CN116468419A - Rail transit big data operation and maintenance decision analysis method, device and storage medium - Google Patents
Rail transit big data operation and maintenance decision analysis method, device and storage medium Download PDFInfo
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Abstract
The invention discloses a rail transit big data operation and maintenance decision analysis method, a device and a storage medium, wherein the method comprises the following steps: s1, a perception layer comprises a track traffic association basic database; s2, the data acquisition layer acquires fault alarm data and analog quantity data from the sensing layer and transmits the fault alarm data and analog quantity data to the data management center; s3, a health evaluation model is arranged in the data management center, and real-time health diagnosis is carried out on the health evaluation model; and S4, when the health index of the equipment i reaches the health index maintenance threshold, the data management center sends an equipment maintenance and treatment suggestion work order to the PMS system. According to the invention, the fault alarm data associated with the track traffic equipment are collected and stored according to the upper and lower hierarchical architecture, a health evaluation model is built in the data management center, and health index quantitative calculation is carried out on each equipment under stations, lines and networks, so that early warning time is accurately predicted and obtained, and the track traffic equipment is convenient to guide the state maintenance of the track traffic equipment.
Description
Technical Field
The invention relates to the field of urban rail transit, in particular to a rail transit big data operation and maintenance decision analysis method, a device and a storage medium.
Background
In recent years, urban rail transit in China is rapidly developed, and the investment in communication and signal equipment is gradually enormous. The state maintenance is one of the important ways to optimize and improve the traditional planned maintenance, the state maintenance is highly dependent on the equipment facility state evaluation and the analysis of the change rule, the data analysis of the rail transit equipment at the present stage is still in the manual analysis stage, the manual screening, operation, analysis and processing of the mass alarm data, the log file and the equipment failure rate of the monitoring equipment are required, the manual analysis has the defects of low efficiency, easy error, high labor cost and the like, and meanwhile, the analysis data in the prior art is not comprehensive enough, the data mining dimension is not deep enough, and the equipment state is difficult to accurately analyze. Along with the finer and stricter requirements of equipment health evaluation indexes and data mining dimensions, manual means can not meet the requirements of mass data coupling analysis. Meanwhile, the evaluation result of the health state of the equipment based on data analysis needs to be periodically presented, and the existing data analysis needs to be manually operated, so that the problems of long time consumption, low efficiency, easy error and the like exist, and the analysis requirement of the maintenance data of the wire network level cannot be met.
Disclosure of Invention
The invention aims to overcome the technical problems pointed out by the background technology, and provides a rail transit big data operation and maintenance decision analysis method, a device and a storage medium, wherein fault alarm data associated with rail transit equipment are collected and stored according to an upper hierarchical architecture and a lower hierarchical architecture, a health evaluation model is built in a data management center, health indexes of various equipment under stations, lines and networks are quantitatively calculated, real-time accurate diagnosis of the equipment health degree data is realized to obtain health indexes, early warning time is accurately predicted, and then the health change trend of the stations, the lines and the networks is obtained, so that the state maintenance of the rail transit equipment is conveniently guided.
The aim of the invention is achieved by the following technical scheme:
a rail transit big data operation and maintenance decision analysis method comprises the following steps:
s1, a perception layer comprises a rail traffic association basic database, wherein the rail traffic association basic database comprises a communication maintenance system, a signal maintenance system, a PMS system and an external input data document which are associated with rail traffic;
s2, the data acquisition layer acquires fault alarm data and analog quantity data from the sensing layer and transmits the fault alarm data and analog quantity data to the data management center, and the data management center performs hierarchical membership from bottom to top according to equipment, stations, lines and nets and stores the hierarchical membership into the basic database;
s3, a health evaluation model is arranged in the data management center, and the health evaluation model carries out real-time health diagnosis according to the following method:
s31, sequentially arranging fault alarm data of the equipment i according to the operation time, and obtaining average fault-free interval time M based on an MTBF algorithm i With running time of equipment i, average no fault interval M i Obtaining an average fault-free interval change curve of the equipment i by fitting the abscissa and the ordinate, and obtaining a curvature coefficient C i ；
S32, respectively constructing an evaluation model corresponding to the equipment i, wherein the evaluation model of the equipment i adopts the following health evaluation relation formula:
wherein lambda is i For failure rate of device i, K i For the scaling factor corresponding to device i, C i For the curvature coefficient of the mean fault-free interval curve of the device i, H i Is a health index;
mi is the mean time between failure of the device;
the health evaluation model outputs health indexes in real time according to the running time of the equipment i as real-time diagnosis data of the equipment i;
s33, setting a health index early warning threshold Z of the equipment i i The health evaluation model calculates that the running state of the equipment i reaches the health index early warning threshold Z based on a curve fitting algorithm of a least square method i Early warning time in time;
s4, a maintenance and treatment database is stored in the data management center, and a health index maintenance and maintenance threshold value and a corresponding equipment maintenance and treatment suggestion work order are stored in the maintenance and treatment database; when the health index of the equipment i reaches a health index maintenance threshold, the data management center sends an equipment maintenance treatment suggestion work order to the PMS system, and the PMS system creates a treatment work order and a personnel task assignment; the maintenance treatment database also stores fault data of the equipment i and a corresponding fault troubleshooting treatment work order, if the fault alarm data of the equipment i in the basic database of the data management center has the corresponding fault data in the maintenance treatment database, the maintenance treatment database sends the fault troubleshooting treatment work order to the PMS system, and the PMS system creates the troubleshooting treatment work order and the personnel task assignment.
The further technical scheme is as follows: the method also comprises the following steps:
s5, the data management center stores real-time diagnosis data and early warning time of the equipment i in a hierarchical architecture from bottom to top according to equipment, stations, lines and wire networks to form summarized data of three levels of station level, line level and wire network level; the data management center and the PMS system respectively transmit data to a platform service layer, and the platform service layer is provided with a data visualization service unit which realizes data report forms and data visualization display at the equipment level, the station level, the line level and the line network level.
The further technical scheme is as follows: the method also comprises the following steps:
s6, the platform service layer is connected with a platform application layer, the platform application layer is provided with a data display configuration unit, a platform authority management unit, a health management large screen and a health management background, the data display configuration unit is used for configuring data display, the platform authority management unit is used for recording and authority setting management of a platform user, the health management large screen is used for displaying a data report and data visual display, and the health management background is used for managing and storing data.
Preferably, in the method S5, the data management center integrates a line database according to the line classification, and the line database stores fault alarm data, real-time diagnosis data, data report and data summary of all devices under the affiliated line; each track traffic engineering line is independently provided with a corresponding data acquisition unit.
Preferably, the data acquisition layer is used for respectively acquiring a communication maintenance system, a signal maintenance system and a PMS system through a terminal sensor, and the data acquisition layer is also used for realizing the input of external input data documents including excel and txt; the communication maintenance system comprises transmission, wireless, clock, broadcasting, PIS and monitoring data, the signal maintenance system comprises vehicle-mounted, turnout, power supply, ATS, interlocking, DCS and axle counting data, the PMS system comprises fault data, work order information and task personnel information, and the external input data file comprises equipment log, maintenance record and alarm information data.
Preferably, a data cleaning algorithm, a turnout historical position algorithm, a turnout action curve analysis algorithm, a data analysis mining algorithm, a vehicle-mounted MPBF algorithm, a power supply voltage current daily deviation algorithm and a subsystem intelligent matching algorithm are stored in the data management center; before the data management center stores the data into a basic database, performing data cleaning processing including deletion processing, duplication removal, illegal data removal and invalid data filtration on the data through a data cleaning algorithm; after the data cleaning process is completed, the data analysis mining algorithm classifies, extracts, integrates and integrates the data according to the parameter entering format specification of the health evaluation model and according to lines, stations and equipment, and forms model basic data to be stored in a basic database.
Preferably, in the method S5, the data visualization service unit implements a data report and a data visualization display according to a line network level, a line level and a station level, where the line network level display includes a full line network health report, early warning prediction and operation and maintenance decision data, the line level display includes a fault report, a health overview and line level operation and maintenance decision data, and the station level display includes health evaluation results, change trends, equipment early warning and maintenance suggestion data of specific equipment.
A rail transit big data operation and maintenance decision analysis system, comprising:
the perception layer comprises a rail traffic association basic database, wherein the rail traffic association basic database comprises a communication maintenance system, a signal maintenance system, a PMS system and an external input data document which are associated with the rail traffic, and is used for linking the rail traffic association data;
the data acquisition layer is used for acquiring fault alarm data and analog quantity data from the sensing layer and transmitting the fault alarm data and the analog quantity data to the data management center;
the data management center is internally provided with a health evaluation model, the health evaluation model is used for calculating and obtaining average fault-free interval time and real-time diagnosis data of the equipment i, and the health evaluation model is setHealth index early warning threshold Z of equipment i i The health evaluation model calculates that the running state of the equipment i reaches the health index early warning threshold Z based on a curve fitting algorithm of a least square method i Early warning time in time;
the data management center stores a maintenance treatment database, and the maintenance treatment database stores health index maintenance thresholds and corresponding equipment maintenance treatment recommended worksheets; when the health index of the equipment i reaches a health index maintenance threshold, the data management center sends an equipment maintenance treatment suggestion work order to the PMS system, and the PMS system creates a treatment work order and a personnel task assignment; the maintenance treatment database also stores fault data of the equipment i and a corresponding fault troubleshooting treatment work order, if the fault alarm data of the equipment i in the basic database of the data management center has the corresponding fault data in the maintenance treatment database, the maintenance treatment database sends the fault troubleshooting treatment work order to the PMS system, and the PMS system creates the troubleshooting treatment work order and the personnel task assignment.
The utility model provides a big data operation and maintenance decision analysis device of track traffic which characterized in that: comprising the following steps:
at least one processor;
at least one memory for storing at least one program;
when the at least one program is executed by the at least one processor, the at least one processor is enabled to realize the rail transit big data operation and maintenance decision analysis method.
A storage medium having stored therein a processor-executable program which when executed by a processor is for performing the rail transit big data operation and maintenance decision analysis method of the present invention.
Compared with the prior art, the invention has the following advantages:
(1) According to the invention, the fault alarm data associated with the track traffic equipment are collected and stored according to the upper and lower hierarchical architecture, the health evaluation model is built in the data management center, and health index quantitative calculation is carried out on each equipment under the station, the line and the wire network, so that real-time accurate diagnosis of the equipment health degree data is realized to obtain health indexes, and further early warning time is accurately predicted, and then the health change trend of the station, the line and the wire network is obtained, thereby being convenient for guiding the state maintenance of the track traffic equipment.
(2) According to the invention, large data collection and classification of fault data are carried out, health indexes of all equipment are calculated, and the health report is summarized according to three levels of stations, lines and lines, so that the train is guided to be maintained in a timely state, the analysis data is comprehensive, the excavation depth is higher, and the method has the advantages of high efficiency, high accuracy, comprehensive analysis and the like.
(3) According to the method, fault data of the rail transit equipment are collected in real time, a health evaluation model is constructed, average non-fault interval time is calculated to obtain an average non-fault interval change curve, equipment health index diagnosis is conducted through the health evaluation model, and then a health index change trend is fitted and predicted through a curve fitting algorithm, so that early warning time can be predicted, and train state maintenance is guided.
Drawings
FIG. 1 is a schematic flow chart of a first embodiment of a rail transit big data operation and maintenance decision analysis method;
FIG. 2 is a flow chart of a second method S5 according to the embodiment of the invention;
FIG. 3 is a schematic flow chart of a method S6 according to an embodiment of the invention;
FIG. 4 is a schematic block diagram of a rail transit big data operation and maintenance decision analysis system according to the present invention;
FIG. 5 is a schematic diagram of a software architecture of a rail transit big data operation and maintenance decision analysis system according to an embodiment;
FIG. 6 is a schematic diagram of a rail transit big data operation and maintenance decision analysis method and system according to the present invention;
FIG. 7 is a diagram showing interface effects for health index in an embodiment;
FIG. 8 is a diagram of health index operation and maintenance advice interface effects according to an embodiment.
Detailed Description
The invention is further illustrated by the following examples:
example 1
As shown in fig. 1, the method for analyzing the operation and maintenance decision of the big data of the rail transit comprises the following steps:
s1, a perception layer comprises a rail traffic association basic database, wherein the rail traffic association basic database comprises a communication maintenance system, a signal maintenance system, a PMS system and an external input data document which are associated with rail traffic. The data acquisition of the track traffic equipment not only supports online access to the independent data acquisition center of each engineering line, but also can use a Binlog synchronization tool to carry out data synchronization, and supports offline EXCEL and TXT files to be put into storage through an import interface, so that the operation data of the track traffic equipment is excavated in all directions through multiple channels, and the accuracy of intelligent operation and maintenance analysis decision is improved.
In some embodiments, the data acquisition layer is used for respectively acquiring a communication maintenance system, a signal maintenance system and a PMS system through a terminal sensor, and the data acquisition layer is also used for realizing the input of external input data documents including excel and txt; the communication maintenance system comprises transmission, wireless, clock, broadcasting, PIS and monitoring data, the signal maintenance system comprises vehicle-mounted, turnout, power supply, ATS, interlocking, DCS and axle counting data, the PMS system comprises fault data, work order information and task personnel information, and the external input data file comprises equipment log, maintenance record and alarm information data.
S2, the data acquisition layer acquires fault alarm data and analog quantity data from the perception layer and transmits the fault alarm data and the analog quantity data to the data management center, and the data management center performs hierarchical membership and stores the hierarchical membership to the basic database from bottom to top according to equipment, stations, lines and wires.
S3, a health evaluation model is arranged in the data management center, and the health evaluation model carries out real-time health diagnosis according to the following method:
s31, sequentially arranging fault alarm data of the equipment i according to the operation time, and obtaining average fault-free interval time M based on an MTBF algorithm i With running time of equipment i, average no fault interval M i Obtaining an average fault-free interval change curve of the equipment i by fitting the abscissa and the ordinate, and obtaining a curvature coefficient C i ；
S32, respectively constructing an evaluation model corresponding to the equipment i, wherein the evaluation model of the equipment i adopts the following health evaluation relation formula:
wherein lambda is i For failure rate of device i, K i For the scaling factor corresponding to device i, C i For the curvature coefficient of the mean fault-free interval curve of the device i, H i Is a health index;
M i mean time between failure for the device;
the health evaluation model outputs health indexes in real time according to the running time of the equipment i as real-time diagnosis data of the equipment i;
s33, setting a health index early warning threshold Z of the equipment i i The health evaluation model calculates that the running state of the equipment i reaches the health index early warning threshold Z based on a curve fitting algorithm of a least square method i Early warning time in time.
The data acquisition layer and the data management center integrate, classify, screen, extract and analyze the equipment operation data based on the configurable health evaluation model to generate the equipment health evaluation result, wherein the health evaluation model is dynamically adjusted according to the equipment type, and the equipment health evaluation result is more accurate.
In some embodiments, the data management center stores a data cleaning algorithm, a turnout history position algorithm, a turnout action curve analysis algorithm, a data analysis mining algorithm, a vehicle-mounted MPBF algorithm, a power supply voltage current daily deviation algorithm and a subsystem intelligent matching algorithm; before the data management center stores the data into a basic database, performing data cleaning processing including deletion processing, duplication removal, illegal data removal and invalid data filtration on the data through a data cleaning algorithm; after the data cleaning process is completed, the data analysis mining algorithm classifies, extracts, integrates and integrates the data according to the parameter entering format specification of the health evaluation model and according to lines, stations and equipment, and forms model basic data to be stored in a basic database.
S4, a maintenance and treatment database is stored in the data management center, and a health index maintenance and maintenance threshold value and a corresponding equipment maintenance and treatment suggestion work order are stored in the maintenance and treatment database; when the health index of the equipment i reaches a health index maintenance threshold (when the equipment health evaluation result generated by the data management center is lower than a set threshold, the system automatically generates equipment maintenance advice, triggers work order creation and task assignment to the PMS system, guides the application and maintenance of equipment, and thus realizes the intelligent operation and maintenance goal of the rail transit equipment), the data management center sends the equipment maintenance advice work order to the PMS system, and the PMS system creates a disposal work order and personnel task assignment; the maintenance treatment database also stores fault data of the equipment i and a corresponding fault troubleshooting treatment work order, if the fault alarm data of the equipment i in the basic database of the data management center has the corresponding fault data in the maintenance treatment database, the maintenance treatment database sends the fault troubleshooting treatment work order to the PMS system, and the PMS system creates the troubleshooting treatment work order and the personnel task assignment.
Example two
The present embodiment includes the following methods in addition to S1 to S4 of the first embodiment:
as shown in fig. 2, S5, the data management center performs real-time diagnosis data and early warning time of the hierarchical architecture storage device i from bottom to top according to the device, station, line and wire network to form three-level summarized data of station level, line level and wire network level; the data management center and the PMS system respectively transmit data to a platform service layer, and the platform service layer is provided with a data visualization service unit which realizes data report forms and data visualization display at the equipment level, the station level, the line level and the line network level.
In some embodiments, in the method S5, the data management center integrates a line database according to the line classification, where the line database stores fault alarm data, real-time diagnosis data, data report and data summary of all devices under the affiliated line; each track traffic engineering line is independently provided with a corresponding data acquisition unit.
In some embodiments, in the method S5, the data visualization service unit implements a data report and a data visualization display according to a line network level, a line level and a station level, and as shown in fig. 7 and 8, the line network level display includes a full line network health report, early warning prediction and operation and maintenance decision data, the line level display includes a fault report, a health overview and line level operation and maintenance decision data, and the station level display includes health evaluation results, change trends, equipment early warning and maintenance suggestion data of specific equipment.
Example III
The present embodiment includes the following methods in addition to S1 to S5 of the second embodiment:
as shown in fig. 3, S6, the platform service layer is connected with a platform application layer, where the platform application layer has a data display configuration unit, a platform authority management unit, a health management large screen, and a health management background, the data display configuration unit is used for configuring data display, the platform authority management unit is used for inputting and managing authority settings of a platform user, the health management large screen is used for displaying a data report and visually displaying data, and the health management background is used for managing and storing data.
Example IV
As shown in fig. 4, a rail transit big data operation and maintenance decision analysis system includes:
the perception layer comprises a rail traffic association basic database, wherein the rail traffic association basic database comprises a communication maintenance system, a signal maintenance system, a PMS system and an external input data document which are associated with the rail traffic, and is used for linking the rail traffic association data;
the data acquisition layer is used for acquiring fault alarm data and analog quantity data from the sensing layer and transmitting the fault alarm data and the analog quantity data to the data management center;
the data management center is internally provided with a health evaluation model, the health evaluation model is used for calculating and obtaining average fault-free interval time and real-time diagnosis data of the equipment i, and the health evaluation model is provided with health index early warning of the equipment iThreshold Z i The health evaluation model calculates that the running state of the equipment i reaches the health index early warning threshold Z based on a curve fitting algorithm of a least square method i Early warning time in time;
the data management center stores a maintenance treatment database, and the maintenance treatment database stores health index maintenance thresholds and corresponding equipment maintenance treatment recommended worksheets; when the health index of the equipment i reaches a health index maintenance threshold, the data management center sends an equipment maintenance treatment suggestion work order to the PMS system, and the PMS system creates a treatment work order and a personnel task assignment; the maintenance treatment database also stores fault data of the equipment i and a corresponding fault troubleshooting treatment work order, if the fault alarm data of the equipment i in the basic database of the data management center has the corresponding fault data in the maintenance treatment database, the maintenance treatment database sends the fault troubleshooting treatment work order to the PMS system, and the PMS system creates the troubleshooting treatment work order and the personnel task assignment.
Fig. 5 shows a schematic diagram of a software functional architecture of a rail transit big data operation and maintenance decision analysis system, where a perception layer provides basic data support based on an existing communication maintenance system, a signal maintenance system, a PMS system and an external input data document. The data acquisition layer is mainly used for acquiring equipment information forwarded by the bottom layer signal subsystem of each route, and acquiring alarm and analog data of each route through a data grabbing tool binlog; the method supports the access of data of the PMS system through a standard interface protocol and the realization of offline data import such as excel, txt and the like through an import interface. After the data center layer receives the data provided by collection, the data are extracted, converted and loaded, the generated result data are written into a database, and the data result to be displayed is transmitted to the data platform service layer module in an interface mode. The platform service layer provides basic services such as parameter configuration, user management, authority management, data access, visual display and the like. The data display layer receives the model data of the data center layer, and stores the result as a report file, wherein the report file is formed every day; and simultaneously, the conclusive data result and the operation and maintenance suggestion are displayed in various visual graphs. The detailed functional principle is shown in fig. 6, and in order to improve the accuracy of intelligent operation and maintenance analysis decision, the rail transit big data operation and maintenance decision analysis system needs a large amount of acquisition equipment data. Each track traffic engineering line is provided with an independent data acquisition center, key information of a communication maintenance system, a signal maintenance system, a PMS system and external data is acquired through a terminal sensor, the communication maintenance system comprises transmission, wireless, clock, broadcasting, PIS, monitoring and other professions, the signal maintenance system comprises vehicle-mounted, turnout, power supply, ATS, interlocking, DCS, shaft counting and other professions, the PMS system comprises fault data and work order information, and the external data comprises equipment logs, maintenance records, alarm information and the like. The data management center is in butt joint with the acquisition module by adopting an HTTP protocol, and data transmission is carried out by using an API interface mode through a maintenance network; simultaneously supporting synchronous access modes among databases; the offline data file is put in storage through the import interface, and the encapsulated data is uploaded to a line database of the data management center. The data management center bears the data management function and is responsible for processing and storing the acquired data. For this purpose, the data management center is built with various data processing algorithms, including a data preprocessing algorithm and various professional data analysis mining algorithms, such as a data cleaning algorithm, a switch history position algorithm, a switch action curve analysis algorithm, a vehicle-mounted MPBF algorithm, a power supply voltage current day deviation algorithm, a subsystem intelligent matching algorithm, and the like. After the data uploaded by each engineering line acquisition module are collected, the data management center starts to clean the data. And performing deletion processing, duplication removal, illegal data removal, invalid data filtration and the like on the data by utilizing a data cleaning algorithm. After the cleaning is finished, the data analysis mining algorithm classifies, extracts and integrates the data according to the parameter entering format specification of the health evaluation model and lines, stations and equipment to form model basic data, and the model basic data is stored in a basic database. And each specialty combines collected data mining information, maintenance experience, manufacturer design parameters and field expert opinions to formulate various equipment health degree thresholds of the health evaluation model. And the health evaluation model carries out health evaluation and fault prediction on the communication signal equipment according to model basic data generated by the data management center, and the diagnosis result is stored in a result database of the data management center.
The health management platform of the rail transit big data operation and maintenance decision analysis system is provided with a treatment suggestion library, the treatment suggestions come from each professional history maintenance experience, manufacturer maintenance manuals, communication signal fault diagnosis specialists and the like, and the treatment suggestion library can be continuously optimized and supplemented according to practical results. And the health management platform makes analysis decisions such as maintenance suggestions, disposal suggestions and the like according to the current health evaluation and fault prediction of the equipment, and guides the equipment to be maintained and overhauled. And generating device treatment suggestions according to preset health thresholds of various devices, and triggering work order creation and task assignment to the PMS system. The rail transit big data operation and maintenance decision analysis system generates analysis decisions according to the diagnosis results of the health evaluation model and the health management platform, and generates various customized reports for display. The display data scale is divided into three layers, namely a wire network level, a line level and a station level. The wire network level display terminal focuses more on the health diagnosis results of the whole wire network communication signal equipment, including whole wire network health report forms, early warning prediction and operation and maintenance decisions. As shown in fig. 7 and 8, the line-level presentation terminal focuses on the current line health status, including fault reporting, health overview, and line-level operation and maintenance decisions. The station-level display terminal focuses on the equipment conditions of the current station, including health evaluation results, change trends, equipment early warning and maintenance suggestions of specific equipment.
A rail transit big data operation and maintenance decision analysis device, comprising:
at least one processor;
at least one memory for storing at least one program;
when at least one program is executed by at least one processor, the at least one processor implements the steps of the rail transit big data operation and maintenance decision analysis method described in the first embodiment, the second embodiment, or the third embodiment.
A storage medium having stored therein a processor-executable program, wherein the processor-executable program when executed by a processor is configured to perform the steps of the rail transit big data operation and maintenance decision analysis method described in the first embodiment, the second embodiment, or the third embodiment.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (10)
1. A rail transit big data operation and maintenance decision analysis method is characterized in that: the method comprises the following steps:
s1, a perception layer comprises a rail traffic association basic database, wherein the rail traffic association basic database comprises a communication maintenance system, a signal maintenance system, a PMS system and an external input data document which are associated with rail traffic;
s2, the data acquisition layer acquires fault alarm data and analog quantity data from the sensing layer and transmits the fault alarm data and analog quantity data to the data management center, and the data management center performs hierarchical membership from bottom to top according to equipment, stations, lines and nets and stores the hierarchical membership into the basic database;
s3, a health evaluation model is arranged in the data management center, and the health evaluation model carries out real-time health diagnosis according to the following method:
s31, sequentially arranging fault alarm data of the equipment i according to the operation time, and obtaining average fault-free interval time M based on an MTBF algorithm i With running time of equipment i, average no fault interval M i Obtaining an average fault-free interval change curve of the equipment i by fitting the abscissa and the ordinate, and obtaining a curvature coefficient C i ；
S32, respectively constructing an evaluation model corresponding to the equipment i, wherein the evaluation model of the equipment i adopts the following health evaluation relation formula:
wherein lambda is i For failure rate of device i, K i For the scaling factor corresponding to device i, C i For the curvature coefficient of the mean fault-free interval curve of the device i, H i Is a health index;
M i mean time between failure for the device;
the health evaluation model outputs health indexes in real time according to the running time of the equipment i as real-time diagnosis data of the equipment i;
s33, setting a health index early warning threshold Z of the equipment i i The health evaluation model calculates that the running state of the equipment i reaches the health index early warning threshold Z based on a curve fitting algorithm of a least square method i Early warning time in time;
s4, a maintenance and treatment database is stored in the data management center, and a health index maintenance and maintenance threshold value and a corresponding equipment maintenance and treatment suggestion work order are stored in the maintenance and treatment database; when the health index of the equipment i reaches a health index maintenance threshold, the data management center sends an equipment maintenance treatment suggestion work order to the PMS system, and the PMS system creates a treatment work order and a personnel task assignment; the maintenance treatment database also stores fault data of the equipment i and a corresponding fault troubleshooting treatment work order, if the fault alarm data of the equipment i in the basic database of the data management center has the corresponding fault data in the maintenance treatment database, the maintenance treatment database sends the fault troubleshooting treatment work order to the PMS system, and the PMS system creates the troubleshooting treatment work order and the personnel task assignment.
2. The rail transit big data operation and maintenance decision analysis method according to claim 1, wherein: the method also comprises the following steps:
s5, the data management center stores real-time diagnosis data and early warning time of the equipment i in a hierarchical architecture from bottom to top according to equipment, stations, lines and wire networks to form summarized data of three levels of station level, line level and wire network level; the data management center and the PMS system respectively transmit data to a platform service layer, and the platform service layer is provided with a data visualization service unit which realizes data report forms and data visualization display at the equipment level, the station level, the line level and the line network level.
3. The rail transit big data operation and maintenance decision analysis method according to claim 2, wherein: the method also comprises the following steps:
s6, the platform service layer is connected with a platform application layer, the platform application layer is provided with a data display configuration unit, a platform authority management unit, a health management large screen and a health management background, the data display configuration unit is used for configuring data display, the platform authority management unit is used for recording and authority setting management of a platform user, the health management large screen is used for displaying a data report and data visual display, and the health management background is used for managing and storing data.
4. The rail transit big data operation and maintenance decision analysis method according to claim 2, wherein: in the method S5, the data management center integrates a line database according to line classification, and the line database stores fault alarm data, real-time diagnosis data, data report forms and data summarization of all devices under affiliated lines; each track traffic engineering line is independently provided with a corresponding data acquisition unit.
5. The rail transit big data operation and maintenance decision analysis method according to claim 1, wherein: the data acquisition layer is used for respectively acquiring a communication maintenance system, a signal maintenance system and a PMS system through a terminal sensor, and is also used for realizing the input of external input data documents including excel and txt; the communication maintenance system comprises transmission, wireless, clock, broadcasting, PIS and monitoring data, the signal maintenance system comprises vehicle-mounted, turnout, power supply, ATS, interlocking, DCS and axle counting data, the PMS system comprises fault data, work order information and task personnel information, and the external input data file comprises equipment log, maintenance record and alarm information data.
6. The rail transit big data operation and maintenance decision analysis method according to claim 1, wherein: the data management center is internally provided with a data cleaning algorithm, a turnout historical position algorithm, a turnout action curve analysis algorithm, a data analysis mining algorithm, a vehicle-mounted MPBF algorithm, a power supply voltage current daily deviation algorithm and a subsystem intelligent matching algorithm; before the data management center stores the data into a basic database, performing data cleaning processing including deletion processing, duplication removal, illegal data removal and invalid data filtration on the data through a data cleaning algorithm; after the data cleaning process is completed, the data analysis mining algorithm classifies, extracts, integrates and integrates the data according to the parameter entering format specification of the health evaluation model and according to lines, stations and equipment, and forms model basic data to be stored in a basic database.
7. The rail transit big data operation and maintenance decision analysis method according to claim 2, wherein: in the method S5, the data visualization service unit realizes data report forms and data visualization display according to line network level, line level and station level, wherein the line network level display comprises a full line network health report form, early warning prediction and operation and maintenance decision data, the line level display comprises a fault report form, a health overview and line level operation and maintenance decision data, and the station level display comprises health evaluation results, change trends, equipment early warning and maintenance suggestion data of specific equipment.
8. The rail transit big data operation and maintenance decision analysis system is characterized in that: comprising the following steps:
the perception layer comprises a rail traffic association basic database, wherein the rail traffic association basic database comprises a communication maintenance system, a signal maintenance system, a PMS system and an external input data document which are associated with the rail traffic, and is used for linking the rail traffic association data;
the data acquisition layer is used for acquiring fault alarm data and analog quantity data from the sensing layer and transmitting the fault alarm data and the analog quantity data to the data management center;
the data management center is internally provided with a health evaluation model, the health evaluation model is used for calculating and obtaining average fault-free interval time and real-time diagnosis data of the equipment i, and the health evaluation model is provided with a health index early warning threshold Z of the equipment i i Health evaluationCurve fitting algorithm calculation equipment i running state based on least square method of price model reaches health index early warning threshold Z i Early warning time in time;
the data management center stores a maintenance treatment database, and the maintenance treatment database stores health index maintenance thresholds and corresponding equipment maintenance treatment recommended worksheets; when the health index of the equipment i reaches a health index maintenance threshold, the data management center sends an equipment maintenance treatment suggestion work order to the PMS system, and the PMS system creates a treatment work order and a personnel task assignment; the maintenance treatment database also stores fault data of the equipment i and a corresponding fault troubleshooting treatment work order, if the fault alarm data of the equipment i in the basic database of the data management center has the corresponding fault data in the maintenance treatment database, the maintenance treatment database sends the fault troubleshooting treatment work order to the PMS system, and the PMS system creates the troubleshooting treatment work order and the personnel task assignment.
9. The utility model provides a big data operation and maintenance decision analysis device of track traffic which characterized in that: comprising the following steps:
at least one processor;
at least one memory for storing at least one program;
when executed by at least one processor, causes the at least one processor to implement the method of any one of claims 1-7.
10. A storage medium having stored therein a processor executable program, wherein the processor executable program when executed by a processor is for performing the method of any of claims 1-7.
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