CN112884325A - Method and system for application analysis and health condition evaluation of customer station equipment - Google Patents

Method and system for application analysis and health condition evaluation of customer station equipment Download PDF

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Publication number
CN112884325A
CN112884325A CN202110198378.7A CN202110198378A CN112884325A CN 112884325 A CN112884325 A CN 112884325A CN 202110198378 A CN202110198378 A CN 202110198378A CN 112884325 A CN112884325 A CN 112884325A
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health
equipment
real
passenger
target
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李君�
王洪亮
陈瑞凤
徐春婕
方凯
沈海燕
杨国元
张亚伟
金久强
徐海生
李超
谢甲旭
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Institute of Computing Technologies of CARS
Beijing Jingwei Information Technology Co Ltd
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Institute of Computing Technologies of CARS
Beijing Jingwei Information Technology Co Ltd
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    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00—Computing arrangements based on specific mathematical models
    • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
    • 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
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/26—Government or public services
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification

Abstract

The invention provides a method and a system for application analysis and health condition evaluation of passenger station equipment, wherein the method comprises the following steps: acquiring real-time state information related to target equipment in a passenger station; determining a health probability value of the target device; determining the health matching level of the target equipment according to the health probability value; and under the condition that the health matching level is lower than the preset health level threshold, dynamically evaluating the health condition of the target equipment according to the real-time state information to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle estimation result of the target equipment. The invention can solve the problem of resource waste caused by untimely or frequent overhaul of equipment in the traditional operation mode, and can predict and analyze the remaining life cycle, the fault trend and the production operation condition of the intelligent passenger station equipment by reasonably setting the maintenance nodes, thereby improving the working efficiency of passenger operators and greatly improving the travel satisfaction of passengers and the intelligent level of railway passenger stations.

Description

Method and system for application analysis and health condition evaluation of customer station equipment
Technical Field
The invention relates to the technical field of equipment management, in particular to a method and a system for application analysis and health condition evaluation of customer station equipment.
Background
The railway informatization is taken as an important means for supporting passenger service and passenger service of railway passenger stations, and plays a significant role in the construction process of the railway passenger stations in China. The emergence of advanced technologies such as artificial intelligence, a neural network, a robot, the Internet of things, big data, cloud computing and 5G and the like rapidly promotes the informatization and intelligent construction of high-speed railways in China, intelligent applications developed based on business requirements are infinite, and the types and the number of equipment matched with the informatization and intelligent development of stations are exponentially increased.
As a core unit and a component for supporting the informatization and intelligent development of railways, the safety, reliability and maintainability of intelligent passenger station equipment directly relate to the processes of passenger transport production organization, passenger transport operation command, passenger trip and the like, and the operation and maintenance thereof directly influence the intelligent construction process of railways.
For a long time, the operation and maintenance of railway passenger station equipment in China usually adopts the modes of manual regular inspection, manual work report of work orders, fault hypothesis and the like to control the equipment to be overhauled, inspected and checked (for short, three-inspection), has high labor cost, low efficiency, small coverage, ineffective supervision of the operation process and easy error inspection, missing inspection and the like, and the traditional calendar type operation and maintenance is adopted, so that the situations of insufficient maintenance or excessive maintenance are easily generated, the waste of resources is caused, and the economic bearing capability is poor.
In a conventional state-based maintenance (CBM) system, although the real-time state of a device is considered, a "threshold" method is still used as a judgment rule for operation and maintenance of the device, the device within the "threshold" is not processed or monitored, only the device exceeding the "threshold" is maintained, and although the operation and maintenance costs are saved, many dangerous points are not controlled in time, so that the life cycle of the device is shortened, the reliability of the device is poor, and other problems occur
In view of this, it is necessary to improve the existing passenger station equipment management method to ensure the safe and efficient operation of passenger service and passenger service in railway passenger stations.
Disclosure of Invention
The embodiment of the invention provides a method and a system for analyzing application of passenger station equipment and evaluating health conditions, aiming at solving the defects that the fed-back mass data cannot be subjected to targeted analysis according to business requirements and passenger operation requirements and cannot provide operation guidance and auxiliary decision for station workers because of various front-end acquisition equipment and large number in the conventional railway passenger station.
The invention provides a method for analyzing application and evaluating health condition of passenger station equipment, which comprises the following steps: acquiring real-time state information related to target equipment in a passenger station through a data engine; determining a health probability value of the target equipment according to the real-time state information; determining the health matching level of the target equipment according to the health probability value; under the condition that the health matching level is lower than a preset health level threshold value, dynamically evaluating the health condition of the target equipment according to the real-time state information to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle estimation result of the target equipment; the larger the preset health level threshold is, the more normal the health condition of the target device is.
According to the application analysis and health condition evaluation method for the passenger station equipment, the method for determining the health probability value of the target equipment according to the real-time state information comprises the following steps: classifying all the passenger station equipment based on the functions and performances of all the equipment in the passenger station, and constructing a health condition evaluation index system; determining a dividing mode of the health grades, including determining health probability value ranges of different health grades; based on a 9-degree scoring method, a health evaluation matrix is created by combining a health condition evaluation index system, classification results and health probability value ranges of different health grades; calculating the prior probability of the base-level indexes and the relative probabilities of the middle-level indexes and the high-level indexes in a health condition evaluation index system based on a health evaluation matrix, and creating a Bayesian network for index evaluation; and determining the health probability value of the target equipment by using the Bayesian network according to the relevant real-time state information of the target equipment.
According to the method for analyzing the application and evaluating the health condition of the passenger station equipment, provided by the invention, after the health evaluation result, the fault trend prediction result, the fault deduction result and the residual life cycle prediction result of the target equipment are obtained, the method further comprises the following steps: determining the equipment health condition of the target equipment and an auxiliary decision suggestion aiming at the target equipment by combining the train operation plan data; the train operation plan data includes at least one of: train running state data, passenger transport operation plan data and equipment management plan data; the assistant decision suggestion includes at least one of: the method comprises the following steps of equipment purchase scheme, equipment management plan, equipment operation and maintenance strategy, equipment energy-saving and environment-friendly measures and equipment production operation plan optimization scheme.
According to the method for analyzing the application and evaluating the health condition of the passenger station equipment provided by the invention, after the equipment health condition of the target equipment and the assistant decision suggestion aiming at the target equipment are determined, the method further comprises the following steps: visually displaying the equipment health condition and the auxiliary decision suggestion by utilizing at least one of a station three-dimensional mathematical model, a plane display interface and a query terminal; the visual display mode comprises at least one of the following modes: graphs, charts, lists, pie charts, histograms, scatter plots, three-dimensional electronic maps, fishbone plots, line graphs, audio, video.
According to the method for analyzing the application of the client station equipment and evaluating the health condition, provided by the invention, the dynamic evaluation of the health condition of the target equipment is carried out according to the real-time state information, and the method comprises the following steps: based on an artificial intelligence analysis method, dynamically evaluating the health condition of the real-time state information; the artificial intelligence analysis method comprises at least one of the following steps: KNN model, SVM model, TOPSIS method.
According to the method for analyzing the application and evaluating the health condition of the client station equipment, before the real-time state information related to the target equipment in the client station is acquired through the data engine, the method further comprises the following steps: respectively acquiring the running state information of each internal system device and the running state information of each external system device in real time by using an internal interface and an external interface of a passenger station device health state management system and a data acquisition terminal; uploading the running state information of the internal system equipment and the running state information of the external system equipment to a data engine; the internal system device includes at least one of: the system comprises a passenger service and production control platform, a railway ticket selling and booking system, a passenger transport equipment management information system, a passenger transport management system and a railway transport scheduling management system; the external system device includes at least one of: elevator, air conditioner, lighting and blower in building automation system and fire alarm system.
According to the method for analyzing the application and evaluating the health condition of the passenger station equipment, before the health probability value of the target equipment is determined according to the real-time state information, the method further comprises the following steps: carrying out primary preprocessing on the real-time state information; performing feature extraction on the real-time state information subjected to primary preprocessing based on a principal component analysis method, and performing state classification on the real-time state information subjected to feature extraction; performing secondary preprocessing on the real-time state information after state classification; the first pretreatment comprises at least one of the following: cleaning, integrating, transforming and reducing; the secondary pretreatment comprises at least one of the following: cleaning, standardizing and normalizing.
The invention also provides a system for analyzing the application of the passenger station equipment and evaluating the health condition, which comprises the following components: the equipment state acquisition module is mainly used for acquiring real-time state information related to target equipment in the passenger station through a data engine; the data analysis processing module is used for determining the health probability value of the target equipment according to the real-time state information; the state analysis module is mainly used for determining the health matching level of the target equipment according to the health probability value; the health state evaluation and auxiliary analysis module is used for dynamically evaluating the health state of the target equipment according to the real-time state information under the condition that the health matching level is lower than a preset health level threshold value so as to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle prediction result of the target equipment; the larger the preset health level threshold is, the more normal the health condition of the target device is.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the steps of the application analysis and health condition evaluation method of the passenger station device.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the passenger station apparatus application analysis and health assessment method as described in any of the above.
The method and the system for analyzing the application of the passenger station equipment and evaluating the health condition can solve the problem of resource waste caused by untimely or frequent overhaul of the equipment in the traditional operation mode, can predict and analyze the remaining life cycle, the fault trend and the production operation condition of the intelligent passenger station equipment by reasonably setting the maintenance nodes, improve the working efficiency of passenger transport operation personnel, and greatly improve the travel satisfaction of passengers and the intelligent level of a railway passenger transport station.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for analyzing application and evaluating health status of a guest station device according to the present invention;
FIG. 2 is a schematic flow chart of determining a health match rating for a passenger station device provided by the present invention;
FIG. 3 is a schematic flow chart for constructing a health evaluation index system according to the present invention;
FIG. 4 is a second schematic flow chart of the method for analyzing application and evaluating health status of the client station equipment according to the present invention;
FIG. 5 is a schematic flow diagram of the real-time status information acquisition provided by the present invention;
FIG. 6 is a schematic diagram of a method for preprocessing real-time status information according to the present invention
FIG. 7 is a schematic structural diagram of a system for analyzing application and evaluating health status of a guest station device according to the present invention;
FIG. 8 is a second schematic structural view of a system for analyzing application and evaluating health status of a guest station device according to the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that in the description of the embodiments of the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The invention comprehensively considers the factors of economic affordability, equipment reliability, equipment maintainability, equipment life cycle Management and the like of an intelligent passenger station, and aims to provide a method, a system, a device and electronic equipment for application analysis and Health evaluation of intelligent passenger station equipment by combining fault Prediction and Health Management (PHM) theory, so as to carry out application analysis and Health evaluation on the intelligent passenger station equipment, wherein the method comprises the following steps: analyzing the acquired real-time state information in real time by using a big data analysis platform, and dividing a plurality of health matching grades according to the equipment type and grade; and a health evaluation model is established by combining with an artificial intelligence algorithm, application analysis and health condition evaluation are carried out on intelligent passenger station equipment, a supply unit and a maintenance unit, operation and maintenance prediction and fault trend deduction are carried out, maintenance time nodes are defined more clearly, passenger transport workers can manage the equipment conveniently, auxiliary decision support is provided for passenger transport operation command, and passenger travel satisfaction and passenger transport operation efficiency are greatly improved.
The following describes a method and a system for application analysis and health condition evaluation of a guest station device according to an embodiment of the present invention with reference to fig. 1 to 9.
Fig. 1 is a schematic flow chart of a method for analyzing application and evaluating health status of a guest station device according to the present invention, as shown in fig. 1, including but not limited to the following steps:
step 101: acquiring real-time state information related to target equipment in a passenger station through a data engine;
step 102: determining a health probability value of the target equipment according to the real-time state information;
step 103: determining the health matching level of the target equipment according to the health probability value;
step 104: and under the condition that the health matching level is lower than a preset health level threshold, dynamically evaluating the health condition of the target equipment according to the real-time state information to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle estimation result of the target equipment. Wherein the larger the preset health level threshold is, the more normal the health condition of the target device is.
Wherein, the real-time status information of the passenger station device mainly comprises: running state information of devices such as a display screen, a broadcast interface machine, a sound pick-up, an inquiry machine, a video monitoring camera, an automatic ticket selling and checking terminal, an entrance ticket checking gate and the like in internal systems such as a passenger service and production management and control platform, a ticket selling and booking system (TRS), a passenger transport equipment management information system, a passenger transport management system, a railway transportation scheduling management system (TDMS) and the like; the system comprises a plurality of pieces of equipment such as elevators, air conditioners, lighting and fans in external systems such as a Building Automation System (BAS) and a Fire Alarm System (FAS), and particularly comprises daily normal operation state logs, fault logs, maintenance work orders, maintenance logs, equipment hardware configuration conditions, background operation logs and system logs of passenger service and production management and control platforms, TRSs, passenger transport equipment management information systems, passenger transport management systems, TDMSs and the like, and even comprises supply information of the specific equipment, supply records of suppliers and maintenance service records and the like.
The real-time state information of all the passenger station equipment is collected and summarized in an intelligent passenger station equipment health condition management data engine.
In step 101, when application analysis and health evaluation need to be performed on a target device in any of the guest stations, real-time status information related to the target device may be called from the data engine.
Further, after obtaining the real-time status information related to the target device, the real-time status information may be monitored and analyzed, including: reasonably classifying the target equipment by combining the function and the performance of the target equipment, classifying health condition indexes by combining classification conditions, importance and performance indexes, and constructing a health evaluation matrix by matching with the preset classification grade and range of the health indexes; establishing a Bayesian network for analyzing the health condition of intelligent passenger station equipment by calculating prior probability and probability of fault occurring at specific time; and finally, the occurrence probability of the superior node of the target equipment, namely the health probability value of the target equipment can be obtained by utilizing the Bayesian network.
Finally, in step 103, the evaluation indexes in the pre-constructed device health state threshold interval are combined, so as to obtain the health matching level of the target device.
For example, the threshold interval of the health condition of the intelligent passenger station may be pre-divided into 6 levels according to the health level of the equipment, which are respectively health, sub-health, qualified, abnormal, fault, and serious fault, and the value ranges corresponding to each interval are V1[0.95,1.0], V2[0.90,0.95 ], V3[0.85,0.90 ], V4[0.80,0.85 ], V5[0.70,0.80 ], V6[0.60,0.70 ").
Assuming that the health probability value of the target device calculated in step 102 is 0.89, it can be determined that the health matching level of the target device is level V3, and the health status of the device is sub-health status.
The division of the threshold interval of the health status of the device is performed according to the characteristics and functions of the target object and the related content such as the current health status. In the specific matching, the probability that the target device is healthy (i.e., the healthy probability value) is matched with the threshold interval. Wherein, the health probability value of each target device can be obtained through a pre-constructed Bayesian network.
The real-time state information of the target equipment is firstly classified according to the equipment type, and then the next subdivision is carried out according to the prediction requirement of the target equipment.
For example: the estimated failure trend of the ticket vending machine needs to perform targeted analysis on detected failure information of the ticket vending machine according to relevant real-time state information of main components of the ticket vending machine, such as failure state, failure occurrence time, maintenance period, failure duration, post-maintenance state, component replacement condition and the like, and the total health probability value of the ticket vending machine is obtained by integrating the analysis contents. Then, the real-time status information is matched with the threshold interval of the target device, and finally a matching result (namely, a health matching grade) is obtained. According to the health matching level, the target device can be determined to belong to a certain level between V1-V6, that is, the health level of the target device can be determined to be used for evaluating whether the target device is healthy and the degree of health.
Further, in step 104, after the health matching level of the target device is obtained as V3 (i.e. the health level thereof is at the V3 level), the health level is compared with the preset health level threshold (assumed as the V2 level) corresponding to the target device. Because the health level of the target device is lower than the preset health level threshold, it can be determined that the health state of the target device does not reach the standard, and the health state needs to be dynamically evaluated according to the target device, so as to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle prediction result of the target device. The above results can provide decision support for passenger transport staff by combining with plan data such as train operation state basic conditions, passenger transport operation plans, equipment management plans and the like.
In addition, in the case that the health matching level of the target device is not lower than (higher than or equal to) the preset health level threshold, the health state of the target device can be considered as healthy, and further processing is not needed, namely, the existing health state of the target device is maintained, and the latest evaluation result and auxiliary decision suggestion (such as passenger transportation operation planning, maintenance planning, device use and deployment situation and the like) are kept as comparison criteria of historical data and next analysis.
The method for analyzing the application of the passenger station equipment and evaluating the health condition can solve the problem of resource waste caused by untimely or frequent overhaul of the equipment in the traditional operation mode, can predict and analyze the remaining life cycle, the fault trend and the production operation condition of the intelligent passenger station equipment by reasonably setting the maintenance nodes, improves the working efficiency of passenger transport operation personnel, and greatly improves the traveling satisfaction of passengers and the intelligent level of a railway passenger transport station.
Based on the content of the foregoing embodiments, as an optional embodiment, the health probability value of the target device is determined according to the real-time status information, which includes but is not limited to:
classifying all the passenger station equipment based on the functions and performances of all the equipment in the passenger station, and constructing a health condition evaluation index system; determining a dividing mode of the health grades, including determining health probability value ranges of different health grades; based on a 9-degree scoring method, a health evaluation matrix is created by combining a health condition evaluation index system, classification results and health probability value ranges of different health grades; calculating the prior probability of the base-level indexes and the relative probabilities of the middle-level indexes and the high-level indexes in the health condition evaluation index system based on the health evaluation matrix, and creating a Bayesian network for index evaluation; and determining the health probability value of the target equipment by using the Bayesian network according to the relevant real-time state information of the target equipment.
Fig. 2 is a schematic flow chart of determining the health matching level of the passenger station device, and as shown in fig. 2, according to the method for analyzing the application of the passenger station device and evaluating the health condition, classification of the passenger station device is determined according to specific functions and performances of the passenger station device, classification of health evaluation indexes is performed by combining classification conditions, device importance levels, device performance indexes and the like, a health evaluation matrix is established in the next step by determining an index level range, and then a priori rule and the probability of occurrence of a fault at specific time are calculated, so that a bayesian network of the health evaluation indexes is established. And then, the probability of the superior node is obtained according to the network structure, so that the evaluation indexes are divided by combining the probability intervals, and finally the health evaluation grade of the corresponding equipment is obtained.
Optionally, the classification condition refers to that the intelligent passenger station equipment is classified according to functions, performances, categories and evaluation criteria. The importance level of the equipment refers to the importance of the equipment under a certain evaluation index. It should be noted that the above-mentioned importance level of the device is a relative concept, namely: the degree of importance of a certain index relative to other indexes. The device performance index refers to a performance parameter of the device, and is more specific, for example: the ticket issuing speed, the ticket issuing rate, the paper jam rate, the CPU occupation ratio and other specific parameters of the automatic ticket vending machine.
Fig. 3 is a schematic flow chart of the method for constructing a health condition evaluation index system provided by the present invention, and as shown in fig. 5, the method performs health evaluation index grade division according to the category, importance degree and main function of intelligent passenger station equipment, constructs a health condition evaluation index system according to the above, and constructs a health condition evaluation index body specifically including:
the first-level index is the overall health level of the intelligent passenger station equipment and is marked as L; the secondary indexes are respectively the health levels of the service equipment and the ticket equipment, and are respectively recorded as A, B (the electromechanical equipment is not considered in the application for the moment); the three-level indexes are divided into display equipment and control equipment according to the equipment identification function, and are specifically divided into travel service display equipment, travel service control equipment, passenger ticket display equipment and passenger ticket control equipment which are respectively marked as A1, A2, B1 and B2; the level four indexes are display and control equipment of a specific travel service system and a passenger ticket system, and specifically comprise the following steps: the system comprises a guiding display screen (comprising a window screen, a ticket amount screen, a platform screen, a channel screen and the like, which are recorded as A11), an arrival notification screen (which is recorded as A12), a handheld mobile terminal (which is recorded as A13), a clock device (which is recorded as A14), a video monitoring camera (which is recorded as A15), a tourist service system server (which is recorded as A21), a broadcast control device (which is recorded as A22), a guiding control device (which is recorded as A23), an intelligent video control device (which is recorded as A24), an automatic ticket vending machine (which is recorded as B11), an automatic ticket taker (which is recorded as B12), a ticket checking gate (which is recorded as B13), a mobile ticket supplementing machine (which is recorded as B14), an identity authentication server (which is recorded as B21) and a ticket selling and.
All the above devices are increased or decreased according to the difference of each intelligent passenger station). All the four-level indexes can be associated and matched with specific equipment according to evaluation standards such as equipment completeness rate, equipment operation system, equipment failure rate, mean time between failure and operation, equipment reliability, failure outage rate, equipment failure frequency, mean time between failure and repair, mean time between prevention and maintenance, overall efficiency OEE, equipment response time and the like according to functions, performances, service objects and the like of the intelligent customer station equipment.
It should be noted that the specific types of devices in different stations may vary, and the basic architecture is only provided in the present application, and the model can be adjusted and optimized by the specific newly added devices.
For example, the above-mentioned dividing manner for determining the health level includes determining the health probability value range of each of the different health levels, which may be:
the method comprises the steps of dividing an equipment health condition threshold interval of an intelligent passenger station into 6 health grades according to the health grade of equipment, wherein the health grades are respectively health, sub-health, qualified, abnormal, fault and serious fault, and the health probability value ranges corresponding to each interval are respectively V1[0.95,1.0], V2[0.90,0.95 ], V3[0.85,0.90 ], V4[0.80,0.85), V5[0.70,0.80), V6[0.60, 0.70).
And constructing a health evaluation matrix based on a 9-degree scoring method according to evaluation results of all evaluation indexes to calculate the prior probability of the basic indexes and the health probability values corresponding to the middle and high-level indexes, and placing the obtained health probability values in a threshold interval for comparison. If the health index value of the target equipment is far away from the standard value, the possibility that the health condition of the equipment belongs to the standard is smaller, and finally the health grade of the target equipment is determined according to the maximum probability principle.
The health evaluation matrix can be regarded as an importance degree evaluation matrix, and the specific form is as follows: the probability value of a specific index corresponding to a certain type of equipment is determined by taking various types of passenger equipment as a horizontal axis and taking various indexes as a vertical axis, for example: the importance degree probability of determining the average barrier-free working time of the ticket vending machine is 1/5, and the importance degree probability of determining the failure degree of the LED display screen is 1/3 and the like.
Further, since the bayesian network is a Directed Acyclic Graph (DAG), it is composed of nodes representing variables and Directed edges connecting the nodes. The nodes represent random variables, the directed edges among the nodes represent the mutual correlation system (the father node points to the son node), the relation strength is expressed by conditional probability, and the prior probability is used for expressing information without the father node. The node variables may be abstractions of any problem, such as: test values, observations, opinion polls, etc. The method is applicable to expressing and analyzing uncertain and probabilistic events, and to making decisions that are conditionally dependent on a variety of control factors, and can make inferences from incomplete, inaccurate, or uncertain knowledge or information.
Therefore, the invention utilizes the characteristics of the Bayesian network, the intelligent customer station equipment health condition management system combines the functions and the performances of the intelligent customer station equipment to reasonably classify the equipment, the intelligent customer station equipment is subjected to health condition index division according to the parameters such as classification condition, equipment importance, specific equipment performance indexes and the like, a health condition evaluation matrix is constructed, the prior probability and the probability of faults occurring at specific time are calculated, the Bayesian network (Bayesian network) of the health condition of the intelligent customer station equipment is constructed, and the occurrence probability of a superior node is obtained by the Bayesian network structure, so that the health probability value of the target equipment can be determined.
The method for application analysis and health condition evaluation of the passenger station equipment, provided by the invention, combines a PHM theory, utilizes a big data analysis platform to analyze the acquired real-time state information in real time, divides a plurality of health grades according to different types and grades of the equipment, and establishes a Bayesian network model for health evaluation by combining an artificial intelligence algorithm, so as to perform application analysis and health condition evaluation on the intelligent passenger station equipment, further provide a more definite maintenance time node for operation and maintenance prediction and fault trend deduction of the equipment, facilitate passenger transport staff to manage the equipment and provide auxiliary decision support for passenger transport operation command, and greatly improve the travel satisfaction degree of passengers and the passenger transport operation efficiency.
Based on the content of the foregoing embodiment, as an optional embodiment, after obtaining the health assessment result, the failure trend prediction result, the failure deduction result, and the remaining life cycle prediction result of the target device, the method further includes: determining equipment health conditions of the target equipment and an auxiliary decision suggestion aiming at the target equipment by combining train operation plan data; the train operation plan data includes at least one of: train running state data, passenger transport operation plan data and equipment management plan data; the aid decision suggestion includes at least one of: the method comprises the following steps of equipment purchase scheme, equipment management plan, equipment operation and maintenance strategy, equipment energy-saving and environment-friendly measures and equipment production operation plan optimization scheme.
The invention provides a method for analyzing application and evaluating health condition of passenger station equipment, which determines a proper node of equipment maintenance by obtaining a dynamic evaluation result of the health condition of each passenger station equipment, and definitely formulates maintenance content in the maintenance node, and specifically comprises the following steps:
after the results of health index calculation and evaluation, fault trend prediction, running condition deduction, fault situation deduction, remaining life cycle prediction and the like of each passenger station device are obtained, decision support can be provided for passenger transport workers according to the results and by combining plan data such as train running state basic conditions, passenger transport operation plans, device management plans and the like.
Specifically, the corresponding evaluation may be given according to the health condition of the target device in combination with the maintenance unit and the supplier of the target device. If the evaluation object is a maintenance unit, combining the operation maintenance condition; if the evaluation object is a supplier, the health condition and the big data analysis result of the operation condition of other equipment provided by the supplier can be referred to. Therefore, evaluation support is provided for the station, and whether the supplier or the maintenance unit is adopted at the later stage is determined.
Further, according to the application analysis and health condition evaluation method for the passenger station equipment, after results of health index calculation and evaluation, fault trend prediction, running condition deduction, fault situation deduction, remaining life cycle prediction and the like of each passenger station equipment are obtained, product quality of a supply unit and service quality of a maintenance unit can be evaluated in a targeted mode according to equipment categories, and therefore qualified suppliers and maintenance units can be selected in the later stage of a passenger station conveniently.
The assistant decision means may include: and providing an equipment purchase scheme, an equipment management plan, an equipment operation and maintenance strategy, equipment energy-saving and environment-friendly measures, an equipment production operation plan optimization scheme and the like.
It should be noted that the present invention maintains the existing health status of the intelligent passenger station equipment health indicators meeting the threshold range, as well as the latest evaluation results and decision-making assistance suggestions, as comparison criteria for historical data and next analysis.
For example: for the current healthier equipment (such as reaching the level of V2 and above), the existing aid decision-making means (including passenger transportation operation planning, maintenance planning, equipment use and deployment conditions and the like) is maintained.
According to the method for analyzing the application of the passenger station equipment and evaluating the health condition, the problems that the evaluation and the health condition of the intelligent passenger station equipment depend on manual routing inspection and evaluation and timely and accurate evaluation results and management schemes cannot be obtained are solved by establishing the intelligent passenger station application analysis and health condition evaluation system, and passenger transport operation guidance and supplier and maintenance manager selection evaluation problems of passenger transport workers are solved, resource waste caused by untimely or frequent maintenance and the like of equipment in a traditional operation mode is thoroughly changed, maintenance nodes are reasonably arranged, real-time operation and maintenance conditions are visually displayed, the residual life cycle and fault trend of the intelligent passenger station equipment and even production operation conditions are predicted and analyzed, the working efficiency of the passenger transport workers is improved, and the travel satisfaction of passengers and the intelligent level of railway passenger transport stations are greatly improved.
Based on the content of the foregoing embodiment, as an optional embodiment, after determining the device health condition of the target device and the assistant decision suggestion for the target device, the method further includes: visually displaying the equipment health condition and the auxiliary decision suggestion by utilizing at least one of a station three-dimensional mathematical model, a plane display interface and a query terminal; the visual display mode comprises at least one of the following modes: graphs, charts, lists, pie charts, histograms, scatter plots, three-dimensional electronic maps, fishbone plots, line graphs, audio, video.
The method for analyzing the application of the passenger station equipment and evaluating the health condition, provided by the invention, comprises the following steps of performing front-end visual display on the obtained results after obtaining the health evaluation result, the fault trend prediction result, the fault deduction result and the residual life cycle prediction result of each target equipment so as to facilitate passenger production operators to timely and conveniently obtain auxiliary decision and support data, and comprises the following steps:
the visual display of the health condition of intelligent passenger station equipment is realized by combining a plurality of media such as a station three-dimensional mathematical model, a plane display interface, a query terminal and the like;
wherein, the display mode includes but is not limited to: the display method comprises the following steps of displaying various display modes such as graphs, charts, lists, pie charts, bar charts, scatter diagrams, three-dimensional electronic maps, fishbone diagrams, broken line diagrams, audio and video.
Based on the content of the foregoing embodiment, as an optional embodiment, the dynamically evaluating the health condition of the target device according to the real-time status information includes:
based on an artificial intelligence analysis method, dynamically evaluating the health condition of the real-time state information; the artificial intelligence analysis method comprises at least one of the following steps: KNN model, SVM model, TOPSIS method.
The dynamic evaluation of the health condition provided by the invention specifically comprises the following steps: and the intelligent passenger station equipment health condition management system evaluates the intelligent passenger station equipment health condition according to the matching result of the health threshold range, and generates a health evaluation result, a fault trend prediction, a fault deduction result, a residual life cycle prediction and the like of the intelligent passenger station equipment.
The method comprises the steps of carrying out reasonable data classification and grade evaluation on an equipment health condition threshold matching result by using a big data analysis related algorithm (such as a decision tree, Bayes and the like), then carrying out analysis and evaluation on data by using artificial intelligence processing means such as KNN, Gradient Boosting, SVM, TOPSIS and the like, and selecting a proper model and algorithm by the model according to equipment operation condition data, equipment characteristics and specific classification so as to obtain results such as intelligent passenger station equipment health index calculation and evaluation, fault trend prediction, operation condition deduction, fault situation deduction, residual life cycle prediction and the like.
In the evaluation process, the passenger traffic needs and the traffic requirements of passenger services are combined, such as: the intelligent inquiry and help seeking, the intelligent connection in the station, the fault prediction, the residual life cycle estimation of the equipment, the fault trend deduction, the intelligent scheduling, the intelligent operation and maintenance management and the like are provided.
Furthermore, the above assistant decision suggestion may further include: and the product quality of the supply unit and the service quality of the maintenance unit are evaluated in a targeted manner according to the equipment category, so that qualified suppliers and maintenance units can be selected at the later stage of the passenger station.
The specific way of processing the real-time status information by using the above-mentioned artificial intelligence processing means such as KNN, Gradient Boosting, SVM model, TOPSIS, etc. is not specifically limited, but generally includes but is not limited to: and selecting a proper processing means according to the data structure and the data form of the real-time state information and the analysis target.
Fig. 4 is a second flowchart of the application analysis and health status evaluation method for the guest station device according to the embodiment of the present invention, and as shown in fig. 4, the whole application analysis and health status evaluation method includes, but is not limited to, the following:
step 1: and the passenger station equipment health condition management data engine acquires the real-time state information of each intelligent passenger station equipment in real time, and stores and calls the real-time state information.
Step 2: and the intelligent passenger station equipment health condition management system performs data preprocessing on any acquired real-time state information in the background.
And step 3: the intelligent passenger station equipment health condition management system carries out state monitoring on the processed data, and the state monitoring comprises the following steps: according to the classification and the use condition of the passenger station equipment and the service requirement, the health threshold value range of the passenger station equipment is divided into a plurality of different intervals (namely a plurality of health probability value ranges). And meanwhile, acquiring the health probability value of each target device to be analyzed. And matching the health probability value of the target equipment with the plurality of divided health probability value ranges to obtain the health matching level of the target equipment.
And 4, step 4: comparing the health matching level of the target equipment with a preset health level threshold, and if the health matching level of the target equipment exceeds the threshold range of the preset health level threshold, proving that the health state of the target equipment does not reach the standard; if the threshold range of the preset health level threshold is not exceeded, the health state of the target device is proved to be good, and at this time, the existing health state of the device, the last evaluation result and the suggestion of the aid decision can be maintained, and the process goes to step 7.
And 5: and when the health state of the target equipment is determined to be not up to the standard, the health state management system dynamically evaluates the health state of the target equipment, including acquiring a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle estimation result of the target equipment.
Step 6: after the result of the dynamic evaluation of the health condition of the target equipment is obtained, the intelligent passenger station equipment health condition management system provides corresponding auxiliary decision suggestions by an artificial intelligence method according to the result.
And 7: and combining the analysis of the front section to display the health state evaluation result and the auxiliary decision suggestion so that the staff in the station can take corresponding treatment in time.
Based on the content of the foregoing embodiment, as an optional embodiment, before the obtaining, by the data engine, real-time status information related to a target device in a client station, the method further includes:
respectively acquiring the running state information of each internal system device and the running state information of each external system device in real time by using an internal interface and an external interface of a passenger station device health state management system and a data acquisition terminal;
uploading the operating condition information of the internal system equipment and the operating condition information of the external system equipment to the data engine;
the internal system device includes at least one of: the system comprises a passenger service and production control platform, a railway ticket selling and booking system, a passenger transport equipment management information system, a passenger transport management system and a railway transport scheduling management system;
the external system device includes at least one of: elevator, air conditioner, lighting and blower in building automation system and fire alarm system.
Fig. 5 is a schematic flow diagram of the real-time status information acquisition provided by the present invention, and as shown in fig. 5, the data engine of the health management system of the intelligent client station device acquires the real-time status information of each intelligent client station device in real time, including:
respectively acquiring running state information of equipment such as a display screen, a broadcast interface machine, a sound pick-up, an inquiry machine, a video monitoring camera, an automatic ticket selling and checking terminal, an inbound ticket checking gate and the like of internal systems such as a passenger service and production control platform, a Railway ticket selling and reserving System (TRS), a passenger transport equipment management information System, a passenger transport management System, a Railway transportation scheduling management System (TDMS) and the like by virtue of an internal interface and an external interface of an intelligent passenger station equipment health state management System and data acquisition terminals such as a sensor, an infrared detector, a camera and the like deployed in a passenger station; and acquiring real-time operation state data of equipment such as elevators, air conditioners, lighting and fans of external systems such as a Building Automation System (BAS) and a Fire Alarm System (FAS), and uploading the real-time operation state data to an intelligent passenger station equipment health condition management data engine.
The operation condition information of the intelligent passenger station equipment can include: daily normal operation state logs, fault logs, maintenance work orders, maintenance logs, equipment hardware configuration conditions, background operation logs and system logs and fault logs of passenger service and production management and control platforms, TRSs, passenger transport equipment management information systems, passenger transport management systems, TDMSs and other systems of equipment such as ticket checking gates, ticket vending machines, self-service ticket checking terminals and the like, and even comprises supply information of specific equipment and supply and maintenance service record and other equipment operation related data of suppliers and maintenance providers.
According to the method for the application analysis and the health condition evaluation of the client station equipment, the real-time collection and the summarization of the real-time state information related to all the client station equipment in the whole client station equipment health condition management system are realized in a data engine calling mode, a data basis is provided for the application analysis and the health condition evaluation of the client station equipment, and the comprehensiveness and the reliability of data analysis are effectively guaranteed.
Based on the content of the foregoing embodiment, as an optional embodiment, before the determining the health probability value of the target device according to the real-time status information, the method further includes:
carrying out primary preprocessing on the real-time state information; performing feature extraction on the real-time state information after primary pretreatment based on a principal component analysis method, and performing state classification on the real-time state information after feature extraction; performing secondary preprocessing on the real-time state information after state classification; the first pretreatment comprises at least one of the following: cleaning, integrating, transforming and reducing; the secondary pretreatment comprises at least one of the following: cleaning, standardizing and normalizing.
Fig. 6 is a schematic diagram of a method for preprocessing real-time status information provided by the present invention, as shown in fig. 6, because a data engine is only used for collecting data, but because the whole system for managing the health status of the passenger station equipment has a lot of passenger station equipment, and the real-time status information related to each passenger station equipment may be collected by different collection devices, and because the collected real-time status information also includes more useless interference information and information with obvious errors, the method for analyzing application and evaluating health status of the passenger station equipment further includes a process for preprocessing data after the real-time status information related to the passenger station equipment is acquired, including:
firstly, carrying out primary pretreatment such as cleaning, integration, transformation, reduction and the like on the acquired real-time state information; then, performing feature extraction on the preprocessed data by a Principal Component Analysis (PCA) method; and then, carrying out state classification according to the data state information after the characteristics are extracted, and carrying out secondary pretreatment such as cleaning, standardization, normalization and the like on the classified data again to finally obtain the effective real-time state information related to the intelligent passenger station equipment.
Fig. 7 is a schematic structural diagram of a system for analyzing application and evaluating health status of a guest station device according to the present invention, as shown in fig. 7, including but not limited to: the system comprises a device state acquisition module 701, a data analysis processing module 702, a state analysis module 703 and a health state evaluation and auxiliary analysis module 704.
The device state acquisition module 701 is mainly used for acquiring real-time state information related to target devices in the passenger station through a data engine; the data analysis processing module 702 is mainly configured to determine a health probability value of the target device according to the real-time status information; the state analysis module 703 is mainly configured to determine a health matching level of the target device according to the health probability value; the health status evaluation and auxiliary analysis module 704 is configured to perform dynamic health status evaluation on the target device according to the real-time status information under the condition that the health matching level is lower than a preset health level threshold, so as to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle prediction result of the target device; the larger the preset health level threshold is, the more normal the health condition of the target device is.
Fig. 8 is a second schematic structural view of the application analysis and health status evaluation system for the guest station equipment provided by the present invention, and as shown in fig. 8, the application analysis and health status evaluation system for the intelligent guest station equipment provided by the present invention mainly includes: an equipment state acquisition module 701, which is mainly used for real-time state acquisition 607; the state analysis module 703 is mainly used for real-time state analysis and application condition evaluation; a data analysis processing module 702, which is mainly used for data preprocessing, data feature extraction, data sample classification, standardization processing, and the like; the health condition evaluation and auxiliary analysis module 704 is mainly used for building a package evaluation index, evaluating a health index, evaluating a supplier, analyzing an auxiliary decision, and the like.
The system for application analysis and health condition evaluation of the intelligent passenger station equipment provided by the invention can also comprise: the application analysis and assistant decision display module 705 is mainly used for performing application analysis display and assistant decision display.
The application analysis and health condition evaluation system for the passenger station equipment can solve the problem of resource waste caused by untimely or frequent overhaul of the equipment in the traditional operation mode, and can predict and analyze the remaining life cycle, the fault trend and the production operation condition of the intelligent passenger station equipment by reasonably setting the maintenance nodes, thereby improving the working efficiency of passenger transport operation personnel and greatly improving the traveling satisfaction of passengers and the intelligent level of a railway passenger transport station.
It should be noted that, in the embodiment of the present invention, the application analysis and health condition evaluation system for the guest station device may be implemented based on the method for application analysis and health condition evaluation for the guest station device described in any one of the above embodiments when being specifically executed, and details of this embodiment are not described herein.
Fig. 9 is a schematic structural diagram of an electronic device provided in the present invention, and as shown in fig. 9, the electronic device may include: a processor (processor)910, a communication Interface (Communications Interface)920, a memory (memory)930, and a communication bus 940, wherein the processor 910, the communication Interface 920, and the memory 930 communicate with each other via the communication bus 940. The processor 910 may invoke logic instructions in the memory 930 to perform a method for application analysis and health assessment by a guest station device, the method comprising: acquiring real-time state information related to target equipment in a passenger station through a data engine; determining a health probability value of the target equipment according to the real-time state information; determining the health matching level of the target equipment according to the health probability value; under the condition that the health matching level is lower than a preset health level threshold value, dynamically evaluating the health condition of the target equipment according to the real-time state information to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle estimation result of the target equipment; the larger the preset health level threshold is, the more normal the health condition of the target device is.
Furthermore, the logic instructions in the memory 930 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, which when executed by a computer, enable the computer to perform the method for application analysis and health evaluation of a guest station device provided by the above methods, the method comprising: acquiring real-time state information related to target equipment in a passenger station through a data engine; determining a health probability value of the target equipment according to the real-time state information; determining the health matching level of the target equipment according to the health probability value; under the condition that the health matching level is lower than a preset health level threshold value, dynamically evaluating the health condition of the target equipment according to the real-time state information to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle estimation result of the target equipment; the larger the preset health level threshold is, the more normal the health condition of the target device is.
In yet another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the method for application analysis and health evaluation of a guest station device provided in the foregoing embodiments, and the method includes: acquiring real-time state information related to target equipment in a passenger station through a data engine; determining a health probability value of the target equipment according to the real-time state information; determining the health matching level of the target equipment according to the health probability value; under the condition that the health matching level is lower than a preset health level threshold value, dynamically evaluating the health condition of the target equipment according to the real-time state information to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle estimation result of the target equipment; the larger the preset health level threshold is, the more normal the health condition of the target device is.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A method for application analysis and health condition evaluation of passenger station equipment is characterized by comprising the following steps:
acquiring real-time state information related to target equipment in a passenger station through a data engine;
determining a health probability value of the target equipment according to the real-time state information;
determining the health matching level of the target equipment according to the health probability value;
under the condition that the health matching level is lower than a preset health level threshold value, dynamically evaluating the health condition of the target equipment according to the real-time state information to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle prediction result of the target equipment;
the larger the preset health level threshold is, the more normal the health condition of the target device is.
2. The method for application analysis and health evaluation of a client station device as claimed in claim 1, wherein the determining the health probability value of the target device according to the real-time status information comprises:
classifying all the passenger station equipment based on the functions and performances of all the equipment in the passenger station, and constructing a health condition evaluation index system;
determining a dividing mode of the health grades, including determining health probability value ranges of different health grades;
based on a 9-degree scoring method, a health evaluation matrix is created by combining the health condition evaluation index system, the classification result and the health probability value ranges of different health grades;
calculating the prior probability of the base-level indexes and the relative probabilities of the middle-level indexes and the high-level indexes in the health condition evaluation index system based on the health evaluation matrix, and creating a Bayesian network for index evaluation;
and determining the health probability value of the target equipment according to the related real-time state information of the target equipment by utilizing the Bayesian network.
3. The method for application analysis and health assessment of customer station equipment according to claim 1, wherein after obtaining the health assessment results, the failure trend prediction results, the failure deduction results and the remaining life cycle prediction results of the target equipment, further comprising:
determining equipment health conditions of the target equipment and an auxiliary decision suggestion aiming at the target equipment by combining train operation plan data;
the train operation plan data includes at least one of: train running state data, passenger transport operation plan data and equipment management plan data;
the aid decision suggestion includes at least one of: the method comprises the following steps of equipment purchase scheme, equipment management plan, equipment operation and maintenance strategy, equipment energy-saving and environment-friendly measures and equipment production operation plan optimization scheme.
4. The passenger station equipment application analysis and health assessment method of claim 3, further comprising, after determining the equipment health of the target equipment and the aid decision suggestion for the target equipment:
visually displaying the equipment health condition and the auxiliary decision suggestion by utilizing at least one of a station three-dimensional mathematical model, a plane display interface and a query terminal;
the visual display mode comprises at least one of the following modes: graphs, charts, lists, pie charts, histograms, scatter plots, three-dimensional electronic maps, fishbone plots, line graphs, audio, video.
5. The method for application analysis and health assessment of a client station device according to claim 1, wherein said dynamically assessing health of said target device based on said real-time status information comprises:
based on an artificial intelligence analysis method, dynamically evaluating the health condition of the real-time state information;
the artificial intelligence analysis method comprises at least one of the following steps: KNN model, SVM model, TOPSIS method.
6. The method for application analysis and health assessment of a client station device as claimed in claim 1, wherein before the real-time status information related to the target devices in the client station is obtained by the data engine, the method further comprises:
respectively acquiring the running state information of each internal system device and the running state information of each external system device in real time by using an internal interface and an external interface of a passenger station device health state management system and a data acquisition terminal;
uploading the operating condition information of the internal system equipment and the operating condition information of the external system equipment to the data engine;
the internal system device includes at least one of: the system comprises a passenger service and production control platform, a railway ticket selling and booking system, a passenger transport equipment management information system, a passenger transport management system and a railway transport scheduling management system;
the external system device includes at least one of: elevator, air conditioner, lighting and blower in building automation system and fire alarm system.
7. The method for application analysis and health evaluation of a client station device as claimed in claim 1, wherein before determining the health probability value of the target device according to the real-time status information, the method further comprises:
carrying out primary preprocessing on the real-time state information;
performing feature extraction on the real-time state information after primary pretreatment based on a principal component analysis method, and performing state classification on the real-time state information after feature extraction;
performing secondary preprocessing on the real-time state information after state classification;
the primary pretreatment comprises at least one of: cleaning, integrating, transforming and reducing;
the secondary pretreatment comprises at least one of: cleaning, standardizing and normalizing.
8. A passenger station equipment application analysis and health condition evaluation system is characterized by comprising:
the device state acquisition module is used for acquiring real-time state information related to the target device in the passenger station through the data engine;
the data analysis processing module is used for determining the health probability value of the target equipment according to the real-time state information;
the state analysis module is used for determining the health matching level of the target equipment according to the health probability value;
the health state evaluation and auxiliary analysis module is used for dynamically evaluating the health state of the target equipment according to the real-time state information under the condition that the health matching level is lower than a preset health level threshold value so as to obtain a health evaluation result, a fault trend prediction result, a fault deduction result and a residual life cycle prediction result of the target equipment;
the larger the preset health level threshold is, the more normal the health condition of the target device is.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method steps of the application analysis and health assessment method of the passenger station device according to any one of claims 1 to 7 when executing the computer program.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method steps of the passenger station application analysis and health assessment method of any of claims 1 to 7.
CN202110198378.7A 2021-02-22 2021-02-22 Method and system for application analysis and health condition evaluation of customer station equipment Pending CN112884325A (en)

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CN114298616A (en) * 2022-03-11 2022-04-08 西南石油大学 Equipment health state evaluation method and device and computer equipment
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CN115481929A (en) * 2022-10-17 2022-12-16 四川大学华西医院 Method and device for evaluating effectiveness of reconstruction measures, terminal equipment and storage medium
CN115658799A (en) * 2022-10-18 2023-01-31 日本电产（韶关）有限公司 Production data display method and system
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CN116342111A (en) * 2023-05-30 2023-06-27 中汽信息科技(天津)有限公司 Intelligent transaction method and system for automobile parts based on big data
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CN114997554B (en) * 2022-03-21 2024-03-12 湖北文理学院 Evaluation method, device and storage medium for health condition of complex mechanical equipment
CN115352493A (en) * 2022-08-26 2022-11-18 交控科技股份有限公司 Method and device for calculating system health degree of full-line vehicle-mounted controller
CN115352493B (en) * 2022-08-26 2024-04-26 交控科技股份有限公司 Method and device for calculating health degree of all-line vehicle-mounted controller system
CN115481929A (en) * 2022-10-17 2022-12-16 四川大学华西医院 Method and device for evaluating effectiveness of reconstruction measures, terminal equipment and storage medium
CN115481929B (en) * 2022-10-17 2023-11-24 四川大学华西医院 Reconstruction measure effectiveness evaluation method and device, terminal equipment and storage medium
CN115658799A (en) * 2022-10-18 2023-01-31 日本电产（韶关）有限公司 Production data display method and system
CN115658799B (en) * 2022-10-18 2023-08-15 尼得科电机(韶关)有限公司 Production data display method and system
CN116187864A (en) * 2023-04-20 2023-05-30 北京达净科技有限公司 Passenger transport service analysis and scheduling method, system and management and control platform
CN116342111A (en) * 2023-05-30 2023-06-27 中汽信息科技(天津)有限公司 Intelligent transaction method and system for automobile parts based on big data
CN116342111B (en) * 2023-05-30 2023-08-29 中汽信息科技(天津)有限公司 Intelligent transaction method and system for automobile parts based on big data
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