 2013-10-02 北京泰乐德信息技术有限公司 一种轨道交通监测数据的存储和处理方法及系统
CN103391185A (zh) * 2013-08-12 2013-11-13 北京泰乐德信息技术有限公司 一种轨道交通监测数据的云安全存储和处理方法及系统
CN104077552A (zh) * 2014-07-07 2014-10-01 北京泰乐德信息技术有限公司 一种基于云计算的轨道交通信号综合运维方法及系统

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2018090605A1 (zh) * 2016-11-18 2018-05-24 中兴通讯股份有限公司 一种数据中心的管理方法及系统
CN108075917A (zh) * 2016-11-18 2018-05-25 中兴通讯股份有限公司 一种数据中心的管理方法及系统

Also Published As

Publication number Publication date
CN104077552A (zh) 2014-10-01
CN104077552B (zh) 2017-08-25

Similar Documents

Publication Publication Date Title
WO2016004775A1 (zh) 一种基于云计算的轨道交通信号综合运维方法及系统
CN110059631B (zh) 接触网非接触式监测缺陷识别方法
CN108591104B (zh) 一种基于云平台的风机故障预测与健康管理系统、方法
US20200350984A1 (en) Geographic information system integrated satellite management system
CN104503399B (zh) 一种集团级风电机组状态监测及故障诊断平台
CN110264015A (zh) 反窃电稽查监控方法及平台
CN105574593B (zh) 基于云计算和大数据的轨道状态静态检控系统及方法
CN110674861A (zh) 一种输变电巡检图像智能分析方法及装置
CN112948457A (zh) 客运索道检测监测与健康诊断系统、方法、介质、设备
CN103699698A (zh) 一种基于改进贝叶斯的轨道交通故障识别方法及系统
WO2019196869A1 (zh) 一种确定巡检基站列表的方法以及巡检装置
CN106779096B (zh) 配电网报修态势主动预警系统
CN103745229A (zh) 一种基于svm的轨道交通故障诊断方法及系统
CN107241397A (zh) 一种基于云平台的多级列车调度指挥系统
CN104777813A (zh) 综合气象观测运行监控系统及其监控方法
CN108860223B (zh) 一种数据处理系统及方法
CN108446396B (zh) 一种基于改进型cim模型的电力数据处理方法
CN107918830A (zh) 一种基于大数据技术的配电网运行状态评估系统及方法
CN107609987A (zh) 一种面向设备主人的智能变电运维系统
CN111753034A (zh) 一种一站式地理大数据平台
CN112278012B (zh) 一种动车组轮对管理系统
CN114066429A (zh) 一种业务工单处理方法及系统
CN108258802B (zh) 一种配电网中配电设备的运行状况的监测方法和装置
CN114239874A (zh) 一种基于城市能源地图的电网数据处理系统及方法
CN118378868A (zh) 基于一网统管的业务多级联动指挥调度管理系统及方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15819040

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15819040

Country of ref document: EP

Kind code of ref document: A1