WO2019114288A1 - 车站终端故障风险预警方法、装置、终端及存储介质 - Google Patents

车站终端故障风险预警方法、装置、终端及存储介质 Download PDF

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Publication number
WO2019114288A1
WO2019114288A1 PCT/CN2018/099001 CN2018099001W WO2019114288A1 WO 2019114288 A1 WO2019114288 A1 WO 2019114288A1 CN 2018099001 W CN2018099001 W CN 2018099001W WO 2019114288 A1 WO2019114288 A1 WO 2019114288A1
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WIPO (PCT)
Prior art keywords
fault
station
station terminal
parameter value
module
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PCT/CN2018/099001
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English (en)
French (fr)
Inventor
曾庆宁
张应钊
宋维
萧仪章
卢经伟
Original Assignee
广州广电运通金融电子股份有限公司
广州广电运通智能科技有限公司
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Publication of WO2019114288A1 publication Critical patent/WO2019114288A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data

Definitions

  • the invention relates to the field of station terminal fault monitoring, in particular to a station terminal fault risk warning method, device, terminal and storage medium.
  • the existing automatic ticket checking system is divided into five layers, including a central clearing system, a line computer, a station computer, a station terminal, and a ticket card.
  • the station terminal is installed in the station hall, and provides self-service ticket checking service directly to the passengers.
  • the station terminal uploads its own status information to the station computer in real time and uploads it step by step, and finally arrives at the central clearing system to monitor the status of each hardware module in the station terminal in real time.
  • the staff can notify the maintenance personnel to attend the scene according to the fault message on the station computer, line computer or central clearing system.
  • the existing fault feedback mechanism is limited to the current fault, and has certain limitations.
  • the embodiment of the invention provides a method, a device, a terminal and a storage medium for warning of a station terminal failure risk, which can effectively solve the technical problem that the prior art cannot warn of a potentially high-risk fault of a station terminal.
  • An embodiment of the present invention provides a method for early warning of a station terminal failure risk, including:
  • the fault information is calculated by a preset algorithm to obtain a current fault risk state of the station terminal;
  • the fault risk state includes a fault type and a probability of occurrence of the fault type;
  • the failure warning threshold is negatively correlated with the passenger flow of the station where the station terminal is located within a predetermined range.
  • calculating according to the pre-calculated fault evolution rule of the station terminal, calculating a remaining time of the fault type occurring under a preset probability; wherein the fault evolution law is based on a fault type in the fault feature knowledge base and Corresponding control health parameter fitting calculations;
  • detecting the currently available maintenance resource information of the station extracting a historical maintenance record of the station; wherein the station is a station where the station terminal is located, and/or a plurality of stations connected to the station;
  • the generated maintenance decision information is output.
  • the maintenance decision information includes at least one of adding manual service, implementing passenger flow restriction, and dispatching maintenance workers and maintenance methods.
  • the fault type of the fault that occurs in the station terminal and its corresponding health parameter value are added to the fault feature knowledge base.
  • the health parameter value comprises one or more of the following:
  • An operating parameter value characterizing the daily loss of each module of the station terminal is
  • the aging parameter value is a ratio of a current running time of each module of the station terminal to a design running time or a ratio of a current transaction number of each module of the station terminal to a design transaction number; and/or the working parameter The value is the number of daily transactions of each module of the station terminal.
  • the station terminal fault risk early warning method disclosed in the embodiment of the present invention extracts the reference health parameter value of the preset threshold range of the current health parameter value included in the pre-established fault feature knowledge base and The fault information corresponding to each of the health parameter values is compared, and each of the extracted health parameter values and the corresponding fault information is calculated by using a preset algorithm to obtain a current fault risk status of the station terminal.
  • the fault risk status includes a fault type and a probability of occurrence of the fault type. When the probability of occurrence of the fault type reaches a preset early warning threshold, an early warning signal is issued, which realizes an early warning of a potentially high fault risk of the station terminal, and improves the timeliness of fault processing.
  • Another embodiment of the present invention provides a station terminal failure risk warning device, including:
  • a data acquisition module configured to acquire current running data of the station terminal, where the current running data includes a current health parameter value
  • a fault risk calculation module configured to extract a comparison health parameter value of the preset threshold range including the current health parameter value in the pre-established fault feature knowledge base, and fault information corresponding to the control health parameter value, and extract the extracted fault information
  • the control health parameter value and the corresponding fault information are calculated by using a preset algorithm to obtain a current fault risk state of the station terminal;
  • the fault risk state includes a fault type and a probability of occurrence of the fault type;
  • the fault risk warning module is configured to issue a fault early warning signal when the probability that the fault type occurs reaches a preset fault early warning threshold.
  • the station terminal failure risk warning device further includes:
  • a fault time calculation module configured to calculate, according to a pre-calculated fault evolution rule of the station terminal, a remaining time that occurs when the fault type occurs under a preset probability; wherein the fault evolution law is according to the fault feature knowledge base The type of fault and the corresponding control health parameter are calculated and fitted;
  • the fault time warning module is configured to issue a time warning signal when the remaining time reaches a preset time warning threshold.
  • the station terminal failure risk warning device further includes:
  • a maintenance resource detecting module configured to detect maintenance resource information currently available at the station, and extract a historical maintenance record of the station; wherein the station is a station where the station terminal is located, and/or a plurality of stations connected to the station ;
  • a maintenance decision generating module configured to generate maintenance decision information according to the fault early warning signal, the fault type, the time warning signal, and the historical maintenance record, in combination with the currently available maintenance resource information of the station;
  • a maintenance decision notification module is configured to output the generated maintenance decision information.
  • the station terminal failure risk warning device further includes:
  • the knowledge base update module is configured to add the fault type of the fault that occurs in the station terminal and the corresponding health parameter value to the fault feature knowledge base when receiving the signal that the station terminal is faulty.
  • the station terminal failure risk early warning device disclosed in the embodiment of the present invention acquires current operation data of the station terminal through the data acquisition module; and calculates the current fault risk status of the station terminal by using the fault risk calculation module,
  • the fault risk status includes the fault type and the probability of occurrence of the fault type.
  • the fault risk warning module issues an early warning signal to the fault risk that reaches the preset warning threshold, and realizes an early warning prompt for the potential high fault risk of the station terminal. purpose.
  • Another embodiment of the present invention provides a terminal including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the computer program to implement the above The station terminal failure risk early warning method described in the embodiment of the invention.
  • Another embodiment of the present invention provides a storage medium, where the storage medium includes a stored computer program, wherein when the computer program is running, controlling the device where the storage medium is located to perform the station terminal failure described in the above embodiment of the invention Risk warning method.
  • FIG. 1 is a schematic flow chart of a method for early warning of station terminal failure risk according to a first embodiment of the present invention.
  • FIG. 2 is a schematic flow chart of a method for warning of station terminal failure risk according to a second embodiment of the present invention.
  • FIG. 3 is a schematic flow chart of a method for warning of station terminal failure risk according to a third embodiment of the present invention.
  • FIG. 4 is a schematic flow chart of a method for warning of station terminal failure risk according to a fourth embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of a station terminal failure risk early warning device according to a fifth embodiment of the present invention.
  • FIG. 1 is a schematic flowchart of a station terminal failure risk early warning method according to a first embodiment of the present invention, including:
  • the current running data of the station terminal can be automatically obtained by the control software of the station terminal, or the station terminal fault risk early warning device actively issues an inquiry command to drive the control software of the station terminal to detect and acquire, and the invention is implemented.
  • the examples are not specifically limited.
  • the health parameter value is a series of parameter values related to the working life of the station terminal.
  • the health parameter value may include characterizing each module of the station terminal.
  • the aging parameter value of the aging degree and the operating parameter value characterizing the daily loss of each module of the station terminal.
  • the aging parameter value refers to a ratio of a current running time of each module of the station terminal to a design running time or a ratio of a current transaction number of each module of the station terminal to a design transaction number;
  • the working parameter value is The number of daily transactions of each module of the station terminal. Taking the aging parameter value as an example, assuming that the number of design transactions of the banknote module is 1 million times of banknotes in and out, and the banknote entry and exit process has been performed 500 times, the aging parameter value of the banknote module is 50%.
  • the current running data of the station terminal may further include register data, transaction data, log files, and the like of each module.
  • the fault feature knowledge base is based on the station monitoring data of the station terminal and the station maintenance record, and through the massive analysis and core content extraction, constructs a set of experience that can provide fault warning and decision guidance basis.
  • the operation monitoring data includes each module status, register data, transaction data, and operation log of the station terminal, but is not limited thereto.
  • the fault feature knowledge base includes historical fault information of all station terminals of the station and corresponding health parameter values and maintenance record information; wherein the station is a station where the station terminal is located, and/or A plurality of stations in which the station is networked; for example, in the present embodiment, the station may be a station on the entire route in which the vehicle operates or a station on the entire urban rail transit route.
  • the reference health parameter value of the preset threshold range including the current health parameter value in the pre-established fault feature knowledge base and the fault information corresponding to each of the control health parameter values are extracted, and the fault information is extracted.
  • Each of the comparison health parameter values and corresponding fault information is calculated by a preset algorithm to obtain a fault risk distribution list.
  • the fault risk distribution list includes fault information corresponding to the preset health value of the preset threshold range of the current health parameter value, where the fault information includes the number of faults, the fault type, and the probability of each fault type.
  • the current fault risk status of the station terminal is obtained by the fault risk distribution list, and the fault risk status is the probability of each fault type included in the fault information.
  • the preset threshold range is ⁇ 10%.
  • preset threshold range is not fixed, and may be appropriately adjusted according to different health parameter values and the current operating environment, and is not specifically limited herein.
  • the preset threshold range of setting the aging parameter value is ⁇ 10%, and the preset threshold range corresponding to the daily transaction number is 15%; and the current aging parameter value of the station terminal is 30%, If the number of daily transactions is 10,000/day, the fault information in the fault knowledge base is extracted from 27% to 33%, and the number of daily transactions is 0.85 to 11500/day, and a fault risk distribution list is generated.
  • the number of faults of the fault information in the fault risk distribution list is 1000
  • the fault types of the 1000 fault information are a billing module failure failure, a banknote module deposit failure fault, and a coin module zero failure failure.
  • 700 are the failure of the banknote module to reject the coin
  • 200 are the failure of the banknote module
  • 100 are the failure of the coin module to change. Therefore, according to the preset algorithm, under the health parameter value of the aging parameter value of 30% and the daily transaction number of 10,000/day, the current fault risk state of the station terminal, that is, the banknote module default failure occurs.
  • the probability of failure is 70%
  • the probability of failure of banknote module deposit failure is 20%
  • the probability of failure of coin module change is 10%.
  • the fault warning threshold may also be set according to the operating environment of the station.
  • the fault warning threshold may be negatively correlated with the passenger flow of the station where the station terminal is located within a predetermined range, that is, When the traffic volume is large, the value of the fault warning threshold is relatively low, and when the traffic volume is small, the value of the corresponding fault warning threshold is relatively large; for example, the predetermined range is 80. %-90%, according to the operating environment of the station, the failure warning threshold may be set to 80%, or may be set to 90%, which is not specifically limited in the embodiment of the present invention.
  • the station terminal failure risk early warning method extracts a comparison health parameter value of a preset threshold range of the current health parameter value included in the pre-established fault feature knowledge base, and each Calculating the current fault risk status of the station terminal according to the fault information corresponding to the health parameter value, where the fault risk status includes a fault type and a probability of occurrence of the fault type, when the probability of the fault type occurs An early warning signal is issued when the preset warning threshold is reached.
  • the station terminal failure risk early warning method achieves the purpose of early warning of potential high-risk failure risks.
  • the second embodiment of the present invention adds the following steps to the first embodiment:
  • the remaining time of the fault type occurring under the preset probability may be predicted, so that the maintenance plan is planned in advance, and the timeliness of the fault processing is improved.
  • the preset probability is greater than or equal to 90%.
  • the third embodiment of the present invention adds the following steps to the first embodiment:
  • the station may be a station on the entire route in which the vehicle is running or a station on the entire urban rail transit route.
  • the maintenance resource information includes, but is not limited to, the number of spare parts of each module of the station terminal of all stations; the maintenance decision information includes adding manual service, implementing passenger flow restriction, and dispatching maintenance workers and maintenance methods. At least one kind of information.
  • the probability of failure of the current banknote module of the station terminal is 80%, but the number of spare parts of the banknote module of the station where the station terminal is located is 0, and the number of spare parts of the banknote module of the adjacent station is 2,
  • the generated maintenance decision information is output, which specifically includes sending the maintenance decision information to the maintenance personnel or the fault risk monitoring platform, which is not specifically limited in the embodiment of the present invention.
  • the fault warning signal, the fault type, the time warning signal, and the maintenance record combined with the current available maintenance resources of the station, generate maintenance decision information and send it to the maintenance personnel or the fault risk monitoring platform, so that the maintenance personnel The fault can be processed quickly and reasonably according to the maintenance decision information, which greatly improves the efficiency of fault handling.
  • the fourth embodiment of the present invention adds the following steps to the first embodiment:
  • the signal that the station terminal is faulty may be acquired by the method of the first embodiment (for example, when the probability of occurrence of the fault type reaches 100%), or may be detected by other means, where The manner of acquiring the signal at which the station terminal is faulty is not specifically limited.
  • FIG. 5 is a schematic structural diagram of a station terminal failure risk early warning device 500 according to a fifth embodiment of the present invention, including:
  • the data acquisition module 510 is configured to acquire current running data of the station terminal, where the current running data includes a current health parameter value;
  • the fault risk calculation module 520 is configured to extract a comparison health parameter value of the preset threshold range including the current health parameter value in the pre-established fault feature knowledge base, and fault information corresponding to the control health parameter value, and the extracted fault information
  • the control health parameter value and the corresponding fault information are calculated by using a preset algorithm to obtain a current fault risk state of the station terminal;
  • the fault risk state includes a fault type and a probability of occurrence of the fault type;
  • the fault risk warning module 530 is configured to issue a fault early warning signal when the probability that the fault type occurs reaches a preset fault early warning threshold.
  • the health parameter value is a series of parameter values related to the working life of the station terminal.
  • the health parameter value includes aging of each module of the station terminal.
  • the degree of aging parameter value and the value of the operating parameter characterizing the daily loss of each module of the station terminal.
  • the aging parameter value refers to a ratio of a current running time of each module of the station terminal to a design running time or a ratio of a current transaction number of each module of the station terminal to a design transaction number;
  • the working parameter value is The number of daily transactions of each module of the station terminal. Taking the aging parameter value as an example, assuming that the number of design transactions of the banknote module is 1 million times of banknotes in and out, and the banknote entry and exit process has been performed 500 times, the aging parameter value of the banknote module is 50%.
  • the current running data of the station terminal further includes register data, transaction data, and log files of each module.
  • the fault feature knowledge base is based on the station monitoring data of the station terminal and the station maintenance record, and through the massive analysis and core content extraction, constructs a set of experience that can provide fault warning and decision guidance basis.
  • the operation monitoring data includes each module status, register data, transaction data, and operation log of the station terminal, but is not limited thereto.
  • the fault feature knowledge base includes historical fault information of all station terminals of the station and corresponding health parameter values and maintenance record information; wherein the station is a station where the station terminal is located, and/or A plurality of stations in which the station is networked; for example, in the present embodiment, the station is a station on the entire route in which the vehicle runs or a station on the entire urban rail transit route.
  • the preset threshold range is not fixed, and may be appropriately adjusted according to different health parameter values and the current operating environment, and is not specifically limited herein.
  • the fault warning threshold may also be set according to the operating environment of the station. For example, the fault warning threshold may be negatively correlated with the passenger flow of the station where the station terminal is located within a predetermined range, that is, when the passenger flow is large. The value of the fault warning threshold is relatively low, and when the traffic volume is small, the value of the corresponding fault warning threshold is relatively large; for example, the predetermined range is 80%-90%, according to The operating environment of the station is different, and the fault warning threshold may be set to 80% or 90%, which is not specifically limited in the embodiment of the present invention.
  • the station terminal failure risk warning device further includes:
  • a fault time calculation module configured to calculate, according to a pre-calculated fault evolution rule of the station terminal, a remaining time that occurs when the fault type occurs under a preset probability; wherein the fault evolution law is according to the fault feature knowledge base The type of fault and the corresponding control health parameters are fitted and calculated.
  • the fault time warning module is configured to issue a time warning signal when the remaining time reaches a preset time warning threshold.
  • the station terminal failure risk warning device further includes:
  • a maintenance resource detecting module configured to detect maintenance resource information currently available at the station, and extract a historical maintenance record of the station; wherein the station is a station where the station terminal is located, and/or a plurality of stations connected to the station .
  • the maintenance decision generating module is configured to generate maintenance decision information according to the fault early warning signal, the fault type, the time warning signal, and the historical maintenance record, and the currently available maintenance resource information of the station.
  • a maintenance decision notification module is configured to output the generated maintenance decision information.
  • the station may be a station on the entire route in which the vehicle is running or a station on the entire urban rail transit route.
  • the maintenance resource information includes, but is not limited to, the number of spare parts of each module of the station terminal of all stations; the maintenance decision information includes adding manual service, implementing passenger flow restriction, and dispatching maintenance workers and maintenance methods. At least one kind of information.
  • the generated maintenance decision information is output, and the maintenance decision information is sent to the maintenance personnel or the fault risk detection platform, which is not specifically limited in the embodiment of the present invention.
  • the station terminal failure risk warning device further includes:
  • the knowledge base update module is configured to add the fault type of the fault that occurs in the station terminal and the corresponding health parameter value to the fault feature knowledge base when receiving the signal that the station terminal is faulty.
  • the signal that the station terminal is faulty may be acquired by the method of the first embodiment (for example, when the probability of occurrence of the fault type reaches 100%), or may be detected by other means, where The manner of acquiring the signal at which the station terminal is faulty is not specifically limited.
  • the fault information that occurs is added to the fault feature knowledge base to further improve the fault feature knowledge base.
  • the station terminal failure risk early warning device disclosed in the embodiment of the present invention acquires the current operation data of the station terminal through the data acquisition module; and calculates the current fault risk status of the station terminal by using the fault risk calculation module, where the fault risk status includes the fault type and The probability of occurrence of the fault type; then, the fault risk warning module issues an early warning signal to the fault risk of reaching the preset warning threshold, and achieves the purpose of alerting the potential high-risk fault of the station terminal.
  • the fault time calculation module further calculates the remaining time of the fault type occurring under the preset probability, so that the maintenance plan is planned in advance, and the timeliness of the fault processing is improved.
  • the maintenance resource detection module detects the currently available maintenance resource information of the station, extracts the historical maintenance record of the station, generates maintenance decision information through the maintenance decision generation module, and sends the maintenance decision information to the maintenance personnel or the failure through the maintenance decision notification module.
  • the risk detection platform enables maintenance personnel to quickly and reasonably handle faults according to maintenance decisions, greatly improving the efficiency of fault handling.
  • a sixth embodiment of the present invention provides a terminal including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, the processor executing the computer program
  • the station terminal failure risk early warning method described in any of the above embodiments is implemented.
  • a seventh embodiment of the present invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, controlling a device where the storage medium is located to perform a station terminal failure according to any of the above embodiments Risk warning method.
  • the computer program may be partitioned into one or more modules/units, the one or more modules/units being stored in the memory, and by the The processor executes to complete the present invention.
  • the one or more modules/units may be a series of computer program instruction segments capable of performing a particular function, the instruction segments being used to describe the execution of the computer program in the terminal.
  • the so-called processor can be a central processing unit (CPU), or other general-purpose processor, digital signal processor (DSP), application specific integrated circuit (ASIC), ready-made Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like, which is a control center of the terminal, and connects various parts of the entire terminal using various interfaces and lines.
  • the memory can be used to store the computer program and/or module, the processor implementing the terminal by running or executing a computer program and/or module stored in the memory, and invoking data stored in the memory Various functions of the device.
  • the memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may be stored. Data created based on the use of the mobile phone (such as audio data, phone book, etc.).
  • the memory may include a high-speed random access memory, and may also include non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a Secure Digital (SD) card.
  • non-volatile memory such as a hard disk, a memory, a plug-in hard disk, a smart memory card (SMC), and a Secure Digital (SD) card.
  • Flash Card at least one disk storage device, flash memory device, or other volatile solid-state storage device.
  • the terminal integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of a software functional unit and sold or used as a standalone product. Based on such understanding, the present invention implements all or part of the processes in the foregoing embodiments, and may also be completed by a computer program to instruct related hardware.
  • the computer program may be stored in a computer readable storage medium. The steps of the various method embodiments described above may be implemented when the program is executed by the processor.
  • the computer program comprises computer program code, which may be in the form of source code, object code form, executable file or some intermediate form.
  • the computer readable medium may include any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM). , random access memory (RAM, Random Access Memory), electrical carrier signals, telecommunications signals, and software distribution media. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in a jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, computer readable media Does not include electrical carrier signals and telecommunication signals.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical. Units can be located in one place or distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • the connection relationship between the modules indicates that there is a communication connection between them, and specifically, one or more communication buses or signal lines can be realized.

Abstract

本发明公开了一种车站终端故障风险预警方法、装置、终端及存储介质，所述方法包括：获取车站终端的当前运行数据，所述当前运行数据包括当前健康参数值；提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及所述对照健康参数值对应的故障信息，将提取出的所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态；所述故障风险状态包括故障类型及所述故障类型发生的概率；当所述故障类型发生的概率达到预设的故障预警阈值时，发出故障预警信号。采用本发明实施例能对车站终端的潜在高发的故障风险进行预警，提高故障处理的时效性。

Description

车站终端故障风险预警方法、装置、终端及存储介质 技术领域
本发明涉及车站终端故障监控领域，尤其涉及一种车站终端故障风险预警方法、装置、终端及存储介质。
背景技术
城市轨道交通在早期的建设过程中通常以运营为目标，重点旨在保障运行的功能，并未将车站自动售检票系统的运行维护作为重要目标。近年来，各地轨道交通建设的规模呈快速增长趋势，地铁、高铁等公共场所中因自动售检票系统故障导致车站站厅拥堵，引发乘客投诉和安全隐患的案例时有发生。为有效保障自动售检票系统的健康运营，对车站终端进行检修、故障诊断预警和健康管理，无疑将为城市轨道交通的安全、舒适运营提供必要的保障。
现有的自动售检票系统分为五层架构，包括中央清分系统、线路计算机、车站计算机、车站终端以及票卡。其中，车站终端安装在站厅，直接面向乘客提供自助售检票服务。在运营过程中，车站终端将自身的状态信息实时上传到车站计算机并逐级上传，最终抵达中央清分系统，以对车站终端中的各硬件模块的状态进行实时监控，当硬件模块发生故障，工作人员可根据车站计算机、线路计算机或中央清分系统上的故障提示通知维修人员到场处理。但现有的故障反馈机制仅限于处理当前的故障，存在一定的局限性。
发明内容
本发明实施例提供一种车站终端故障风险预警方法、装置、终端及存储介质，能有效解决现有技术无法对车站终端潜在高发的故障进行预警的技术问题。
本发明一实施例提供一种车站终端故障风险预警方法，包括：
获取车站终端的当前运行数据，所述当前运行数据包括当前健康参数值；
提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及所述对照健康参数值对应的故障信息，将提取出的所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态；所述故障风险状态包括故障类型及所述故障类型发生的概率；
当所述故障类型发生的概率达到预设的故障预警阈值时，发出故障预警信号。
优选地，所述故障预警阈值在预定范围内与所述车站终端所在的车站的客流量呈负相关。
优选地，根据预先计算的所述车站终端的故障演变规律，计算所述故障类型在预设概率下发生的剩余时间；其中，所述故障演变规律根据所述故障特征知识库中的故障类型及对应的对照健康参数拟合计算得到；
当所述剩余时间到达预设的时间预警阈值时，发出时间预警信号。
优选地，检测车站当前可用的维修资源信息，提取所述车站的历史维修记录；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个车站；
根据所述预警信号、所述故障类型以及历史维修记录，结合车站当前可用的维修资源信息，生成维修决策信息；
将生成的所述维修决策信息输出。
优选地，所述维修决策信息包括增加人工服务、实行客流限制以及派发维修工、维修方法中的至少一种信息。优选地，当接收到所述车站终端发生故障的信号时，将所述车站终端发生所述故障的故障类型及其对应的健康参数值添加到所述故障特征知识库中。
优选地，所述健康参数值包括以下的一种或几种：
表征所述车站终端各个模块的老化程度的老化参数值；
表征所述车站终端各个模块的日损耗的工作参数值。
优选地，所述老化参数值为所述车站终端各个模块的当前运行时间与设计运行时间的比或所述车站终端各个模块的当前交易次数与设计交易次数的比；和/或所述工作参数值为所述车站终端各个模块的日交易次数。
与现有技术相比，本发明实施例公开的车站终端故障风险预警方法，通过提取预先建立的故障特征知识库中所包含的所述当前健康参数值的预设阈值范围的对照健康参数值以及每一所述对照健康参数值对应的故障信息，并将提取出的每一所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态，所述故障风险状态包括故障类型及所述故障类型发生的概率。当所述故障类型发生的概率达到预设的预警阈值时，发出预警信号，实现了对车站终端潜在高发的故障风险进行预警，提高了故障处理的时效性。
本发明另一实施例对应提供了一种车站终端故障风险预警装置，包括：
数据获取模块，用于获取车站终端的当前运行数据，所述当前运行数据包括当前健康参数值；
故障风险计算模块，用于提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及所述对照健康参数值对应的故障信息，并将提取出的所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态；所述故障风险状态包括故障类型及所述故障类型发生的概率；
故障风险预警模块，用于当所述故障类型发生的概率达到预设的故障预警阈值时，发出故障预警信号。
优选地，所述车站终端故障风险预警装置还包括：
故障时间计算模块，用于根据预先计算的所述车站终端的故障演变规律，计算所述故障类型在预设概率下发生的剩余时间；其中，所述故障演变规律根据所述故障特征知识库中的故障类型及对应的对照健康参数拟合计算得到；
故障时间预警模块，用于当所述剩余时间到达预设的时间预警阈值时，发出时间预警信号。
优选地，所述车站终端故障风险预警装置还包括：
维修资源检测模块，用于检测车站当前可用的维修资源信息，提取所述车站的历史维修记录；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个车站；
维修决策生成模块，用于根据所述故障预警信号、所述故障类型、所述时间预警信号以及历史维修记录，结合车站当前可用的维修资源信息，生成维修决策信息；
维修决策通知模块，用于将生成的所述维修决策信息输出。
优选地，所述车站终端故障风险预警装置还包括：
知识库更新模块，用于当接收到所述车站终端发生故障的信号时，将所述车站终端发生所述故障的故障类型及其对应的健康参数值添加到所述故障特征知识库中。
与现有技术相比，本发明实施例公开的车站终端故障风险预警装置通过数据获取模块获取车站终端的当前运行数据；通过故障风险计算模块计算得到所述车站终端当前的故障风险状态，所述故障风险状态包括故障类型及所述故障类型发生的概率；然后通过故障风险预警模块对达到预设的预警阈值的故障风险，发出预警信号，实现了对车站终端的潜在高发故障风险进行预警提示的目的。
本发明另一实施例提供了一种终端，包括处理器、存储器以及存储在所述存储器中且被配置为由所述处理器执行的计算机程序，所述处理器执行所述计算机程序时实现上述发明实施例所述的车站终端故障风险预警方法。
本发明另一实施例提供了一种存储介质，所述存储介质包括存储的计算机程序，其中，在所述计算机程序运行时控制所述存储介质所在设备执行上述发明实施例所述的车站终端故障风险预警方法。
附图说明
图1是本发明第一实施例提供的一种车站终端故障风险预警方法的流程示意图。
图2是本发明第二实施例提供的一种车站终端故障风险预警方法的流程示意图。
图3是本发明第三实施例提供的一种车站终端故障风险预警方法的流程示意图
图4是本发明第四实施例提供的一种车站终端故障风险预警方法的流程示意图
图5是本发明第五实施例提供的一种车站终端故障风险预警装置的结构示意图。
具体实施方式
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
参见图1，是本发明第一实施例提供的一种车站终端故障风险预警方法的流程示意图，包括：
S101、获取车站终端的当前运行数据，所述当前运行数据包括当前健康参数值。
在本实施例中，所述车站终端当前运行数据可由车站终端的控制软件自动运行获取，或者由车站终端故障风险预警装置主动下发查询命令，来驱动车站终端的控制软件检测获取，本发明实施例不作具体限定。
在本实施例中，所述健康参数值是与所述车站终端的工作寿命相关的一系列参数值，例如，在本实施例中，所述健康参数值可包括表征所述车站终端各个模 块的老化程度的老化参数值和表征所述车站终端各个模块的日损耗的工作参数值。其中，所述老化参数值是指所述车站终端各个模块的当前运行时间与设计运行时间的比或所述车站终端各个模块的当前交易次数与设计交易次数的比；所述工作参数值为所述车站终端各个模块的日交易次数。以老化参数值为例，假设纸币模块的设计交易次数为100万次纸币进出的处理，当前已经累计执行了50万次纸币进出处理，则所述纸币模块的老化参数值为50％。
在本实施例中，所述车站终端当前运行数据还可以包括各个模块的寄存器数据、交易数据和日志文件等。
S102、提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及所述对照健康参数值对应的故障信息，将提取出的所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态；所述故障风险状态包括故障类型及所述故障类型发生的概率。
在本实施例中，所述故障特征知识库是基于车站的车站终端的运行监控数据结合车站维修记录，通过海量分析和核心内容提取，构建形成的一套可提供故障预警及决策指引依据的经验知识平台。其中，所述运行监控数据包括所述车站终端各个模块状态、寄存器数据、交易数据和运行日志，但不限于此。
需要说明的是，所述故障特征知识库包括车站所有车站终端的历史故障信息及其对应的健康参数值和维修记录信息；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个车站；例如，在本实施例中，所述车站可为车辆运行的整个路线上的车站或整个城市轨道交通路线上的车站。
在本实施例中，首先提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及每一所述对照健康参数值对应的故障信息，将提取出的每一所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到故障风险分布列表。其中，所述故障风险分布列表包含了当前健 康参数值的预设阈值范围的对照健康参数值所对应的故障信息，所述故障信息包括故障条数、故障类型及各个故障类型的概率。
通过故障风险分布列表可得到所述车站终端当前的故障风险状态，所述故障风险状态则是所述故障信息中包含的各个故障类型的概率。优选地，所述预设阈值范围为±10％。
但需要说明的是，所述预设阈值范围并不是固定不变的，可根据不同的健康参数值和当前运行环境进行适当的调节，在此不做具体的限定。
比如，在本实施例中，设定老化参数值的预设阈值范围为±10％，日交易次数对应的预设阈值范围为15％；而所述车站终端当前的老化参数值为30％，日交易次数为1万/天，则提取故障知识库中老化参数值为27％～33％，日交易次数为0.85～1.15万/天所对应的故障信息，生成故障风险分布列表。
假设上述故障风险分布列表的故障信息的故障条数为1000条，而所述1000条故障信息的故障类型分别为纸币模块退币失败故障、纸币模块存款失败故障和硬币模块找零失败故障。其中，700条为纸币模块退币失败故障，200条为纸币模块存款失败故障，100条为硬币模块找零失败故障。由此，根据预设的算法计算得到在老化参数值为30％，日交易次数为1万/天的健康参数值下，所述车站终端当前的故障风险状态，即发生纸币模块退币失败故障的概率为70％，发生纸币模块存款失败故障的概率为20％，发生硬币模块找零失败故障的概率为10％。
S103、当所述故障类型发生的概率达到预设的故障预警阈值时，发出故障预警信号。
在本实施例中，所述故障预警阈值也可以根据车站的运行环境进行设定，例如所述故障预警阈值可在预定范围内与所述车站终端所在的车站的客流量呈负相关，即在客流量较大的时候，所述故障预警阈值的取值相对较低，而在客流量较小的时候，对应的所述故障预警阈值的取值则相对较大；比如，上述预定范围 为80％-90％，根据车站的运行环境不同，所述故障预警阈值可以设定为80％，也可以设定为90％，本发明实施例不作具体限定。
综上所述，本发明实施例提供的一种车站终端故障风险预警方法，通过提取预先建立的故障特征知识库中所包含所述当前健康参数值的预设阈值范围的对照健康参数值以及每一所述对照健康参数值对应的故障信息，计算得到所述车站终端当前的故障风险状态，所述故障风险状态包括故障类型及所述故障类型发生的概率，当所述故障类型发生的概率达到预设的预警阈值时，发出预警信号。所述车站终端故障风险预警方法实现了对潜在高发的故障风险进行预警的目的。
参见图2，为本发明第二实施例提供的一种车站终端故障风险预警方法，本发明的第二实施例在第一实施例的基础上增加了如下步骤：
S204、根据预先计算的所述车站终端的故障演变规律，计算所述故障类型在预设概率下发生的剩余时间；其中，所述故障演变规律根据所述故障特征知识库中的故障类型及对应的对照健康参数拟合计算得到。
S205、当所述剩余时间到达预设的时间预警阈值时，发出时间预警信号。
在本实施例中，可以预测所述故障类型在预设概率下发生的剩余时间，便于预先策划维护方案，提高故障处理的时效性。优选地，所述预设概率大于等于90％。
参见图3，为本发明第三实施例提供的一种车站终端故障风险预警方法，本发明的第三实施例在第一实施例的基础上增加了如下步骤：
S306、检测车站当前可用的维修资源信息，提取所述车站的历史维修记录；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个车站。
例如，在本实施例中，所述车站可为车辆运行的整个路线上的车站或整个城市轨道交通路线上的车站。
S307、根据所述故障预警信号、所述故障类型、所述时间预警信号以及历史维修记录，结合车站当前可用的维修资源信息，生成维修决策信息。
S308、将生成的所述维修决策信息输出。
在本实施例中，所述维修资源信息包括所有车站的车站终端的各个模块的备件数，但不限于此；所述维修决策信息包括增加人工服务、实行客流限制以及派发维修工、维修方法中的至少一种信息。
例如，所述车站终端当前的纸币模块的故障概率为80％，但检测到所述车站终端所在的车站的纸币模块的备件数为0，而相邻的车站的纸币模块的备件数为2，则维修决策信息中则建议派发维修工并建议维修人员从相邻的车站领取纸币模块的备件到所述车站终端所在的车站开展相应的维修工作。
在本实施例中，将生成的所述维修决策信息输出，具体包括将维修决策信息发送给维护人员或故障风险监控平台，本发明实施例不作具体的限定。本实施例根据所述故障预警信号、所述故障类型、所述时间预警信号及维修记录，结合车站当前的可用维修资源，生成维修决策信息并发送给维护人员或故障风险监控平台，使得维护人员可以根据维修决策信息迅速合理地对故障进行处理，大大提高了故障处理的效率。
参见图4，为本发明第四实施例提供的一种车站终端故障风险预警方法，本发明的第四实施例在第一实施例的基础上增加了如下步骤：
S409、当接收到所述车站终端发生故障的信号时，将所述车站终端发生所述故障的故障类型及其对应的健康参数值添加到所述故障特征知识库中。
在本实施例中，所述车站终端发生故障的信号可以由第一实施例的方法获取(例如当所述故障类型发生的概率达到100％时)也可以是通过其他的方式检测获取，在此对所述车站终端发生故障的信号的获取方式不做具体的限定。
在本实施例中，将发生的故障信息添加到故障特征知识库中，进一步完善故障特征知识库。参见图5，是本发明第五实施例提供的一种车站终端故障风险预警装置500的结构示意图，包括：
数据获取模块510，用于获取车站终端的当前运行数据，所述当前运行数据包括当前健康参数值；
故障风险计算模块520，用于提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及所述对照健康参数值对应的故障信息，将提取出的所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态；所述故障风险状态包括故障类型及所述故障类型发生的概率；
故障风险预警模块530，用于当所述故障类型发生的概率达到预设的故障预警阈值时，发出故障预警信号。
在本实施例中，所述健康参数值是与所述车站终端的工作寿命相关的一系列参数值，例如，在本实施例中，所述健康参数值包括表征所述车站终端各个模块的老化程度的老化参数值和表征所述车站终端各个模块的日损耗的工作参数值。其中，所述老化参数值是指所述车站终端各个模块的当前运行时间与设计运行时间的比或所述车站终端各个模块的当前交易次数与设计交易次数的比；所述工作参数值为所述车站终端各个模块的日交易次数。以老化参数值为例，假设纸币模块的设计交易次数为100万次纸币进出的处理，当前已经累计执行了50万次纸币进出处理，则所述纸币模块的老化参数值为50％。
在本实施例中，所述车站终端当前运行数据还包括各个模块的寄存器数据、交易数据和日志文件等。
在本实施例中，所述故障特征知识库是基于车站的车站终端的运行监控数据结合车站维修记录，通过海量分析和核心内容提取，构建形成的一套可提供故障预警及决策指引依据的经验知识平台。其中，所述运行监控数据包括所述车站终端各个模块状态、寄存器数据、交易数据和运行日志，但不限于此。
需要说明的是，所述故障特征知识库包括车站所有车站终端的历史故障信息及其对应的健康参数值和维修记录信息；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个车站；例如，在本实施例中，所述车站为车辆运行的整个路线上的车站或整个城市轨道交通路线上的车站。
在本实施例中，所述预设阈值范围并不是固定不变的，可根据不同的健康参数值和当前运行环境进行适当的调节，在此不做具体的限定。所述故障预警阈值也可以根据车站的运行环境进行设定，例如所述故障预警阈值可在预定范围内与所述车站终端所在的车站的客流量呈负相关，即在客流量较大的时候，所述故障预警阈值的取值相对较低，而在客流量较小的时候，对应的所述故障预警阈值的取值则相对较大；比如，上述预定范围为80％-90％，根据车站的运行环境不同，所述故障预警阈值可以设定为80％，也可以设定为90％，本发明实施例不做具体限定。
优选地，所述车站终端故障风险预警装置还包括：
故障时间计算模块，用于根据预先计算的所述车站终端的故障演变规律，计算所述故障类型在预设概率下发生的剩余时间；其中，所述故障演变规律根据所述故障特征知识库中的故障类型及对应的对照健康参数拟合计算得到。
故障时间预警模块，用于当所述剩余时间到达预设的时间预警阈值时，发出时间预警信号。
优选地，所述车站终端故障风险预警装置还包括：
维修资源检测模块，用于检测车站当前可用的维修资源信息，提取所述车站的历史维修记录；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个车站。
维修决策生成模块，用于根据所述故障预警信号、所述故障类型、所述时间预警信号以及历史维修记录，结合车站当前可用的维修资源信息，生成维修决策信息。
维修决策通知模块，用于将生成的所述维修决策信息输出。
在本实施例中，所述车站可为车辆运行的整个路线上的车站或整个城市轨道交通路线上的车站。
在本实施例中，所述维修资源信息包括所有车站的车站终端的各个模块的备 件数，但不限于此；所述维修决策信息包括增加人工服务、实行客流限制以及派发维修工、维修方法中的至少一种信息。
在本实施例中，将生成的所述维修决策信息输出，包括将所述维修决策信息发送给维护人员或故障风险检测平台，本发明实施例不作具体限定。
优选地，所述车站终端故障风险预警装置还包括：
知识库更新模块，用于当接收到所述车站终端发生故障的信号时，将所述车站终端发生所述故障的故障类型及其对应的健康参数值添加到所述故障特征知识库中。
在本实施例中，所述车站终端发生故障的信号可以由第一实施例的方法获取(例如当所述故障类型发生的概率达到100％时)也可以是通过其他的方式检测获取，在此对所述车站终端发生故障的信号的获取方式不做具体的限定。
在本实施例中，将发生的故障信息添加到故障特征知识库中，进一步完善故障特征知识库。
本发明实施例公开的车站终端故障风险预警装置通过数据获取模块获取车站终端的当前运行数据；通过故障风险计算模块计算得到所述车站终端当前的故障风险状态，所述故障风险状态包括故障类型及所述故障类型发生的概率；然后通过故障风险预警模块对达到预设的预警阈值的故障风险，发出预警信号，实现了对车站终端的潜在高发故障风险进行预警提示的目的。
进一步通过故障时间计算模块计算所述故障类型在预设概率下发生的剩余时间，便于预先策划维护方案，提高故障处理的时效性。
进一步通过维修资源检测模块检测车站当前可用的维修资源信息，提取所述车站的历史维修记录，通过维修决策生成模块生成维修决策信息，并通过维修决策通知模块将维修决策信息发送给维护人员或故障风险检测平台，使得维护人员可以根据维修决策迅速合理地对故障进行处理，大大提高了故障处理的效率。
本发明第六实施例提供了一种终端，所述终端包括处理器、存储器以及存储 在所述存储器中且被配置为由所述处理器执行的计算机程序，所述处理器执行所述计算机程序时实现上述任意一实施例所述的车站终端故障风险预警方法。
本发明第七实施例提供了一种存储介质，所述存储介质包括存储的计算机程序，其中，在所述计算机程序运行时控制所述存储介质所在设备执行上述任意实施例所述的车站终端故障风险预警方法。
示例性的，第六及第七实施例中，所述计算机程序可以被分割成一个或多个模块/单元，所述一个或者多个模块/单元被存储在所述存储器中，并由所述处理器执行，以完成本发明。所述一个或多个模块/单元可以是能够完成特定功能的一系列计算机程序指令段，该指令段用于描述所述计算机程序在所述终端中的执行过程。
所称处理器可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等，所述处理器是所述终端的控制中心，利用各种接口和线路连接整个终端的各个部分。
所述存储器可用于存储所述计算机程序和/或模块，所述处理器通过运行或执行存储在所述存储器内的计算机程序和/或模块，以及调用存储在存储器内的数据，实现所述终端设备的各种功能。所述存储器可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序(比如声音播放功能、图像播放功能等)等；存储数据区可存储根据手机的使用所创建的数据(比如音频数据、电话本等)等。此外，存储器可以包括高速随机存取存储器，还可以包括非易失性存储器，例如硬盘、内存、插接式硬盘，智能存储卡(Smart Media Card,SMC)，安全数字(Secure Digital,SD)卡，闪存卡(Flash Card)、 至少一个磁盘存储器件、闪存器件、或其他易失性固态存储器件。
其中，所述终端集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明实现上述实施例方法中的全部或部分流程，也可以通过计算机程序来指令相关的硬件来完成，所述的计算机程序可存储于一计算机可读存储介质中，该计算机程序在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机程序包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。
需说明的是，以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。另外，本发明提供的装置实施例附图中，模块之间的连接关系表示它们之间具有通信连接，具体可以实现为一条或多条通信总线或信号线。本领域普通技术人员在不付出创造性劳动的情况下，即可以理解并实施。
以上所述是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也视为本发明的保护范围。

Claims (14)

  1. 一种车站终端故障风险预警方法，其特征在于，包括：
    获取车站终端的当前运行数据，所述当前运行数据包括当前健康参数值；
    提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及所述对照健康参数值对应的故障信息，将提取出的所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态；所述故障风险状态包括故障类型及所述故障类型发生的概率；
    当所述故障类型发生的概率达到预设的故障预警阈值时，发出故障预警信号。
  2. 根据权利要求1所述的车站终端故障风险预警方法，其特征在于：
    所述故障预警阈值在预定范围内与所述车站终端所在的车站的客流量呈负相关。
  3. 根据权利要求1所述的车站终端故障风险预警方法，其特征在于，还包括:
    根据预先计算的所述车站终端的故障演变规律，计算所述故障类型在预设概率下发生的剩余时间；其中，所述故障演变规律根据所述故障特征知识库中的故障类型及对应的对照健康参数拟合计算得到；
    当所述剩余时间到达预设的时间预警阈值时，发出时间预警信号。
  4. 根据权利要求1所述的车站终端故障风险预警方法，其特征在于，进一步还包括：
    检测车站当前可用的维修资源信息，提取所述车站的所述车站终端的历史维修记录；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个 车站；
    根据所述预警信号、所述故障类型以及历史维修记录，结合车站当前可用的维修资源信息，生成维修决策信息；
    将生成的所述维修决策信息输出。
  5. 根据权利要求4所述的车站终端故障风险预警方法，其特征在于，所述维修决策信息包括增加人工服务、实行客流限制以及派发维修工、维修方法中的至少一种信息。
  6. 根据权利要求1所述的车站终端故障风险预警方法，其特征在于，还包括：
    当接收到所述车站终端发生故障的信号时，将所述车站终端发生所述故障的故障类型及其对应的健康参数值添加到所述故障特征知识库中。
  7. 根据权利要求1至6任一项所述的车站终端故障风险预警方法，其特征在于：所述健康参数值包括以下的一种或几种：
    表征所述车站终端各个模块的老化程度的老化参数值；
    表征所述车站终端各个模块的日损耗的工作参数值。
  8. 根据权利要求7所述的车站终端故障风险预警方法，其特征在于：
    所述老化参数值为所述车站终端各个模块的当前运行时间与设计运行时间的比或所述车站终端各个模块的当前交易次数与设计交易次数的比；和/或所述工作参数值为所述车站终端各个模块的日交易次数。
  9. 一种车站终端故障风险预警装置，其特征在于，包括：
    数据获取模块，用于获取车站终端的当前运行数据，所述当前运行数据包括 当前健康参数值；
    故障风险计算模块，用于提取预先建立的故障特征知识库中包含所述当前健康参数值的预设阈值范围的对照健康参数值以及所述对照健康参数值对应的故障信息，将提取出的所述对照健康参数值及对应的故障信息通过预设的算法进行计算，得到所述车站终端当前的故障风险状态；所述故障风险状态包括故障类型及所述故障类型发生的概率；
    故障风险预警模块，用于当所述故障类型发生的概率达到预设的故障预警阈值时，发出故障预警信号。
  10. 根据权利要求9所述的车站终端故障风险预警装置，其特征在于，还包括：
    故障时间计算模块，用于根据预先计算的所述车站终端的故障演变规律，计算所述故障类型在预设概率下发生的剩余时间；其中，所述故障演变规律是根据所述故障特征知识库中的故障类型及对应的对照健康参数拟合计算得到；
    故障时间预警模块，用于当所述剩余时间到达预设的时间预警阈值时，发出时间预警信号。
  11. 根据权利要求9所述的车站终端故障风险预警装置，其特征在于，还包括：
    维修资源检测模块，用于检测车站当前可用的维修资源信息，提取所述车站的历史维修记录；其中，所述车站为所述车站终端所在的车站，和/或与该车站联网的多个车站的车站；
    维修决策生成模块，用于根据所述故障预警信号、所述故障类型、所述时间预警信号以及历史维修记录，结合车站当前可用的维修资源信息，生成维修决策信息；
    维修决策通知模块，用于将生成的所述维修决策信息输出。
  12. 根据权利要求9所述的车站终端故障风险预警装置，其特征在于，还包括：
    知识库更新模块，用于当接收到所述车站终端发生故障的信号时，将所述车站终端发生所述故障的故障类型及其对应的健康参数值添加到所述故障特征知识库中。
  13. 一种终端，包括处理器、存储器以及存储在所述存储器中且被配置为由所述处理器执行的计算机程序，所述处理器执行所述计算机程序时实现如权利要求1至8中任意一项所述的车站终端故障风险预警方法。
  14. 一种存储介质，其特征在于，所述存储介质包括存储的计算机程序，其中，在所述计算机程序运行时控制所述存储介质所在设备执行如权利要求1至8中任意一项所述的车站终端故障风险预警方法。
PCT/CN2018/099001 2017-12-15 2018-08-06 车站终端故障风险预警方法、装置、终端及存储介质 WO2019114288A1 (zh)

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Families Citing this family (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108062088B (zh) * 2017-12-15 2019-11-19 广州广电运通金融电子股份有限公司 车站终端故障风险预警方法、装置、终端及存储介质
CN109144027B (zh) * 2018-07-13 2020-06-19 深圳华侨城文化旅游科技集团有限公司 一种游乐设施的故障预警方法、存储介质及应用服务器
CN109189051A (zh) * 2018-10-29 2019-01-11 江苏罗思韦尔电气有限公司 一种基于T-Box的故障存储及处理方法
CN109358603A (zh) * 2018-10-30 2019-02-19 日立楼宇技术（广州）有限公司 车载定位终端的故障上报方法、系统、设备和存储介质
CN110189019A (zh) * 2019-05-29 2019-08-30 中国民用航空总局第二研究所 机场物联网节点设备工作状态动态预警方法和系统
CN110390404A (zh) * 2019-07-12 2019-10-29 杭州培慕科技有限公司 一种基于知识库和数据管理的rcm
CN112537559A (zh) * 2019-09-20 2021-03-23 中国石油化工股份有限公司 油库油气监测方法及监测系统
CN110831310B (zh) * 2019-10-31 2021-12-07 新鸿电子有限公司 X射线源阴极检测方法、检测系统及x射线成像系统
CN110808864A (zh) * 2019-11-12 2020-02-18 国家电网有限公司 通信预警方法、装置及系统
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CN112734138B (zh) * 2021-01-27 2023-07-07 珠海格力电器股份有限公司 一种故障预警方法、装置、设备及存储介质
CN112987696A (zh) * 2021-03-15 2021-06-18 国家电网有限公司 一种区域配电网设备管理平台及其运行方法
CN114267178B (zh) * 2021-12-30 2023-09-26 佳都科技集团股份有限公司 一种车站的智能运营维护方法及装置
CN116362452B (zh) * 2023-03-15 2023-09-12 东莞先知大数据有限公司 一种研磨机故障预警方法、装置及存储介质
CN116366477B (zh) * 2023-05-30 2023-08-18 中车工业研究院(青岛)有限公司 一种列车网络通信信号检测方法、装置、设备及存储介质

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004013208A (ja) * 2002-06-03 2004-01-15 Matsushita Electric Ind Co Ltd 自動料金収受装置の遠隔保守システム
JP2008009764A (ja) * 2006-06-29 2008-01-17 Ricoh Co Ltd 自動料金収受システム
CN201898519U (zh) * 2010-09-01 2011-07-13 燕山大学 带有风险控制的设备维修预警装置
CN202939769U (zh) * 2012-12-16 2013-05-15 四川久远新方向智能科技有限公司 轨道交通自动售检票系统设备运行故障报警系统
CN104778821A (zh) * 2015-04-09 2015-07-15 银江股份有限公司 一种交通设备自动报警系统及方法
CN106209432A (zh) * 2016-06-30 2016-12-07 中国人民解放军国防科学技术大学 基于动态阈值的网络设备亚健康预警方法及装置
CN107433962A (zh) * 2016-06-18 2017-12-05 刘春梅 一种用于轨道交通故障监控与智能预警的方法和系统
CN108062088A (zh) * 2017-12-15 2018-05-22 广州广电运通金融电子股份有限公司 车站终端故障风险预警方法、装置、终端及存储介质

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101950382B (zh) * 2010-09-01 2013-03-06 燕山大学 一种带有风险控制的液压设备最优维修计算方法
CN106384154A (zh) * 2016-08-18 2017-02-08 广州市迪士普音响科技有限公司 智慧平台的自我检查与故障自我处理方法和系统
CN107168278A (zh) * 2017-04-25 2017-09-15 广州地铁集团有限公司 地铁列车信号系统的自动维护与预警系统

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004013208A (ja) * 2002-06-03 2004-01-15 Matsushita Electric Ind Co Ltd 自動料金収受装置の遠隔保守システム
JP2008009764A (ja) * 2006-06-29 2008-01-17 Ricoh Co Ltd 自動料金収受システム
CN201898519U (zh) * 2010-09-01 2011-07-13 燕山大学 带有风险控制的设备维修预警装置
CN202939769U (zh) * 2012-12-16 2013-05-15 四川久远新方向智能科技有限公司 轨道交通自动售检票系统设备运行故障报警系统
CN104778821A (zh) * 2015-04-09 2015-07-15 银江股份有限公司 一种交通设备自动报警系统及方法
CN107433962A (zh) * 2016-06-18 2017-12-05 刘春梅 一种用于轨道交通故障监控与智能预警的方法和系统
CN106209432A (zh) * 2016-06-30 2016-12-07 中国人民解放军国防科学技术大学 基于动态阈值的网络设备亚健康预警方法及装置
CN108062088A (zh) * 2017-12-15 2018-05-22 广州广电运通金融电子股份有限公司 车站终端故障风险预警方法、装置、终端及存储介质

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