CN115809226A - Data docking method and system for intelligent management system of coal washery - Google Patents
Data docking method and system for intelligent management system of coal washery Download PDFInfo
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Abstract
The application provides a data docking method and a data docking system for an intelligent management system of a coal washery, wherein the method comprises the following steps: based on a docking interface provided by each of a plurality of data sources of the coal washery, acquiring data from the plurality of data sources; wherein the data on each data source is data of equipment of the coal washery; based on an interface protocol supported by an intelligent management system of a coal washery, uniformly converting the collected multi-source data into data conforming to the interface protocol to obtain multi-source heterogeneous data; sending the multi-source heterogeneous data to a message middleware; and reading data from the message middleware, performing data cleaning and/or data aggregation on the read data, and writing the data after the data cleaning and/or the data aggregation into a relational database of the intelligent management system of the coal washery. The intelligent management system can realize effective access of various data sources, and write in the intelligent management system of the coal washery after certain data processing, thereby providing effective digital scheduling basis for scheduling of the coal washery and optimizing management of the coal washery.
Description
Technical Field
The application relates to the technical field of coal washery, in particular to a data docking method and system for an intelligent management system of a coal washery.
Background
The Time Series Database (Time Series Database) is a specialized Database for storing and managing Time Series data, has the characteristics of less writing and reading, clear cold and hot, high concurrent writing, no transaction requirement, continuous writing of mass data and the like, can be based on Time interval aggregation analysis and efficient retrieval, and takes values according to dimensions as the Time increases while the latitude of the data is almost unchanged. The time sequence database is widely applied to scenes such as internet of things, economic finance, environment monitoring, industrial manufacturing, agricultural production, hardware and software system monitoring and the like, and a common time sequence database provides a plurality of docking interfaces such as OLE DB, MQTT and Webservice outwards. Besides industrial databases, various sensors are widely applied to industrial coal preparation plants, and basically support general protocols such as MQTT, modbus and HTTP. In the related art, the problems of docking and acquisition of time sequence databases and sensors in the industry exist.
Disclosure of Invention
The application provides a data docking method and system for an intelligent management system of a coal washery.
According to a first aspect of the application, a data docking method for an intelligent management system of a coal washery is provided, and the method comprises the following steps:
the method comprises the steps that data collection is carried out from a plurality of data sources based on docking interfaces respectively provided by the data sources of the coal washing plant; wherein the data on each data source is data of equipment of a coal washery;
based on an interface protocol supported by the intelligent management system of the coal washery, uniformly converting the collected multi-source data into data conforming to the interface protocol to obtain multi-source heterogeneous data;
sending the multi-source heterogeneous data to a message middleware;
and reading data from the message middleware, performing data cleaning and/or data aggregation on the read data, and writing the data after the data cleaning and/or the data aggregation into a relational database of the intelligent management system of the coal washery.
According to a second aspect of the present application, there is provided a data docking system for a coal washery intelligent management system, comprising: the system comprises a data acquisition module, a message middleware and a data processing module; wherein the content of the first and second substances,
the data acquisition module is used for acquiring data from a plurality of data sources based on docking interfaces respectively provided by the plurality of data sources of the coal washery; the data on each data source is data of equipment of a coal washing plant, collected multi-source data are uniformly converted into data conforming to an interface protocol based on the interface protocol supported by an intelligent management system of the coal washing plant, so that multi-source heterogeneous data are obtained, and the multi-source heterogeneous data are sent to the message middleware;
and the data processing module is used for reading data from the message middleware, performing data cleaning and/or data aggregation on the read data, and writing the data after the data cleaning and/or data aggregation into a relational database of the intelligent management system of the coal washery.
According to the data docking method for the intelligent management system of the coal washery, data in various data sources can be collected, data collection and output interface standards are unified, and multi-source heterogeneous data are obtained. And then, effective access of various data sources is realized, and the data are written into a relational database of an intelligent management system of the coal washery after certain processing, so that an effective digital scheduling basis is provided for scheduling of the coal washery, and management of the coal washery is optimized.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments 5, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a data docking method for an intelligent management system of a coal washery according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of another data docking method for an intelligent management system of a coal washery according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of another data docking method for an intelligent management system of a coal washery according to an embodiment of the present disclosure;
fig. 4 is a block diagram of a data docking system for an intelligent management system of a coal washery according to an embodiment of the present disclosure;
FIG. 5 is a block diagram of another data docking system 5 for an intelligent management system of a coal washery according to an embodiment of the present disclosure;
fig. 6 is a block diagram of a data docking system for an intelligent management system of a coal washery according to an embodiment of the present disclosure.
Detailed Description
0 embodiments of the present application, examples of which are illustrated in the accompanying drawings, are described in detail below, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
The application provides a data docking method and system for an intelligent management system of a coal washery. Specifically, the data docking method and system for the intelligent management system of the coal washery according to the embodiments of the present application are described below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a data docking method for an intelligent management system of a coal washery according to an embodiment of the present disclosure. As shown in fig. 1, the data docking method for the intelligent management system of the coal washery comprises the following steps:
It should be noted that the plurality of data sources includes at least two of the following data sources: time series databases, sensors, and relational databases MySQL. As an example, data in the time series database may be collected using an OLEB or http protocol; for sensor data, modbus and MQTT protocols can be adopted for acquisition.
Alternatively, the data provided by the data source may be a large amount of data or may be a small amount of data. For different data volumes, data acquisition can be performed in different ways. As an example, in the case that the data provided by the multiple data sources is a small amount of data, the edge-side rule engine ekheiper may perform data collection from the multiple data sources by using a docking interface provided by each of the multiple data sources of the coal washery. As yet another example, in the case that the data provided by the plurality of data sources is a big data volume, the big data computing engine Spark may be used to collect data from the plurality of data sources by using the docking interfaces provided by the plurality of data sources of the coal washery.
An example of the method for acquiring the time sequence database by using the big data computing engine Spark is described by a simple bin information acquisition method in a scala language:
val spark＝SparkSession.builder()
.appName(this.getClass.getSimpleName)
.master("local[*]")
.getOrCreate()
val jdbcDF＝spark.read
.format("jdbc")
.option("url","jdbc:sqlserver://XX:port")
.option("user","username")
.option("password","password")
.load()
val sql = "(SELECT TOP 1 from OPENQUERY (XX) 'SELECT from XX. Raw data WHERE tag =" XX bin "') order by tismestamp DESC"
val df＝spark.sql(sql)
jdbcDF.createOrReplaceTempView("DATA")
val dictDF＝spark.sql("SELECT*FROM DATA")
dictDF.show()
Where OPENQUERY is the execution of a specified delivery query on a specified link server. The server is the OLE DB data source. OPENQUERY may be referenced in the FROM clause of the query. The OLE DB not only includes Structured Query Language (SQL) capabilities for standard data interface open database connectivity (ODBC), but also has pathways to other non-SQL data types.
It should be noted that, corresponding conversion rules exist between different interface protocols, so that the collected multi-source data is uniformly converted into data conforming to the interface protocols through the corresponding conversion rules based on the interface protocols supported by the intelligent management system of the coal washery, so as to obtain the multi-source heterogeneous data.
And step 103, sending the multi-source heterogeneous data to a message middleware.
As an example, in the case that data provided by multiple data sources is a small amount of data, the multi-source heterogeneous data may be sent to the message middleware RabbitMq. As yet another example, where the data provided by the multiple data sources is a large amount of data, the multi-source heterogeneous data may be sent to the message middleware Kafka.
And step 104, reading data from the message middleware, performing data cleaning and/or data aggregation on the read data, and writing the data after the data cleaning and/or the data aggregation into a relational database of the intelligent management system of the coal washery.
Optionally, the read data may be data washed and/or data aggregated using FlinkSql or HSQL.
It should be noted that, in some embodiments of the present application, when data provided by multiple data sources is large data volume, in order to reduce delay, real-time data may be read from the message middleware by the large data stream processing engine, so that the delay is in the order of milliseconds, and data cleaning and/or data aggregation may be performed on the read real-time data, and the real-time data after data cleaning and/or data aggregation may be sent to the coal washery intelligent management system for monitoring and displaying.
It should be noted that the intelligent management system of the coal washery can schedule various parts of the coal washery, such as car washing and loading, material management, coal system management, shutdown analysis, energy consumption analysis, human resources, production management, and operation management.
According to the data docking method for the intelligent management system of the coal washery, data in various data sources can be collected, data collection and output interface standards are unified, and multi-source heterogeneous data are obtained. And then, effective access of various data sources is realized, and the data are written into a relational database of an intelligent management system of the coal washery after certain processing, so that an effective digital scheduling basis is provided for scheduling of the coal washery, and management of the coal washery is optimized.
It should be noted that, for the case that the data in the database is small, the data can be read in the message middleware at regular time and analyzed, so as to grasp the coal washery condition in time based on the small data volume. As an example, fig. 2 is a schematic flowchart of another data docking method for an intelligent management system of a coal washery according to an embodiment of the present application. Wherein, the data provided by the plurality of data sources is a small data size. As shown in fig. 2, on the basis of the foregoing embodiment, the data interfacing method for the intelligent management system of the coal washery may further include the following steps:
As an example, the target business may be car wash loading, material management, coal management, outage analysis, energy consumption analysis, human resources, and the like.
And step 203, performing data analysis on the first multi-source heterogeneous data based on the timing task to generate a corresponding analysis report.
And step 204, sending the analysis report to an intelligent management system of the coal washery for display.
By implementing the embodiment of the application, data are collected and analyzed from the message middleware at regular time, and an analysis report is generated so as to be displayed in the intelligent management system of the coal washery.
In the case of a large data volume of data in the database, the data can be read from the message middleware based on the data analysis requirement and analyzed, so as to grasp the coal washery condition based on the large data volume. As an example, fig. 3 is a schematic flowchart of another data docking method for an intelligent management system of a coal washery according to an embodiment of the present application. Wherein, the data provided by the plurality of data sources is a large data volume. As shown in fig. 3, based on the embodiment shown in fig. 1, the data docking method for the intelligent management system of the coal washery may further include the following steps:
As an example, the target business may be production management, business management, electromechanical management, outage analysis, energy consumption analysis, human resources, and the like.
And step 303, performing data analysis on the first multi-source heterogeneous data through Hive based on the data analysis requirement to generate a corresponding analysis report.
And step 304, sending the analysis report to an intelligent management system of the coal washery for displaying.
By implementing the embodiment of the application, data are collected and analyzed from the message middleware based on the data analysis requirement, and an analysis report is generated so as to be displayed in the intelligent management system of the coal washery.
Fig. 4 is a block diagram of a data docking system for an intelligent management system of a coal washery according to an embodiment of the present disclosure. As shown in fig. 4, the data docking system for the intelligent management system of the coal washery includes: a data acquisition module 401, message middleware 402, and a data processing module 403. Wherein the content of the first and second substances,
the data acquisition module 401 is configured to acquire data from a plurality of data sources of the coal washery based on respective docking interfaces provided by the plurality of data sources. The data on each data source is data of equipment of the coal washery, collected multi-source data are uniformly converted into data conforming to an interface protocol based on the interface protocol supported by an intelligent management system of the coal washery, multi-source heterogeneous data are obtained, and the multi-source heterogeneous data are sent to the message middleware 402.
And the data processing module 403 is configured to read data from the message middleware 402, perform data cleaning and/or data aggregation on the read data, and write the data after the data cleaning and/or data aggregation into a relational database of the intelligent management system of the coal washery.
In some embodiments of the present application, in a case that data provided by multiple data sources is a small amount of data, the data acquisition module 401 is specifically configured to: and data acquisition is carried out from the plurality of data sources by adopting docking interfaces respectively provided by the plurality of data sources of the coal washery through an edge side rule engine eKuiper.
Optionally, in some embodiments of the present application, in a case where the data provided by the plurality of data sources is a small amount of data, as shown in fig. 5, the data docking system for the intelligent management system of the coal washery may further include a data analysis module 504. Wherein, the data analysis module 504 is configured to determine a timing task of the target service, and read first multi-source heterogeneous data corresponding to the target service from the message middleware, based on the timing task,
and carrying out data analysis on the first multi-source heterogeneous data to generate a corresponding analysis report, and sending 5 the analysis report to an intelligent management system of the coal washery for display.
In some embodiments of the present application, in a case that data provided by the multiple data sources is a large data volume, the data acquisition module 401 is specifically configured to: and data acquisition is carried out from the plurality of data sources by a big data computing engine Spark and by adopting docking interfaces respectively provided by the plurality of data sources of the coal washery.
In some embodiments of the present application, in a case that data provided by multiple data sources is a large amount of data, the 0 data processing module 403 is specifically configured to: reading real-time data from message middleware through a big data stream processing engine, performing data cleaning and/or data aggregation on the read real-time data, and cleaning and & -er the data
Or the real-time data after the data aggregation is sent to an intelligent management system of the coal washery for monitoring and displaying.
Optionally, in some embodiments of the present application, in a case where the data provided by the plurality of data sources is a large amount of data, as shown in fig. 6, the data docking system for the intelligent management system of the coal washery may further include a data analysis module 5 604. Wherein, the data analysis module 604 is used to determine the data analysis requirement of the target service,
and reading first multi-source heterogeneous data corresponding to the target service from the slave message middleware through the data warehouse tool Hive, performing data analysis on the first multi-source heterogeneous data through Hive based on data analysis requirements to generate a corresponding analysis report, and sending the analysis report to the intelligent management system of the coal washery for display.
The specific method for the modules to perform the operations in the above embodiment has been described in detail in the embodiment related to the method, and will not be elaborated herein.
According to the data docking system for the intelligent management system of the coal washery, data in various data sources can be collected, data collection and output interface standards are unified, and multi-source heterogeneous data are obtained. And then, effective access of various data sources is realized, and the data are written into a relational database of an intelligent management system of the coal washery after certain processing, so that an effective digital scheduling basis is provided for scheduling of the coal washery, and management of the coal washery is optimized.
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 present application. 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. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (12)
1. A data docking method for an intelligent management system of a coal washery is characterized by comprising the following steps:
the method comprises the steps that data collection is carried out from a plurality of data sources based on docking interfaces respectively provided by the data sources of the coal washing plant; the data on each data source is data of equipment of a coal washery;
based on an interface protocol supported by the intelligent management system of the coal washery, uniformly converting the collected multi-source data into data conforming to the interface protocol to obtain multi-source heterogeneous data;
sending the multi-source heterogeneous data to a message middleware;
and reading data from the message middleware, performing data cleaning and/or data aggregation on the read data, and writing the data after the data cleaning and/or the data aggregation into a relational database of the intelligent management system of the coal washery.
2. The method of claim 1, wherein, in the case that the data provided by the plurality of data sources is a small amount of data, the collecting data from the plurality of data sources based on the docking interfaces provided by each of the plurality of data sources of the coal washery comprises:
and data acquisition is carried out from a plurality of data sources by an edge side rule engine eKuiper by adopting docking interfaces respectively provided by the plurality of data sources of the coal washery.
3. The method of claim 2, further comprising:
determining a timing task of a target service;
reading first multi-source heterogeneous data corresponding to the target business from the message middleware;
performing data analysis on the first multi-source heterogeneous data based on the timing task to generate a corresponding analysis report;
and sending the analysis report to the intelligent management system of the coal washery for display.
4. The method of claim 1, wherein, in the case that the data provided by the plurality of data sources is a large amount of data, the collecting data from the plurality of data sources based on the docking interfaces provided by each of the plurality of data sources of the coal washery comprises:
and acquiring data from a plurality of data sources of the coal washing plant by adopting docking interfaces respectively provided by the plurality of data sources through a big data computing engine Spark.
5. The method of claim 4, wherein the reading data from the message middleware, performing data cleaning and/or data aggregation on the read data, and writing the data after data cleaning and/or data aggregation to a relational database of the intelligent management system of the coal washery comprises:
and reading real-time data from the message middleware through a big data stream processing engine, performing data cleaning and/or data aggregation on the read real-time data, and sending the real-time data subjected to data cleaning and/or data aggregation to the intelligent management system of the coal washery for monitoring and displaying.
6. The method of claim 4 or 5, further comprising:
determining the data analysis requirement of the target service;
reading first multi-source heterogeneous data corresponding to the target business from the message middleware through a data warehouse tool Hive pair;
based on the data analysis requirement, performing data analysis on the first multi-source heterogeneous data through the Hive to generate a corresponding analysis report;
and sending the analysis report to the intelligent management system of the coal washery for display.
7. A data docking system for a coal washery intelligent management system, comprising: the system comprises a data acquisition module, a message middleware and a data processing module; wherein the content of the first and second substances,
the data acquisition module is used for acquiring data from a plurality of data sources based on docking interfaces respectively provided by the plurality of data sources of the coal washery; the data on each data source is data of equipment of a coal washing plant, collected multi-source data are uniformly converted into data conforming to an interface protocol based on the interface protocol supported by an intelligent management system of the coal washing plant, so that multi-source heterogeneous data are obtained, and the multi-source heterogeneous data are sent to the message middleware;
and the data processing module is used for reading data from the message middleware, performing data cleaning and/or data aggregation on the read data, and writing the data after the data cleaning and/or data aggregation into a relational database of the intelligent management system of the coal washery.
8. The system of claim 7, wherein, in the case that the data provided by the plurality of data sources is of a small data volume, the data acquisition module is specifically configured to:
and data acquisition is carried out from a plurality of data sources by an edge side rule engine eKuiper by adopting docking interfaces respectively provided by the plurality of data sources of the coal washery.
9. The system of claim 8, further comprising:
and the data analysis module is used for determining a timing task of a target service, reading first multi-source heterogeneous data corresponding to the target service from the message middleware, performing data analysis on the first multi-source heterogeneous data based on the timing task to generate a corresponding analysis report, and sending the analysis report to the intelligent management system of the coal washery for display.
10. The system of claim 7, wherein, in the case that the data provided by the plurality of data sources is of a large data volume, the data acquisition module is specifically configured to:
and acquiring data from a plurality of data sources of the coal washery by a big data computing engine Spark through a docking interface respectively provided by the plurality of data sources.
11. The system of claim 10, wherein the data processing module is specifically configured to:
and reading real-time data from the message middleware through a large data stream processing engine, performing data cleaning and/or data aggregation on the read real-time data, and sending the real-time data subjected to data cleaning and/or data aggregation to the coal washery intelligent management system for monitoring and displaying.
12. The system of claim 10 or 11, further comprising:
the data analysis module is used for determining the data analysis requirement of a target service, reading first multi-source heterogeneous data corresponding to the target service from the message middleware through a data warehouse tool Hive, performing data analysis on the first multi-source heterogeneous data through the Hive based on the data analysis requirement to generate a corresponding analysis report, and sending the analysis report to the intelligent management system of the coal washery for display.
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