Rail transit power supply operation and maintenance method and system based on big data

Abstract

The invention discloses a rail transit power supply operation and maintenance method and system based on big data, and the method is characterized in that the method comprises the steps: collecting target rail transit power supply data; processing the target rail transit power supply data according to a preset analysis rule; storing the target rail transit power supply data; retrieving the target rail transit power supply data according to a preset early warning rule to obtain target early warning information; and displaying the target early warning information. The problem that in the prior art, rail transit operation and maintenance data are difficult to fuse is solved, and integration and systematization of power supply intelligent operation and maintenance are achieved.

Description

Technical Field

[0001] The present invention relates to the field of electric power technology, and in particular to a rail transit power supply operation and maintenance method and system based on big data. Background Art

[0002] At present, the phenomenon of old power supply equipment and low degree of digitization is still common. In the current rail transit power supply operation system, some system technologies are still outdated, and it is difficult to apply new technologies and expand them elastically. The network construction level is low, the bandwidth is limited, and mobile bandwidth access is difficult. At the same time, the degree of digitization and intelligence is low, the level of intelligent perception is limited, and the monitoring coverage is incomplete, which restricts the application of intelligent operation and maintenance of rail transit power supply and intelligent auxiliary decision-making. It is difficult to integrate data across disciplines and systems. In the development of intelligent operation and maintenance technology, data has always been the core and foundation of the entire intelligent operation and maintenance. At present, the data of each operation and maintenance system of rail transit are operated and managed separately, the openness of each system is poor, the interconnection and interoperability of data of each discipline and system is difficult, and the existing data generally has problems such as differentiated data structure and low data integrity. There are a large number of information islands, which brings great difficulties to the big data mining and intelligent analysis of intelligent operation and maintenance of power supply.

[0003] Therefore, it is necessary to provide a rail transit power supply operation and maintenance method based on big data to solve the problem of difficulty in integrating rail transit operation and maintenance data in the existing technology and realize the integration and systematization of intelligent power supply operation and maintenance. Summary of the invention

[0004] The main purpose of the present invention is to provide a rail transit power supply operation and maintenance method and system based on big data, so as to solve the problem of difficulty in integrating rail transit operation and maintenance data in the prior art and realize the integration and systematization of intelligent power supply operation and maintenance.

[0005] In a first aspect, the present invention provides a rail transit power supply operation and maintenance method based on big data, comprising:

[0006] Collect target rail transit power supply data;

[0007] Processing the target rail transit power supply data according to preset analysis rules;

[0008] Storing the target rail transit power supply data;

[0009] According to the preset warning rules, the target rail transit power supply data is retrieved to obtain the target warning information;

[0010] Display the target warning information.

[0011] Optionally, the step of collecting target rail transit power supply data includes:

[0012] The target rail transit power supply offline data is collected through Kettle, and the target rail transit power supply real-time data is collected through Flume.

[0013] Optionally, the step of processing the target rail transit power supply data according to a preset analysis rule includes:

[0014] The target rail transit power supply data is processed according to preset analysis rules through Spark SQL and / or Spark Streaming.

[0015] Optionally, the step of storing the target rail transit power supply data includes:

[0016] The target rail transit power supply data is stored in Mongodb or FastDFS.

[0017] Optionally, the step of storing the target rail transit power supply data in Mongodb or FastDFS includes:

[0018] The target rail transit power supply data processed by the preset analysis rules is obtained through Kafka, and the target rail transit power supply data is stored in Mongodb or FastDFS.

[0019] Optionally, the step of retrieving the target rail transit power supply data to obtain target warning information according to a preset warning rule includes:

[0020] Through ElasticSreach, the target rail transit power supply data is retrieved according to the preset warning rules to obtain the target warning information.

[0021] According to a second aspect of an embodiment of the present invention, the present invention provides a rail transit power supply operation and maintenance system based on big data, comprising:

[0022] Collection module: used to collect target rail transit power supply data;

[0023] Processing module: used to process the target rail transit power supply data according to preset analysis rules;

[0024] Storage module: used to store the target rail transit power supply data;

[0025] Retrieval module: used to retrieve the target rail transit power supply data according to preset warning rules to obtain target warning information;

[0026] Display module: used to display the target warning information.

[0027] Optionally, the acquisition module further includes:

[0028] Offline data acquisition unit: used to collect target rail transit power supply offline data through Kettle;

[0029] Real-time data acquisition unit: used to collect real-time data of target rail transit power supply through Flume.

[0030] Optionally, the processing module further includes a stream processing unit: configured to process the target rail transit power supply data according to preset analysis rules through Spark SQL and / or Spark Streaming.

[0031] Optionally, the retrieval module further includes an index retrieval unit: configured to retrieve the target rail transit power supply data through ElasticSreach according to preset warning rules to obtain target warning information.

[0032] Compared with the prior art, one or more embodiments of the above scheme may have the following advantages or beneficial effects:

[0033] The present invention solves the problem of difficulty in integrating rail transit operation and maintenance data in the prior art, and realizes integrated and systematized intelligent power supply operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solution of the present invention, the drawings required for use in the embodiments are briefly introduced below. Obviously, for non-specialized technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0035] Figure 1 A schematic diagram of a flow chart of a rail transit power supply operation and maintenance method based on big data provided by an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a rail transit power supply operation and maintenance system based on big data provided by an embodiment of the present invention;

[0037] Figure 3 A timing diagram of a rail transit power supply operation and maintenance system based on big data is provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will describe the implementation methods of the present invention in detail with reference to the accompanying drawings and embodiments, so that the implementation process of how the present invention applies technical means to solve technical problems and achieve corresponding technical effects can be fully understood and implemented accordingly. The embodiments of the present invention and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the protection scope of the present invention.

[0039] Embodiment 1

[0040] like Figure 1 As shown, an embodiment of the present invention provides a rail transit power supply operation and maintenance method based on big data, comprising the following steps S101 to S105:

[0041] Step S101: Collect target rail transit power supply data.

[0042] For example, Kettle is used to collect the target rail transit power supply offline data, and Flume is used to collect the target rail transit power supply real-time data. The rail transit power supply offline data is the offline static rail transit power supply data, and the rail transit power supply real-time data is the rail transit power supply data obtained from the device in real time.

[0043] Step S102: Processing the target rail transit power supply data according to preset analysis rules.

[0044] For example, the target rail transit power supply data is processed according to preset analysis rules through Spark SQL and / or Spark Streaming. The preset analysis rules, for example, analyze the temperature of the voltage equipment and take the average temperature per hour. Therefore, the original collected data needs to be normalized.

[0045] Step S103: storing target rail transit power supply data.

[0046] For example, the target rail transit power supply data processed by the preset analysis rules is obtained through Kafka, and the target rail transit power supply data is stored in Mongodb or FastDFS.

[0047] Step S104: searching the target rail transit power supply data according to the preset warning rules to obtain the target warning information.

[0048] For example, ElasticSreach retrieves the target rail transit power supply data according to the preset warning rules to obtain the target warning information. After obtaining the normalized data, it is stored in the ElasticSreach index for fast retrieval. For example, if the temperature of the voltage equipment is higher than 80 degrees for 4 consecutive hours, if this condition is met, it means that there may be a problem with the voltage equipment and it needs to be dealt with in time.

[0049] Step S105: Display target warning information.

[0050] For example, information about voltage devices with problems is displayed on the display interface.

[0051] Embodiment 2

[0052] like Figure 2 As shown, an embodiment of the present invention provides a rail transit power supply operation and maintenance system based on big data, including:

[0053] Collection module: used to collect target rail transit power supply data;

[0054] Processing module: used to process the target rail transit power supply data according to preset analysis rules;

[0055] Storage module: used to store the target rail transit power supply data;

[0056] Retrieval module: used to retrieve the target rail transit power supply data according to preset warning rules to obtain target warning information;

[0057] Display module: used to display the target warning information.

[0058] Optionally, the acquisition module further includes:

[0059] Offline data acquisition unit: used to collect target rail transit power supply offline data through Kettle;

[0060] Real-time data acquisition unit: used to collect real-time data of target rail transit power supply through Flume.

[0061] Optionally, the processing module further includes a stream processing unit:

[0062] Used to process the target rail transit power supply data according to preset analysis rules through Spark SQL and / or Spark Streaming.

[0063] Optionally, the retrieval module further includes an index retrieval unit: configured to retrieve the target rail transit power supply data through ElasticSreach according to preset warning rules to obtain target warning information.

[0064] Embodiment 3

[0065] like Figure 3 As shown, an embodiment of the present invention provides a rail transit power supply operation and maintenance method based on big data, comprising:

[0066] Step S1: User logs in and obtains analysis data request;

[0067] For example, a user enters a password and logs into the system after verification.

[0068] Step S2: The business system initiates a request for analysis data to the algorithm library / rule library;

[0069] For example, after the user specifies the analysis rules, the analysis of characters is started.

[0070] Step S3: the algorithm library / rule library obtains offline calculation data from Mongodb (structured data) / FastDFS (unstructured data);

[0071] For example, basic data is obtained according to analysis rules specified by the user.

[0072] Step S4: Mongodb / FastDFS performs normalized data processing;

[0073] For example, clean up irregular basic data.

[0074] Step S5: Normalize data processing and consume data to Kafka.

[0075] For example, sending normalized data to kafka.

[0076] Step S6: Kafka initiates a data collection instruction to the data collection program;

[0077] For example, Kafka connects to Flume to obtain online real-time log data.

[0078] Step S7: linking data sources (detection and monitoring system, production operation system) to access data;

[0079] For example, connected to detection and monitoring systems, production operation systems.

[0080] Step S8: Read data from the data source (detection and monitoring system, production operation system);

[0081] For example, reading detection and monitoring system and production operation system data.

[0082] Step S9: The data collection program returns the collected data to Kafka;

[0083] For example, Kafka obtains collected data.

[0084] Step S10: The normalized data is directly stored in Mongodb / FastDFS.

[0085] For example, storing normalized data directly into Mongodb / FastDFS.

[0086] Step S11: The offline calculated data is returned to the algorithm library.

[0087] For example, the normalized data is stored.

[0088] Step S12: Real-time computing initiates a data consumption request to Kafka;

[0089] For example, real-time computing initiates data consumption requests to Kafka.

[0090] Step S13: Write to Mongodb according to algorithm rules;

[0091] For example, normalized data is written to Mongodb according to algorithmic rules.

[0092] Step S14: Return the result value to the business system;

[0093] For example, the result value will be returned to the business system.

[0094] Step S15: The business system interface obtains the calculated result data for presentation;

[0095] For example, the display interface shows that the temperature of a voltage device is higher than 80 degrees for 4 consecutive hours.

[0096] Step S16: For common business data, the user directly initiates a query request to the business system;

[0097] For example, a user queries voltage devices whose temperature is higher than 80 degrees for four consecutive hours.

[0098] Step S17: The business system initiates a data query request to the Mongodb component of the big data platform;

[0099] For example, query based on the condition that the temperature is higher than 80 degrees for 4 consecutive hours.

[0100] Step S18: The Mongodb database returns data to the business system;

[0101] For example, return the query results.

[0102] Step S19: The result data is displayed and presented to the user interface.

[0103] For example, the query results are displayed on the display interface.

[0104] Among them, the kettle component provides data extraction and transformation tools (ETL) for the big data platform.

[0105] Flume-Agent component provides log data collection function for the big data platform.

[0106] Kafka component provides a distributed data bus system for the big data platform to solve data interaction scenarios between systems.

[0107] Spark component provides a one-stop computing platform for big data platforms, cleans, standardizes, and formats raw data, that is, implements data preprocessing functions.

[0108] Spark SQL and Spark Streaming components support batch processing, stream processing, and data exchange and data operations between the two, and mainly complete the calculation and processing of core indicators required for big data analysis.

[0109] FastDFS component provides a distributed file storage system for big data platforms.

[0110] MongoDB component provides a distributed database system for the big data platform.

[0111] ElasticSreach component provides full-text data retrieval services for big data platforms.

[0112] The present invention provides a power supply intelligent operation and maintenance system based on big data, which includes three major components: ① Intelligent infrastructure detection and monitoring and infrastructure full life cycle management, which provide the data foundation of the power supply intelligent operation and maintenance system and also serve as data guarantee; ② A technical support platform composed of cloud computing and big data technology, which is the carrier for carrying out power supply intelligent operation and maintenance and also serves as technical guarantee; ③ An intelligent business application system composed of fault prediction and health management and intelligent production operations, which covers two aspects: intelligent health management of power supply infrastructure and intelligent production management.

[0113] Intelligent detection and monitoring realizes real-time online diagnosis and identification of the power supply infrastructure and dynamic high-precision detection, including real-time monitoring and dynamic inspection. Real-time monitoring is real-time intelligent monitoring of infrastructure such as substations and tunnels; dynamic inspection mainly includes contact network detection, online detection of bow-net, shoe-rail detection, contact suspension imaging detection, etc.

[0114] The infrastructure life cycle management is based on big data analysis and is the foundation of intelligent power supply operation and maintenance. It integrates basic information, historical information, equipment detection information, equipment maintenance information and other data of power supply equipment and facilities, and establishes data models suitable for equipment and equipment, equipment and assets based on the master data modeling concept, so as to realize the digital management of the life cycle of equipment and facilities from procurement to scrapping, and assets from start to end.

[0115] The cloud platform uses virtualization technology to connect a large amount of hardware through an internal network, integrating various servers, applications, data and other resources to provide flexibly adjustable, low-consumption, high-efficiency, high-availability and highly scalable computing and storage resources.

[0116] The big data platform adopts a distributed architecture, uses distributed processing, distributed databases and cloud storage, conducts joint analysis and deep mining of massive structured data, text, video images, voice, etc., conducts trend analysis on operation and maintenance rules, minimizes the probability of errors in analysis results, and makes the analysis process and results visual and traceable throughout. The core brain of power supply intelligent operation and maintenance is to achieve the transformation of business application systems from expert decision-making to artificial intelligence decision-making, and from empirical models to machine automated intelligent learning.

[0117] Fault prediction and health management mainly use power supply detection and monitoring data and production operation data as data sources, use big data technology to collect data, standardize data processing and storage, establish analysis models and evaluation indicators and perform analysis and calculation, so as to realize power supply infrastructure status assessment, comprehensive health evaluation, fault prediction, life prediction, operation and maintenance decision support and other functions.

[0118] The intelligent production operation relies on the cloud platform and big data platform, and combines the analysis results of the two major applications of full life cycle management and fault prediction and health management to realize the full process management of intelligent power supply production operations, including the preparation of production operation plans, integrated maintenance operations, production operation process card control, production operation monitoring, and maintenance quality evaluation.

[0119] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0120] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned embodiments can be completed by hardware, or can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, and the storage medium mentioned above can be a read-only memory, a disk or an optical disk, etc. Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0123] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or system capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0124] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A rail transit power supply operation and maintenance method based on big data, characterized in that: include: Collect target rail transit power supply data; Processing the target rail transit power supply data according to preset analysis rules; Storing the target rail transit power supply data; According to the preset warning rules, the target rail transit power supply data is retrieved to obtain the target warning information; Display the target warning information.

2. The method according to claim 1, characterized in that The step of collecting target rail transit power supply data includes: The target rail transit power supply offline data is collected through Kettle, and the target rail transit power supply real-time data is collected through Flume.

3. The method according to claim 1, characterized in that The step of processing the target rail transit power supply data according to the preset analysis rules includes: The target rail transit power supply data is processed according to preset analysis rules through Spark SQL and / or Spark Streaming.

4. The method according to claim 1, characterized in that: The step of storing the target rail transit power supply data includes: The target rail transit power supply data is stored in Mongodb or FastDFS.

5. The method according to claim 4, characterized in that The step of storing the target rail transit power supply data in Mongodb or FastDFS includes: The target rail transit power supply data processed by the preset analysis rules is obtained through Kafka, and the target rail transit power supply data is stored in Mongodb or FastDFS.

6. The method according to claim 1, characterized in that The step of retrieving the target rail transit power supply data according to the preset warning rule to obtain the target warning information includes: Through ElasticSreach, the target rail transit power supply data is retrieved according to the preset warning rules to obtain the target warning information.

7. A rail transit power supply operation and maintenance system based on big data, characterized in that: include: Collection module: used to collect target rail transit power supply data; Processing module: used to process the target rail transit power supply data according to preset analysis rules; Storage module: used to store the target rail transit power supply data; Retrieval module: used to retrieve the target rail transit power supply data according to preset warning rules to obtain target warning information; Display module: used to display the target warning information.

8. The system according to claim 7, characterized in that The acquisition module also includes: Offline data acquisition unit: used to collect target rail transit power supply offline data through Kettle; Real-time data acquisition unit: used to collect real-time data of target rail transit power supply through Flume.

9. The system according to claim 7, characterized in that The processing module also includes a stream processing unit: used to process the target rail transit power supply data according to preset analysis rules through Spark SQL and / or Spark Streaming.

10. The system according to claim 7, characterized in that The retrieval module also includes an index retrieval unit: used to retrieve the target rail transit power supply data through ElasticSreach according to preset warning rules to obtain target warning information.

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