CN115848463A - Intelligent operation and maintenance system and method - Google Patents
Intelligent operation and maintenance system and method Download PDFInfo
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- CN115848463A CN115848463A CN202211477745.8A CN202211477745A CN115848463A CN 115848463 A CN115848463 A CN 115848463A CN 202211477745 A CN202211477745 A CN 202211477745A CN 115848463 A CN115848463 A CN 115848463A
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Abstract
The invention provides an intelligent operation and maintenance system and a method, wherein the intelligent operation and maintenance system comprises: the data acquisition module is respectively connected with a plurality of sub-devices of the rail transit signal device through network ports and is used for acquiring state data respectively generated by the plurality of sub-devices; the big data platform is electrically connected with the data acquisition module and is used for converting the format of the state data into a target format to obtain target state data; the data diagnosis module is electrically connected with the big data platform and is used for acquiring a diagnosis result corresponding to the target state data; and the service application module is electrically connected with the data diagnosis module and is used for matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result. The system provided by the invention improves the monitoring range of the intelligent operation and maintenance system on the signal system and improves the operation and maintenance efficiency of the signal system.
Description
Technical Field
The invention relates to the technical field of intelligent operation and maintenance of rail transit, in particular to an intelligent operation and maintenance system and method.
Background
The rail transit intelligent operation and maintenance system is a management system for daily maintenance, fault diagnosis and fault processing capacity of rail transit operation.
In the related art, when a Maintenance Support Subsystem (MSS) collects Maintenance data of a rail transit signal System according to an existing data access standard, the collected Maintenance data is insufficient, and all subsystems of the rail transit signal System cannot be monitored comprehensively, and usually, a manual troubleshooting manner is used to check the fault state and the equipment loss degree of equipment at regular time, and the loss of faulty equipment, the fault reason of the equipment and an operation and Maintenance scheme thereof cannot be obtained in time, so that the operation and Maintenance efficiency of all subsystems of the signal System is low.
Disclosure of Invention
The invention provides an intelligent operation and maintenance system and method, which are used for solving the defects that in the prior art, the maintenance data acquired by the intelligent operation and maintenance system is insufficient, and the fault state and the equipment loss degree of equipment are regularly checked in a manual troubleshooting mode, so that the equipment fault is not maintained timely, and the monitoring range and the operation and maintenance efficiency of a signal system are improved.
The invention provides an intelligent operation and maintenance system, which comprises:
the data acquisition module is respectively connected with a plurality of sub-devices of the rail transit signal device through network ports and is used for acquiring state data generated by the sub-devices;
the big data platform is electrically connected with the data acquisition module and is used for converting the format of the state data into a target format to obtain target state data;
the data diagnosis module is electrically connected with the big data platform and used for acquiring a diagnosis result corresponding to the target state data based on alarm information under the condition that the target state data comprises the alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information and a life prediction value of a sub-device corresponding to the target state data;
the service application module is electrically connected with the data diagnosis module and used for matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, and the knowledge base is determined based on the existing fault data and the operation and maintenance scheme corresponding to the existing fault data.
According to an intelligent operation and maintenance system provided by the invention, the data diagnosis model comprises:
the fault diagnosis model is used for clustering the alarm information to obtain a fault type corresponding to the alarm information;
and the equipment full life cycle management model is used for obtaining a life prediction value of the sub-equipment corresponding to the target state data based on the delivery parameters and the service life of the sub-equipment corresponding to the target state data.
According to the intelligent operation and maintenance system provided by the invention, the state data comprises an offline document and real-time information, and the big data platform comprises:
a batching component, the batching layer to convert the format of the offline document to the target format;
a stream processing component, the batch layer for converting the format of the real-time information into the target format.
According to the intelligent operation and maintenance system provided by the invention, the big data platform further comprises:
a storage component to store the target state data.
According to the intelligent operation and maintenance system provided by the invention, the service application module further comprises:
and the display screen is used for displaying the diagnosis result and the operation and maintenance scheme.
According to the intelligent operation and maintenance system provided by the invention, the system further comprises:
and the deployment component is used for carrying out cluster deployment on the big data platform.
The invention also provides an intelligent operation and maintenance method, which comprises the following steps:
collecting state data generated by a plurality of sub-devices of the rail transit signal device;
converting the format of the state data into a target format to obtain target state data;
under the condition that the target state data comprises alarm information, acquiring a diagnosis result corresponding to the target state data based on the alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information and a life prediction value of a target sub-device corresponding to the target state data;
and matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, wherein the knowledge base is determined based on the operation and maintenance scheme corresponding to the existing rail transit equipment fault type.
According to the intelligent operation and maintenance method provided by the invention, after the operation and maintenance scheme corresponding to the diagnosis result is obtained, the method further comprises the following steps:
and displaying the diagnosis result and the operation and maintenance scheme on a display screen.
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 intelligent operation and maintenance method.
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 intelligent operation and maintenance method as described in any of the above.
The present invention also provides a computer program product, including a computer program, where the computer program is executed by a processor to implement any one of the above-mentioned intelligent operation and maintenance methods.
According to the intelligent operation and maintenance system and the intelligent operation and maintenance method, the monitoring range of the intelligent operation and maintenance system on the signal system is enlarged by collecting the state data of the plurality of sub-devices of the rail transit signal system to perform data analysis, and the target state data of different devices are subjected to fault prediction and device service life prediction through the diagnosis model, so that the problem of low efficiency of manual troubleshooting of the device is solved, and the operation and maintenance efficiency of the signal system is improved.
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 structural diagram of an intelligent operation and maintenance system provided by the present invention;
FIG. 2 is a schematic diagram of data transceiving interaction of a data receiving module provided in the present invention;
FIG. 3 is a flow chart of a fault diagnosis method based on a turnout switch machine model provided by the invention;
fig. 4 is a second schematic structural diagram of the intelligent operation and maintenance system provided in the present invention;
FIG. 5 is a schematic flow chart of an alarm validity analysis method provided by the present invention;
FIG. 6 is an interaction diagram of a data receiving module and a big data platform provided by the present invention;
FIG. 7 is a schematic structural diagram of cluster software provided by the present invention;
FIG. 8 is a schematic flow chart of an intelligent operation and maintenance method provided by the present invention;
fig. 9 is a schematic structural diagram of an electronic device provided by the present invention.
Reference numerals:
110: a data acquisition module; 120: a big data platform; 130: a data diagnosis module;
140: and a service application module.
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.
In the description of the embodiments of the present invention, it should be noted that, unless explicitly stated or limited otherwise, the terms "connected" and "connected" are to be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; the connection can be mechanical connection, electrical connection, wired communication connection or wireless communication connection; may be directly connected or indirectly connected through an intermediate. Specific meanings of the above terms in the embodiments of the present invention can be understood in specific cases by those of ordinary skill in the art.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the embodiments of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The intelligent operation and maintenance system and method of the present invention are described below with reference to fig. 1-8.
Fig. 1 is a schematic structural diagram of an intelligent operation and maintenance system provided by the present invention, and as shown in fig. 1, the present invention provides an intelligent operation and maintenance system, including: a data acquisition module 110, a big data platform 120, a data diagnosis module 130, and a business application module 140.
The data acquisition module 110 is connected to a plurality of sub-devices of the rail transit signal device through a network port, and the data acquisition module 110 is configured to acquire status data generated by the plurality of sub-devices.
In the embodiment, the state data generated by the plurality of sub-devices form multi-source heterogeneous data, and the formats of the data are not uniform.
In this embodiment, the data collection function may be implemented by a deployed maintenance network switch, each subsystem of the rail transit is connected to a switch dedicated to the intelligent operation and maintenance management system through a network interface, and data is transmitted to the maintenance workstation and each intelligent operation and maintenance big data full-life-cycle management server through the switch.
In some embodiments, the plurality of sub-devices include an on-board ATP (Automatic Train Protection system), an ATS (Automatic Train Supervision), a ZC (Zone Controller), a CI (Computer Interlocking), a data communication system, and other interface and device data collection standards and interface standards with intelligent operation and maintenance.
In this embodiment, the heterogeneous data generated by the vehicle-mounted System includes vehicle-mounted board card status, hardware version information, marshalling information, cabinet status information, and locomotive Control and Management System (TCMS) interface information, etc.; heterogeneous data generated by the ATS system comprises equipment hardware state, database state, data packet capturing, data link node monitoring and the like; heterogeneous data generated by a ZC system of the zone controller comprises a ZC board card state, a zone controller running state, an industrial personal computer working state and the like; the heterogeneous data generated by the interlocking system comprises an interlocking control display working state, a relay state and the like; the heterogeneous data generated by the DCS data communication system comprises wireless field intensity, leaky cables, antenna feeder cables and the like.
Fig. 2 is an interaction schematic diagram of data receiving and sending of a data receiving module provided by the present invention, in the embodiment shown in fig. 2, information sent by a vehicle-mounted ATP device is transmitted to a wireless network through a network interface, and then is sent to a ground network management server through the network interface by the wireless network (e.g., TRU), and the intelligent operation and maintenance system and the ground network management server obtain multi-source heterogeneous data (device status information) through the network interface; the device state information sent by a drive radio access (DTO) of the train on duty is sent to an ATP through a network interface, then the ATP information is transmitted to a ground network management server through a process, and the intelligent operation and maintenance system and the ground network management server acquire information through the network interface.
In the embodiment, the equipment state information sent by the transponder transmission unit BTM is sent to the ATP through the CAN bus, then the ATP information is transmitted to the ground network management server through the process, and the intelligent operation and maintenance system and the ground network management server acquire the information through a network interface; the equipment state information sent by the TCMS equipment is sent to the ATP through a multifunctional vehicle bus MVB (a non-MVB can be sent to a DTO through 485 communication and then transferred to the ATP), then the ATP information is transmitted to a ground network management server through a process, and the intelligent operation and maintenance system and the ground network management server acquire information through a network interface; the equipment state information sent by the human-computer interaction interface HMI is sent to the ATP through the CAN bus, then the ATP information is transmitted to the ground network management server through the process, and the intelligent operation and maintenance system and the ground network management server acquire the information through the network interface.
The big data platform 120 is electrically connected to the data acquisition module 110, and the big data platform 120 is configured to convert the format of the state data into a target format to obtain target state data.
In some embodiments, the big data platform 120 layer has functions of processing, analyzing, storing, and the like.
In this embodiment, the multi-source heterogeneous data sent to the big data platform 120 by each subsystem of the signal processing system is processed by a data synchronization platform (for example, an NIFI tool, the background of which has components such as a data processing engine and task scheduling), so as to implement operations such as distributing, pulling and the like on the multi-source heterogeneous data.
It will be appreciated that the big data platform 120 is configured to receive status data sent by various subsystems of the rail transit signal system, and the status data needs to be managed in groups in order to quickly determine a faulty device.
In this embodiment, the grouping may be different subsystem types, for example, the big data platform 120 may classify the status data by transmission through middleware (Kafka) and provide log information of each heterogeneous data for easy viewing or management.
In some embodiments, the data formats contained in the multi-source heterogeneous data are not uniform, and the format of the heterogeneous data of each group is converted into a target format, so that the multi-source heterogeneous data can be uniformly managed.
In this embodiment, the target format can be customized according to the user's requirements.
In the embodiment, after the data format of the grouped multi-source heterogeneous data is converted into the target format, the fault prediction can be performed on the heterogeneous data from different equipment sources, and the equipment corresponding to the fault is maintained according to the prediction result.
The data diagnosis module 130 is electrically connected with the big data platform 120, and the data diagnosis module 130 is configured to, when the target state data includes alarm information, obtain a diagnosis result corresponding to the target state data based on the alarm information, where the diagnosis result includes a fault type corresponding to the alarm information and a predicted life value of a sub-device corresponding to the target state data.
In this embodiment, the data diagnosis module 130 includes a plurality of diagnosis models, for example, a speed radar data model, for performing fault analysis on heterogeneous data generated by a speed radar; the data diagnostic module 130 also includes a device full lifecycle management model for analyzing and counting consumption of faulty devices.
The following description will be given taking an example in which the data diagnosis module 130 includes a turnout switch machine model.
Fig. 3 is a flow schematic of a fault diagnosis method based on a turnout switch machine model provided by the invention, in the embodiment shown in fig. 3, a data acquisition module sends acquired power data of turnout equipment to a big data platform for classification and unifies a data format to obtain target power data, wherein the power data comprises alarming information that a turnout is in fault, after the target turnout data is input to a data diagnosis module, the turnout switch machine model carries out abnormal state recognition on the target turnout data to obtain a prediction result, and when the prediction result is matched with the alarming information, the turnout fault can be determined.
In this embodiment, the switch power curve characteristics and possible failure causes are shown in table 1 below:
TABLE 1 Turnout power curve characteristic and possible fault reason comparison table
| Characteristic of power curve | Possible causes of failure | |
| 1 | The power curve is not 0 after the turnout is turned | DBQ failure |
| 2 | Abnormal fluctuation of power curve in non-operating state | Diode damage |
| 3 | Power curve large lift in locking stage | The action rod has foreign matter or jamming |
| 4 | Abnormal fluctuation of power curve in turnout conversion stage | The rod of the turnout is loosened or not installed in place |
| 5 | Peak point shift of switch power curve | Too tight a tight contact adjustment and inconsistent motion of the traction point |
| 6 | Power curve is maintained after small increase in locking stage | The creeping of the switch rail is affected and the gap needs to be readjusted |
In this embodiment, the manner of diagnosing the model may be: the fault data stored in the big data platform 120 is used as a training sample, the training sample is subjected to operations such as feature extraction, feature selection and feature fusion to obtain key health features, the key health features are marked, and finally the marked key health features are used for training a prediction model to obtain an available diagnosis model.
In some embodiments, after the turnout fault is determined, the equipment full-life-cycle management model can be used for inquiring the factory parameters of the turnout, so that the service life and the consumption of the turnout are counted, and the remaining service life of the turnout is predicted.
The service application module 140 is electrically connected to the data diagnosis module 130, the service application module 140 is configured to match the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, and the knowledge base is determined based on the existing fault data and the operation and maintenance scheme corresponding to the existing fault data.
In this embodiment, the knowledge base may be created based on a preset knowledge graph.
In this embodiment, various data in the knowledge graph are stored in the form of entities, attributes and relationships, that is, the knowledge graph can represent names and corresponding information of the data, and also marks the relationships among different data.
In this embodiment, the knowledge base further includes the equipment faults that have occurred in the rail transit signal system and the corresponding operation and maintenance scheme.
In the embodiment, when the intelligent operation and maintenance system detects that the equipment has a fault, the intelligent operation and maintenance system searches in the knowledge graph according to fault information to perform fault linkage, and gives an alarm to field operation and maintenance personnel according to the expert information and the fault handling scheme stored in the knowledge graph, and provides a solution.
In this embodiment, the business application module 140 also provides functions such as fault visualization display and fault alarm.
Fig. 4 is a second schematic structural diagram of the intelligent operation and maintenance system provided by the present invention, in the embodiment shown in fig. 4, the server constitutes a big data platform, each subsystem of the rail transit is connected to the maintenance network switch through a communication interface, each subsystem sends data to the switch, when the subsystem is in an abnormal state, the server sends alarm information to the switch, and sends normal information at ordinary times, the maintenance network switch sends the data to the intelligent operation and maintenance management system through a firewall, and the data is transmitted to the maintenance workstation and each intelligent operation and maintenance big data full-life-cycle management server through the switch; the data are analyzed, analyzed and processed on the server in a cluster management mode, maintenance suggestions, fault prediction and various kinds of fault information are provided for operation and maintenance personnel through knowledge map query, and maintenance and troubleshooting of rail transit operation and maintenance personnel are facilitated.
In this embodiment, the switch portion is primarily used for data switching and distribution. Signals sent from each track traffic subsystem (ZC, chain, vehicle mounted, etc.) are first distributed via a common exchange. And sending the data to an intelligent operation and maintenance optical switch, a remote board-jumping machine and a dangerous machine detection station machine. When the data is alarmed, the monitoring station can carry out on-site alarm through the alarm lamp filament and inform operation and maintenance personnel; the remote board-jumping machine is responsible for remote debugging of developers; the intelligent operation and maintenance optical switch is responsible for sending data to each intelligent operation and maintenance server; the optical switch uses optical fibers for communication, so that the bandwidth is larger, the data sending speed is higher, and the intelligent operation and maintenance requirements can be met.
In this embodiment, the intelligent operation and maintenance server is mainly divided into a big data platform 120 data node, a Web server node and a big data platform 120 master node, wherein the big data platform 120 data node stores information of the vehicle, such as vehicle speed, position, and vehicle-mounted relevant state, and the information is analyzed on the big data platform 120 data node and then transmitted to the Web node for display; the Web node is mainly responsible for network and display, and can integrate, screen and display the data node information on a webpage; the big data platform 120 master node is mainly used for computing and storing the knowledge-graph and the relevant parts of the rail transit equipment full-life-cycle management.
According to the intelligent operation and maintenance system provided by the embodiment of the invention, the monitoring range of the intelligent operation and maintenance system on the signal system is improved by collecting the state data of the plurality of sub-devices of the rail transit signal system to perform data analysis, and the target state data of different devices are subjected to fault prediction and device service life prediction through the diagnosis model, so that the problems of low efficiency of manual inspection and inaccurate inspection result are solved, and the operation and maintenance efficiency on the signal system is improved.
In some embodiments, the data diagnostic module comprises: the fault diagnosis model is used for clustering the alarm information to obtain a fault type corresponding to the alarm information; and the equipment full life cycle management model is used for obtaining a life prediction value of the equipment corresponding to the target state data based on the delivery parameters and the service duration of the sub-equipment corresponding to the target state data.
In this embodiment, the fault diagnosis model may be one or more of a speed radar data model, an on-board emergency braking model, a turnout switch machine model, or a model for fault diagnosis of other rail transit equipment,
in this embodiment, the fault diagnosis model is used to perform clustering processing on the target state data to obtain a fault prediction result, and the fault prediction result is matched with alarm information included in the target state data, and if the fault prediction result and the alarm information are consistent, it is determined that the equipment corresponding to the target state data has a fault.
The following description will take an example of performing alarm validity analysis on target state data by using a fault diagnosis model as a speed measuring radar data model.
It should be noted that, the speed measuring radar may have a long failure time due to a complex working environment, and may also be recovered quickly after an abnormality occurs. When the train slips in an idling way, the speed measuring radar fails to work, so that the train is degraded, and the alarm of the degradation of the train is caused by the slipping of the wheel pair and the failure of the speed measuring radar.
Fig. 5 is a schematic flow chart of the alarm effectiveness analysis method provided by the present invention, in the embodiment shown in fig. 5, the alarm information is analyzed for effectiveness through the speed measurement radar data model to confirm that the speed measurement radar fails, and then the train idle slip frequency and the train degradation frequency are analyzed in combination to obtain the alarm information for predicting the speed measurement radar failure, which may be a false alarm caused by disturbance or a fault of radar equipment, and the alarm effectiveness analysis method performs the alarm optimization processing on the speed measurement radar data model according to the possible result of the speed measurement radar.
In the embodiment, aiming at the characteristics of the speed measuring radar, the alarm effectiveness analysis can be carried out by utilizing a method of combining two-layer filtering and notification judgment, and the specific method is that when the data at the train end is effective and the states of continuously acquiring the speed measuring radar for 10 times are consistent, the speed measuring radar is considered to be in the state, namely, when the speed measuring radar is continuously acquired for 10 times and is normal, the speed measuring radar is considered to be normal, when the speed measuring radar is continuously acquired for 10 times and is abnormal, and if the speed measuring radar is continuously acquired for 10 times and is normal and also has an abnormal state, the state is considered to be unchanged; when the abnormal state appears 3 times or more in a day or the abnormal state is kept for half an hour or more, the equipment maintenance is considered to be necessary, the system sends out an alarm notice to prompt maintenance personnel to check the appearance and the electrical connection of the speed measuring radar in time.
In the embodiment, after the alarm notification is triggered, the alarm is only reported according to the repeated times of the alarm notification on the day, the alarm is not repeatedly triggered according to the number or timing, if the alarm is completely cleared on the next day, if the alarm has a problem on the next day, the previous process is repeated, the mode does not directly use the state of the equipment as the alarm setting, the situation that the alarm information appears for many times when the repeated state changes is avoided, meanwhile, the abnormal times are counted, the abnormal duration is timed, the alarm number is further reduced, and the number of the daily alarms is restrained, so that the influence of the repeated alarm on the judgment is reduced.
In this embodiment, the model parameters related to the fault diagnosis model are all configurable, and when the fault diagnosis model is applied to other alarm notification services, the parameters can be adjusted to appropriate values according to different service requirements.
In some embodiments, the diagnostic model further includes a device full lifecycle management model for obtaining a life prediction value of the device corresponding to the target state data.
In this embodiment, the diagnosis result of the target state data may be used to analyze the full life cycle of the product through the device full life cycle management model, and select a manufacturer and a device with high relative vulnerability, and further use the device of the manufacturer with stable and reliable device, thereby reducing the device loss cost.
In this embodiment, the full life cycle management of the device in the intelligent operation and maintenance system can trace the source according to the device with the problem, find the information such as the device model and the supplier, and analyze the utilization rate, stability and reliability of the device.
According to the intelligent operation and maintenance system provided by the embodiment of the invention, the fault diagnosis model is set to carry out fault prediction on the target state data to confirm the fault reason of the equipment, and the full life cycle management model of the equipment is set to obtain the service life of the fault equipment, so that the troubleshooting efficiency of the equipment fault of the signal system is improved, and the operation and maintenance cost is reduced.
In some embodiments, the state data includes offline documents and real-time information, and the big data platform includes: the batch processing component is used for converting the format of the offline document into a target format by the batch processing layer; and the batch processing layer is used for converting the format of the real-time information into a target format.
In this embodiment, the data collected by the data collection module may be locally stored offline documents, or may be real-time information generated by normal operation of the signal system.
Fig. 6 is an interaction diagram of a data receiving module and a big data platform provided by the present invention, in the embodiment shown in fig. 6, an offline document is delivered to a batch processing component Spark of the big data platform through an NIFI or minifi interface to perform operations such as classification and unified format, and real-time information is sent to a middleware through the NIFI and a service system interface (NIFI and Kafka are distributed message queues, which can be applied to a big data real-time data processing portion), and the middleware processes and distributes the data and delivers the data to a stream processing component to perform operations such as classification and unified format.
In some embodiments, the big data platform further comprises a security component, wherein the security component is used for performing security protection on the data acquisition part, the data service part (data cleaning and conversion part) and the upper application part of the big data platform, and the security management system of the big data platform can be used for setting access authority for a user, so that the general user cannot change data in the big data service system and only has the access authority.
In the embodiment, when the big data platform communicates with the data acquisition module, a firewall (corresponding security component) is added, so that the security and reliability of data are protected.
According to the intelligent operation and maintenance system provided by the embodiment of the invention, the batch processing component and the stream processing component are arranged on the big data platform to classify and convert the format of the local offline document and the real-time information respectively, so that the big data platform can process various heterogeneous data, and the applicability and the safety of the big data platform are improved.
In some embodiments, the big data platform further comprises: and the storage component is used for storing the target state data.
In the embodiment, after the data are classified and converted into formats, the obtained target state data can be stored, so that a user can conveniently inquire and call the data on a visual interface of the intelligent operation and maintenance system.
In some embodiments, the storage component is further configured to store alarm information in the target state data, and when the intelligent operation and maintenance system stores enough fault alarm information, statistics on alarms, checking alarm positions, alarm solutions, alarm contents, and the like are performed, statistics and sequencing are performed on the alarms, and positions and devices which are easy to go wrong are found for targeted optimization and troubleshooting.
In the embodiment shown in fig. 6, the big data platform further includes multiple databases, and the non-relational database storage is performed by using Redis, the graph database storage is performed by Neo4j, the relational database storage is performed by Mysql, and the data analysis is performed by using Hive. Spark is used for streaming, batch and memory calculations, for data storage and recall.
According to the intelligent operation and maintenance system provided by the embodiment of the invention, the storage module is arranged on the big data platform and is used for storing the target state data and the corresponding alarm information, so that a user can conveniently inquire and call data on a visual interface of the intelligent operation and maintenance system.
In some embodiments, the business application module further comprises: and the display screen is used for displaying the diagnosis result and the operation and maintenance scheme.
In this embodiment, when the intelligent operation and maintenance system diagnoses the target state data, when the diagnosis result is a fault type which has occurred historically, the diagnosis result can be processed through a fault diagnosis scheme stored historically, and the diagnosis result and the processing mode are visually displayed on the display screen, so that the operation and maintenance personnel can guide the field device to perform maintenance operation according to the prompt content of the display screen.
In this embodiment, when the diagnosis result is a new fault type for which there is no existing diagnosis solution, the diagnosis result may be sent to be visualized for analysis and processing by the operation and maintenance personnel.
In this embodiment, the display screen may be a cell phone video capture and beautification tool, such as a VUE.
In this embodiment, the back end of the intelligent operation and maintenance system is built by a Spring-boot frame, the front end is displayed by the VUE, and the Spring-boot frame can perform operations such as authority management and data processing on data stored in the database of the big data platform 120, and convert the data into a format usable by the VUE; the VUE builds a display interface at the front end, wherein the display interface comprises shiro verification, train operation information, equipment states of all train components, a knowledge graph display and fault handling interface and the like; the rear-end spring-boot structure is responsible for reading related data from each database, analyzing knowledge map data through a TransE algorithm to search and predict, and finally transmitting the knowledge map data to the front end to display the data by the front end.
According to the intelligent operation and maintenance system provided by the embodiment of the invention, the diagnosis result is visually displayed, so that operation and maintenance personnel can conveniently execute operation and maintenance operation in time according to the prompt content of the display screen, the operation and maintenance efficiency of the intelligent system is improved, meanwhile, the maintenance time can be reduced, and the time cost and the labor cost can be saved.
In some embodiments, the system further comprises: and the deployment component is used for carrying out cluster deployment on the big data platform.
It can be understood that the clustered software deployment is adopted for the big data platform in the intelligent operation and maintenance system, and the deployment software can be automatically restarted and recovered when the big data platform fails, so that the operation of the whole system is not influenced.
In this example, the deployment was done using the ElasticSearch and Cloudera Manager software.
Fig. 7 is a schematic structural diagram of cluster software provided in the present invention, and in the embodiment shown in fig. 7, the cluster software may be divided into: the system comprises an acquisition system cluster, a message queue cluster, a Hadoop cluster, a computation cluster and a relational database cluster, wherein a flexible deployment mode is adopted for deployment of a large data platform, deployment can be performed under various environments, a cloud platform is supported, a physical machine is deployed, container deployment is performed by using a docker, only a software container needs to be downloaded, and one-key deployment can be performed by using a docker composite file and a script file, so that automatic operation and maintenance are automatically upgraded.
According to the intelligent operation and maintenance system provided by the embodiment of the invention, the deployment component is arranged to perform cluster deployment on the big data platform, so that the intelligent operation and maintenance system supports automatic operation and maintenance in various environments, and the operation and maintenance efficiency of the intelligent system is improved.
The following describes the intelligent operation and maintenance method provided by the present invention, and the intelligent operation and maintenance method described below and the intelligent operation and maintenance system described above may be referred to correspondingly.
Fig. 8 is a schematic flow chart of the intelligent operation and maintenance method provided by the present invention, and as shown in fig. 7, the intelligent operation and maintenance method includes the following steps:
and step 810, collecting state data generated by a plurality of sub-devices of the rail transit signal device.
In this step, the data acquisition module is connected to the rail transit signal device through a network port using status data generated by a plurality of sub-devices of the rail transit signal device of the data acquisition module.
In this embodiment, the data collection function may be implemented by a deployed maintenance network switch.
And step 820, converting the format of the state data into a target format to obtain target state data.
In this step, the big data platform layer has functions of processing, analyzing, storing and the like on the data.
In this step, the grouping mode may be distinguished by different subsystem types, for example, the large data platform may perform transmission classification on the multi-source heterogeneous data through the middleware (Kafka), and provide log information of each heterogeneous data, so as to facilitate viewing or management.
In this embodiment, the target format can be customized according to the user's requirements.
And 830, under the condition that the target state data contains alarm information, acquiring a diagnosis result corresponding to the target state data based on the alarm information, wherein the diagnosis result contains a fault type corresponding to the alarm information and a life prediction value of the sub-equipment corresponding to the target state data.
In this step, the data diagnosis module includes multiple diagnosis models, for example, a speed measuring radar data model, for performing fault analysis on heterogeneous data generated by the speed measuring radar; the data diagnosis module also comprises a device full life cycle management model used for analyzing and counting the consumption condition of the fault device.
In the embodiment, after the turnout fault is determined, the equipment full-life-cycle management model can be used for inquiring the factory parameters of the turnout, so that the service life and the consumption of the turnout are counted, and the residual service life of the turnout is predicted.
And 840, matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, wherein the knowledge base is determined based on the operation and maintenance scheme corresponding to the existing rail transit equipment fault type.
In this step, in this embodiment, a knowledge base may be created based on a preset knowledge graph.
In this embodiment, various data in the knowledge graph are stored in the form of entities, attributes and relationships, that is, the knowledge graph can represent names and corresponding information of the data, and also marks the relationships among different data.
In this embodiment, the knowledge base further includes the equipment faults that have occurred in the rail transit signal system and the corresponding operation and maintenance scheme.
In the embodiment, when the intelligent operation and maintenance system detects that the equipment has a fault, the intelligent operation and maintenance system searches in the knowledge graph according to fault information to perform fault linkage, alarms field operation and maintenance personnel according to the expert information and the fault handling scheme stored in the knowledge graph, and provides a solution.
In this embodiment, the service application module further provides functions such as fault visualization display and fault alarm.
According to the intelligent operation and maintenance method provided by the embodiment of the invention, the monitoring range of the intelligent operation and maintenance system on the signal system is improved by acquiring the state data generated by the plurality of sub-devices of the rail transit signal system to perform data analysis, and the target state data of different devices are subjected to fault prediction and device service life prediction through the diagnosis model, so that the problem of low efficiency in manual troubleshooting of device faults is solved, and the operation and maintenance efficiency of the signal system is improved.
In some embodiments, after obtaining the operation and maintenance plan corresponding to the diagnosis result, the method further includes: and displaying the diagnosis result and the operation and maintenance scheme on a display screen.
In this embodiment, when the intelligent operation and maintenance system diagnoses the target state data, when the diagnosis result is a fault type which has occurred historically, the diagnosis result can be processed through a fault diagnosis scheme stored historically, and the diagnosis result and the processing mode are visually displayed on the display screen, so that the operation and maintenance personnel can guide the field device to perform maintenance operation according to the prompt content of the display screen.
In this embodiment, the display screen may be a cell phone video capture and beautification tool, such as a VUE.
According to the intelligent operation and maintenance method provided by the embodiment of the invention, the diagnosis result is visually displayed, so that operation and maintenance personnel can conveniently execute operation and maintenance operation in time according to the prompt content of the display screen, the operation and maintenance efficiency of the intelligent system is improved, meanwhile, the maintenance time can be reduced, and the time cost and the labor cost can be saved.
Fig. 9 illustrates a physical structure diagram of an electronic device, 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. Processor 910 may invoke logic instructions in memory 930 to perform an intelligent operation and maintenance method comprising: acquiring state data generated by a plurality of sub-devices of the rail transit signal device; converting the state data into a target format to obtain target state data; under the condition that the target state data comprises alarm information, acquiring a diagnosis result corresponding to the target state data based on the alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information and a life prediction value of a target sub-device corresponding to the target state data; and matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, wherein the knowledge base is determined based on the operation and maintenance scheme corresponding to the existing rail transit equipment fault type.
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: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, the computer program may be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, a computer can execute the intelligent operation and maintenance method provided by the above methods, where the method includes: acquiring state data generated by a plurality of sub-devices of the rail transit signal device; converting the state data into a target format to obtain target state data; under the condition that the target state data comprises alarm information, acquiring a diagnosis result corresponding to the target state data based on the alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information and a life prediction value of a target sub-device corresponding to the target state data; and matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, wherein the knowledge base is determined based on the operation and maintenance scheme corresponding to the existing rail transit equipment fault type.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to execute the intelligent operation and maintenance method provided by the above methods, the method including: acquiring state data generated by a plurality of sub-devices of the rail transit signal device; converting the state data into a target format to obtain target state data; under the condition that the target state data comprises alarm information, acquiring a diagnosis result corresponding to the target state data based on the alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information and a life prediction value of a target sub-device corresponding to the target state data; and matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, wherein the knowledge base is determined based on the operation and maintenance scheme corresponding to the existing rail transit equipment fault type.
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. An intelligent operation and maintenance system, comprising:
the data acquisition module is respectively connected with a plurality of sub-devices of the rail transit signal device through network ports and is used for acquiring state data generated by the sub-devices;
the big data platform is electrically connected with the data acquisition module and is used for converting the format of the state data into a target format to obtain target state data;
the data diagnosis module is electrically connected with the big data platform and used for acquiring a diagnosis result corresponding to the target state data based on alarm information under the condition that the target state data comprises the alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information and a life prediction value of a sub-device corresponding to the target state data;
the service application module is electrically connected with the data diagnosis module and used for matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, and the knowledge base is determined based on the existing fault data and the operation and maintenance scheme corresponding to the existing fault data.
2. The intelligent operation and maintenance system according to claim 1, wherein the data diagnosis module comprises:
the fault diagnosis model is used for clustering the alarm information to obtain a fault type corresponding to the alarm information;
and the equipment full life cycle management model is used for obtaining a life prediction value of the sub-equipment corresponding to the target state data based on the delivery parameters and the service life of the sub-equipment corresponding to the target state data.
3. The intelligent operation and maintenance system according to claim 1, wherein the status data comprises offline documents and real-time information, and the big data platform comprises:
a batching component, the batching layer to convert the format of the offline document to the target format;
a stream processing component, the batch layer for converting the format of the real-time information into the target format.
4. The intelligent operation and maintenance system according to claim 1, wherein the big data platform further comprises:
a storage component to store the target state data.
5. The intelligent operation and maintenance system according to claim 1, wherein the service application module further comprises:
and the display screen is used for displaying the diagnosis result and the operation and maintenance scheme.
6. The intelligent operation and maintenance system according to claim 1, further comprising:
and the deployment component is used for carrying out cluster deployment on the big data platform.
7. An intelligent operation and maintenance method is characterized by comprising the following steps:
collecting state data generated by a plurality of sub-devices of the rail transit signal device;
converting the format of the state data into a target format to obtain target state data;
under the condition that the target state data comprises alarm information, acquiring a diagnosis result corresponding to the target state data based on the alarm information, wherein the diagnosis result comprises a fault type corresponding to the alarm information and a life prediction value of a target sub-device corresponding to the target state data;
and matching the diagnosis result with a preset knowledge base to obtain an operation and maintenance scheme corresponding to the diagnosis result, wherein the knowledge base is determined based on the operation and maintenance scheme corresponding to the existing rail transit equipment fault type.
8. The intelligent operation and maintenance method according to claim 7, wherein after the operation and maintenance scheme corresponding to the diagnosis result is obtained, the method further comprises:
and displaying the diagnosis result and the operation and maintenance scheme on a display screen.
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 intelligent operation and maintenance method according to any one of claims 7 to 8 when executing the program.
10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the intelligent operation and maintenance method according to any one of claims 7 to 8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116401128A (en) * | 2023-06-06 | 2023-07-07 | 四川观想科技股份有限公司 | Big data-based information operation and maintenance management system |
| CN116545961A (en) * | 2023-07-03 | 2023-08-04 | 明阳时创(北京)科技有限公司 | Intelligent detection method and system for network switch clusters |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116401128A (en) * | 2023-06-06 | 2023-07-07 | 四川观想科技股份有限公司 | Big data-based information operation and maintenance management system |
| CN116401128B (en) * | 2023-06-06 | 2023-08-08 | 四川观想科技股份有限公司 | Big data-based information operation and maintenance management system |
| CN116545961A (en) * | 2023-07-03 | 2023-08-04 | 明阳时创(北京)科技有限公司 | Intelligent detection method and system for network switch clusters |
| CN116545961B (en) * | 2023-07-03 | 2023-09-15 | 明阳时创(北京)科技有限公司 | Intelligent detection method and system for network switch clusters |
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