CN113220776B - Industrial data processing system and method - Google Patents
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- CN113220776B CN113220776B CN202011422481.7A CN202011422481A CN113220776B CN 113220776 B CN113220776 B CN 113220776B CN 202011422481 A CN202011422481 A CN 202011422481A CN 113220776 B CN113220776 B CN 113220776B
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Abstract
The invention discloses an industrial data processing system and a method, wherein the system comprises a data center platform and a data sensing node; the data perception node comprises an industrial field data perception module and a third-party software system data perception module; the data center platform comprises a data platform data queue, a data cleaning module, a metadata storage module, a data aggregation module, an aggregated data storage module, a data object construction module and a data object storage module. The data processing method realizes the unified processing method of the data by uniformly acquiring various software and hardware system data through the data sensing node, collecting the data with different spaces, different protocols and different types physically to the data queue of the data platform through the distributed data queue, and realizing the data sharing use bottleneck among different systems through data cleaning, data aggregation and data storage, thereby greatly improving the comprehensive use and analysis efficiency of industrial data and providing a data basis for the data-oriented and intelligent development of industrial manufacturing.
Description
Technical Field
The invention relates to an industrial data processing system and method.
Background
With the continuous development of the industrial manufacturing level, on one hand, more and more devices are developed from automation to digitization, and on the other hand, more and more information software systems are applied to industrial production, so that industrial data gradually becomes the core in industrial production activities.
At present, the existing industrial data often exist in a single software system or a single device, the data flow can only exist in the single software system or the single device, and the data chimney structure seriously hinders the circulation of the industrial data among different systems and different devices, so that the analysis of the industrial data has great limitation.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide an industrial data processing system and method, which carry out data processing on production elements, processes and result data comprising a software system and a hardware system through a unified data processing architecture, thereby breaking the isolation between different systems in the data layer.
The purpose of the invention is realized by the following technical scheme: an industrial data processing system comprises a data center platform and data sensing nodes;
the data sensing node comprises an industrial field data sensing module and a third-party software system data sensing module; the industrial field data sensing module monitors parameter and state data of industrial field equipment and simultaneously performs data interaction with the data center platform; the third-party software system data perception module realizes data synchronization of a third-party software system and imports data into a data center platform; the industrial field data sensing module and the third-party software system data sensing module comprise distributed data queues;
the data center platform comprises a data platform data queue, a data cleaning module, a metadata storage module, a data aggregation module, an aggregated data storage module, a data object construction module and a data object storage module; the data platform data queue realizes the aggregation effect on the data of the data sensing node, and caches the data through a storage caching mechanism for the data cleaning module to call and process the data; the data cleaning module calls original data cached in a data queue of the data platform, and cleans and preprocesses the data by using a predefined data processing algorithm to obtain metadata; the metadata storage module is used for carrying out full data storage operation on metadata obtained after data cleaning; the data aggregation module is used for extracting partial data to simulate sample data by acquiring cache data in a data queue of a data platform or metadata after data cleaning according to a specific time range by using a predefined data extraction algorithm, providing a data basis for advanced application of subsequent data and storing the data basis to the aggregated data storage module; the data object constructing module is used for packaging a plurality of metadata into a data entity through a certain combination relation and storing the data entity as a data object to the data object storage module.
Further, the industrial field data perception module comprises:
industrial field data monitoring unit: aiming at different industrial field bus protocols, reading and writing operation of data of bottom equipment is provided, and display and control of information including equipment states and industrial parameters are realized through visual graphics;
a first distributed data queue: providing data synchronization service of field data and a data center platform data queue;
the third-party software system data perception module comprises:
the data synchronization service unit: reading data from a third-party software system periodically or in a triggered manner through a specific strategy, and storing the data into a distributed data queue unit;
a second distributed data queue: and providing a data synchronization service of software data and a data queue of a data center platform.
Further, the factory or the workshop deployed by the first distributed data queue unit is local or off-site.
Furthermore, the data collected by the industrial field data monitoring unit comprises controller data, PLC data, sensor data and equipment data.
Further, the preprocessing in the data cleaning module comprises data protocol conversion, data format conversion, data four-rule operation, multiple data collaborative operation, extremum analysis and mean value analysis.
Further, the data extraction in the data aggregation module includes data extraction of extremum and mean values.
The processing method of the system comprises the following steps:
s1: the data sensing node performs data sensing, and after data is acquired, the step S2 is performed;
s2: performing data storage on a distributed data queue of the data sensing node, and entering step S3;
s3: judging whether to trigger data synchronization service of the distributed data queue and the data platform data queue, if so, entering step S4;
s4: the data platform data queue stores data, and after the data storage is finished, the step S5 is executed;
s5: judging whether to trigger data cleaning operation, if so, entering step S6, otherwise, entering step S7;
s6: executing data cleaning, and after completion, proceeding to step S7;
s7: forming metadata, judging whether to trigger metadata storage, if so, entering step S8, otherwise, entering step S9;
s8: storing the metadata, and entering step S9 after the metadata is stored;
s9: judging whether to trigger data aggregation, if so, entering a step S10, otherwise, entering a step S13;
s10: executing data aggregation operation, and after the data aggregation operation is completed, entering step S11;
s11: judging whether to trigger the storage of aggregated data, if so, entering step S12, otherwise, entering step S13;
s12: executing aggregated data storage, and entering step S13 after the aggregated data storage is finished;
s13: judging whether to trigger the construction of the data object, if so, entering the step S14, otherwise, entering the step S17;
s14, executing the operation of building data object, and entering the step S15 after the operation is finished;
s15: judging whether to trigger data object storage, if so, entering step S16, otherwise, entering step S17;
s16: executing the data object storage operation, and after the data object storage operation is completed, entering step S17;
s17: and (6) ending.
The invention has the beneficial effects that: the invention realizes the unified acquisition of various software and hardware system data through the data sensing node, collects physically different spaces, different protocols and different types of data to the data platform data queue through the data queue distribution, and in addition, realizes the unified data processing method through data cleaning, data aggregation and data storage, breaks through the data island of the existing system, breaks through the bottleneck of data sharing among different existing systems, greatly improves the comprehensive use and analysis efficiency of industrial data, and provides a data basis for the data and intelligent development of industrial manufacturing.
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FIG. 1 is a block diagram of the structure of the present invention;
FIG. 2 is a flow chart of the method of the present invention.
Detailed Description
The technical scheme of the invention is further described in detail by combining the attached drawings: as shown in fig. 1, an industrial data processing system includes a data center platform and data-aware nodes; the data center platform has uniqueness in the architecture, data collection and processing flows are realized, data perception is realized in the architecture, and a plurality of data perception subsystems can be deployed according to data distribution. Specifically, the method comprises the following steps:
the data perception node comprises an industrial field data perception module and a third-party software system data perception module; the industrial field data sensing module monitors parameter and state data of industrial field equipment and simultaneously realizes data interaction with the data center platform; the third-party software system data perception module realizes data synchronization of a third-party software system and imports data into a data center platform; the industrial field data sensing module and the third-party software system data sensing module comprise distributed data queues;
specifically, the data perception nodes are divided into industrial field data perception and third-party software system data perception. The field data perception comprises industrial field data monitoring and distributed data queues, the industrial field data monitoring is mainly used for providing read-write operation on data of bottom equipment aiming at different industrial field bus protocols, the display and control on information such as equipment states, industrial parameters and the like are realized through visual graphs, the distributed data queues are used for providing data synchronization service for the field data and the data platform data queues, and deployed factories or workshops can be local and remote; the data synchronization service reads data from the third-party software system periodically or in a triggering mode through a specific strategy, stores the data into the distributed data queue, and synchronizes node data to the data queue of the data platform according to synchronous configuration of the node and the data center.
The data center platform comprises a data platform data queue, a data cleaning module, a metadata storage module, a data aggregation module, an aggregated data storage module, a data object construction module and a data object storage module;
the data platform data queue realizes the aggregation effect on the data of the data sensing node, and caches data of different sources and different types through a storage caching mechanism so as to enable the data cleaning module to call and process the data;
the data cleaning module is a data consumer of a data platform data queue, calls original data cached in the data platform data queue, and cleans and pre-processes the data by using a predefined data processing algorithm comprising data protocol conversion, data format conversion, data four-rule operation, a plurality of data collaborative operation, extremum analysis and mean value analysis;
the metadata storage module is used for carrying out full data storage operation on metadata obtained after data cleaning, and a structured and semi-structured data storage mode is used; the metadata comprise hardware equipment data and software system data, the hardware data comprise PLC data, sensor data, equipment data and controller data, and the software data mainly relate to a third-party software system database; the third-party software system data comprises product order data (sales data), inventory data, production data (batch data and working procedures, process data), in-out database data and logistics data.
The data aggregation module is used for simulating sample data by acquiring cache data in a data queue of a data platform or metadata after data cleaning according to a specific time range by using a predefined data extraction algorithm comprising an extreme value and an average value, extracting partial data to provide a data base for advanced application of subsequent data, carrying out full storage operation, and storing the data in an aggregated data storage module by using a storage mode of structured and semi-structured data; wherein the high-level applications include: industrial production multi-dimensional data cross analysis, multi-dimensional data visualization, industrial data mining and data deep learning.
The data object construction module is used for packaging a plurality of metadata into one data entity through a certain combination relation, performing full storage operation on the data entity as a data object, and storing the data object to the data object storage module by using a structured data storage mode.
Based on the implementation of the above system, this embodiment further provides a processing method of the system, as shown in fig. 2, including the following steps:
s1: the data sensing node performs data sensing, and after data is acquired, the step S2 is performed;
s2: performing data storage on a distributed data queue of the data sensing node, and entering step S3;
s3: judging whether to trigger data synchronization service of the distributed data queue and the data platform data queue, if so, entering step S4; the triggering mode comprises periodic triggering and quantitative triggering; the periodic triggering means that: triggering data synchronization at specified time intervals, such as every 10 minutes; quantitative triggering means: when the buffer data of the distributed data queue reaches the specified quantity, triggering data synchronization, such as every 100 pieces of data;
s4: the data platform data queue stores data, and after the data storage is finished, the step S5 is executed;
s5: judging whether to trigger data cleaning operation, if so, entering step S6, otherwise, entering step S7;
s6: executing data cleaning, and after completion, proceeding to step S7;
s7: forming metadata, judging whether to trigger metadata storage, if so, entering step S8, otherwise, entering step S9; the triggering mode is as follows: inquiring the metadata configuration parameters in real time, judging whether relevant configuration of metadata storage exists or not, and triggering the metadata storage if the relevant configuration exists;
s8: storing the metadata, and entering step S9 after the metadata is stored;
s9: judging whether to trigger data aggregation, if so, entering a step S10, otherwise, entering a step S13; the triggering mode is as follows: inquiring metadata configuration parameters in real time, judging whether relevant configuration of metadata data aggregation operation exists or not, and triggering metadata storage if relevant configuration exists;
s10: executing data aggregation operation, and after the data aggregation operation is completed, entering step S11;
s11: judging whether to trigger the storage of aggregated data, if so, entering step S12, otherwise, entering step S13; the triggering mode is as follows: inquiring the metadata configuration parameters in real time, judging whether relevant configuration of aggregated data storage exists, and triggering metadata storage if relevant configuration exists;
s12: executing aggregated data storage, and entering step S13 after the aggregated data storage is finished;
s13: judging whether to trigger the construction of the data object, if so, entering the step S14, otherwise, entering the step S17; the triggering mode is as follows: inquiring the metadata configuration parameters in real time, judging whether relevant configuration constructed by the data object exists, and triggering and constructing the data object if the relevant configuration exists;
s14, executing the operation of building data object, and entering the step S15 after the operation is finished;
s15: judging whether data object storage is triggered, if so, entering step S16, otherwise, entering step S17; the triggering mode is as follows: inquiring the metadata configuration parameters in real time, judging whether relevant configuration of data object storage exists or not, and if the relevant configuration exists, triggering data object data storage operation;
s16: executing data object storage operation, and entering S17 after the data object storage operation is completed;
s17: and (6) ending.
Claims (7)
1. An industrial data processing system, characterized by: the data center platform and the data sensing nodes are included;
the data perception node comprises an industrial field data perception module and a third-party software system data perception module; the industrial field data sensing module monitors parameter and state data of industrial field equipment and simultaneously realizes data interaction with the data center platform; the third-party software system data perception module realizes data synchronization of a third-party software system and imports data into a data center platform; the industrial field data sensing module and the third-party software system data sensing module comprise distributed data queues;
the data center platform comprises a data platform data queue, a data cleaning module, a metadata storage module, a data aggregation module, an aggregated data storage module, a data object construction module and a data object storage module; the data platform data queue realizes the aggregation effect on the data of the data sensing node, and caches the data through a storage caching mechanism for the data cleaning module to call and process the data; the data cleaning module calls original data cached in a data queue of the data platform, and cleans and preprocesses the data by using a predefined data processing algorithm to obtain metadata; the metadata comprises hardware equipment data and software system data, the hardware data comprises PLC data, sensor data, equipment data and controller data, the software data relates to the third-party software system database, the third-party software system data comprises product order data, inventory data, production data, warehouse entry and exit data and logistics data, the product order data comprises sales data, and the production data comprises batch data, process data and process data; the metadata storage module is used for carrying out full data storage operation on metadata obtained after data cleaning; the data aggregation module is used for extracting partial data to simulate sample data by acquiring cache data in a data queue of a data platform or metadata after data cleaning according to a specific time range by using a predefined data extraction algorithm, providing a data base for high-level application of subsequent data and storing the data base in the aggregated data storage module; wherein the high-level applications include: cross analysis of industrial production multidimensional data, visualization of multidimensional data, mining of industrial data and deep learning of data; the data object constructing module is used for packaging a plurality of metadata into a data entity through a certain combination relation and storing the data entity as a data object to the data object storage module.
2. An industrial data processing system according to claim 1, wherein: the industrial field data perception module comprises:
industrial field data monitoring unit: aiming at different industrial field bus protocols, reading and writing operation of data of bottom equipment is provided, and display and control of information including equipment states and industrial parameters are realized through visual graphics;
a first distributed data queue: providing data synchronization service of field data and a data queue of a data center platform;
the third-party software system data perception module comprises:
data synchronization service unit: reading data from a third-party software system in a periodic or triggered manner through a specific strategy, and storing the data into a distributed data queue unit;
a second distributed data queue: and providing a data synchronization service of software data and a data queue of a data center platform.
3. An industrial data processing system according to claim 2, wherein: the factory or workshop deployed by the first distributed data queue unit is local or off-site.
4. An industrial data processing system according to claim 2, wherein: the data collected by the industrial field data monitoring unit comprises controller data, PLC data, sensor data and equipment data.
5. An industrial data processing system according to claim 1, wherein: the preprocessing in the data cleaning module comprises data protocol conversion, data format conversion, data four-operation, a plurality of data cooperative operation, extremum analysis and mean value analysis.
6. An industrial data processing system according to claim 5, wherein: and the data extraction in the data aggregation module comprises the data extraction of extreme values and mean values.
7. Method for processing industrial data using an industrial data processing system according to any of claims 1 to 6, characterized in that: the method comprises the following steps:
s1: the data sensing node performs data sensing, and after data is acquired, the step S2 is performed;
s2: performing data storage on a distributed data queue of the data sensing node, and entering step S3;
s3: judging whether to trigger data synchronization service of the distributed data queue and the data platform data queue, if so, entering step S4; the triggering mode comprises periodic triggering and quantitative triggering; the periodic triggering means that: triggering data synchronization at specified time intervals; quantitative triggering refers to: when the amount of the data cached by the distributed data queue reaches a specified amount, triggering data synchronization;
s4: the data platform data queue stores data, and after the data storage is finished, the step S5 is executed;
s5: judging whether to trigger data cleaning operation, if so, entering step S6, otherwise, entering step S7;
s6: executing data cleaning, and after completion, proceeding to step S7;
s7: forming metadata, judging whether metadata storage is triggered, if so, entering a step S8, otherwise, entering a step S9;
s8: storing the metadata, and entering step S9 after the metadata is stored;
s9: judging whether to trigger data aggregation, if so, entering step S10, otherwise, entering step S13;
s10: performing data aggregation operation, and after completion, entering step S11;
s11: judging whether to trigger the storage of aggregated data, if so, entering step S12, otherwise, entering step S13;
s12: executing aggregated data storage, and entering step S13 after the aggregated data storage is finished;
s13: judging whether to trigger the construction of the data object, if so, entering the step S14, otherwise, entering the step S17;
s14, executing the operation of building data object, and entering the step S15 after the operation is finished;
s15: judging whether to trigger data object storage, if so, entering step S16, otherwise, entering step S17;
s16: executing data object storage operation, and entering S17 after the data object storage operation is completed;
s17: and (6) ending.
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Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108762738B (en) * | 2018-04-23 | 2022-03-29 | 北京兰光创新科技有限公司 | Platformized data acquisition system |
| CN109359103A (en) * | 2018-09-04 | 2019-02-19 | 河南智云数据信息技术股份有限公司 | A kind of data aggregate cleaning method and system |
| CN109547240B (en) * | 2018-11-14 | 2022-01-25 | 重庆忽米网络科技有限公司 | Intelligent device based on edge calculation and access and device analysis method |
| CN109799798A (en) * | 2019-01-23 | 2019-05-24 | 上海智共荟智能科技有限公司 | One kind being applied to industrial processing equipment large data sets into acquisition system and method |
| CN109800220B (en) * | 2019-01-29 | 2020-12-15 | 浙江国贸云商企业服务有限公司 | Big data cleaning method, system and related device |
| CN109857732A (en) * | 2019-01-31 | 2019-06-07 | 山东省电子信息产品检验院 | A kind of industry internet platform monitoring data transmission switching method and system |
| CN111464596B (en) * | 2020-03-17 | 2023-08-25 | 石化盈科信息技术有限责任公司 | Data processing system, method, apparatus, computer device, and storage medium |
| CN113325798B (en) * | 2021-07-08 | 2021-11-02 | 宜科(天津)电子有限公司 | Sensor data processing system |
| CN116186136B (en) * | 2023-01-06 | 2024-03-12 | 三峡高科信息技术有限责任公司 | Engineering construction implementation stage data processing method and system |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201374004Y (en) * | 2007-09-26 | 2009-12-30 | 洛克威尔自动控制技术股份有限公司 | Industrial automation system and industrial controller system |
| CN104142662A (en) * | 2013-05-09 | 2014-11-12 | 洛克威尔自动控制技术股份有限公司 | Industrial data analytics in a cloud platform |
| CN105320085A (en) * | 2014-06-25 | 2016-02-10 | 中兴通讯股份有限公司 | Method, apparatus and system for acquiring industrial automation data |
| CN105488235A (en) * | 2016-02-03 | 2016-04-13 | 苏州见微物联网科技有限公司 | Cloud platform data management system based on industrial big data and construction method thereof |
| CN105701181A (en) * | 2016-01-06 | 2016-06-22 | 中电科华云信息技术有限公司 | Dynamic heterogeneous metadata acquisition method and system |
| WO2017010939A1 (en) * | 2015-07-11 | 2017-01-19 | Thinxtream Technologies Pte. Ltd. | Automated computer system and method |
| CN106777227A (en) * | 2016-12-26 | 2017-05-31 | 河南信安通信技术股份有限公司 | Multidimensional data convergence analysis system and method based on cloud platform |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7123974B1 (en) * | 2002-11-19 | 2006-10-17 | Rockwell Software Inc. | System and methodology providing audit recording and tracking in real time industrial controller environment |
| CN103532729B (en) * | 2012-07-05 | 2018-05-04 | 中兴通讯股份有限公司 | The transmission method and device of information |
| CN102801799A (en) * | 2012-08-03 | 2012-11-28 | 国电南瑞科技股份有限公司 | Real-time monitoring system based on B/S architecture |
| CN102902536B (en) * | 2012-09-14 | 2016-04-13 | 胡清河 | A kind of Internet of Things computer system |
| CN103605349B (en) * | 2013-11-26 | 2017-11-14 | 厦门雅迅网络股份有限公司 | A kind of remote real-time data collection and analytic statistics system and method based on CAN bus |
| CN103646111B (en) * | 2013-12-25 | 2017-02-15 | 普元信息技术股份有限公司 | System and method for realizing real-time data association in big data environment |
| CN104683445A (en) * | 2015-01-26 | 2015-06-03 | 北京邮电大学 | Distributed real-time data fusion system |
| CN106096067A (en) * | 2016-08-23 | 2016-11-09 | 成都卡莱博尔信息技术股份有限公司 | There is the mass data platform of data monitoring function |
| CN106339509A (en) * | 2016-10-26 | 2017-01-18 | 国网山东省电力公司临沂供电公司 | Power grid operation data sharing system based on large data technology |
-
2017
- 2017-08-10 CN CN201710680440.XA patent/CN107480244B/en active Active
- 2017-08-10 CN CN202011422481.7A patent/CN113220776B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN201374004Y (en) * | 2007-09-26 | 2009-12-30 | 洛克威尔自动控制技术股份有限公司 | Industrial automation system and industrial controller system |
| CN104142662A (en) * | 2013-05-09 | 2014-11-12 | 洛克威尔自动控制技术股份有限公司 | Industrial data analytics in a cloud platform |
| CN105320085A (en) * | 2014-06-25 | 2016-02-10 | 中兴通讯股份有限公司 | Method, apparatus and system for acquiring industrial automation data |
| WO2017010939A1 (en) * | 2015-07-11 | 2017-01-19 | Thinxtream Technologies Pte. Ltd. | Automated computer system and method |
| CN105701181A (en) * | 2016-01-06 | 2016-06-22 | 中电科华云信息技术有限公司 | Dynamic heterogeneous metadata acquisition method and system |
| CN105488235A (en) * | 2016-02-03 | 2016-04-13 | 苏州见微物联网科技有限公司 | Cloud platform data management system based on industrial big data and construction method thereof |
| CN106777227A (en) * | 2016-12-26 | 2017-05-31 | 河南信安通信技术股份有限公司 | Multidimensional data convergence analysis system and method based on cloud platform |
Non-Patent Citations (2)
| Title |
|---|
| 元数据(Metadata)的发展和相关格式;张敏等;《四川图书馆学报》;20000328(第02期);63-70 * |
| 基于元数据的信息共享平台;张锋等;《中国数字医学》;20080215(第02期);54-57 * |
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| CN107480244A (en) | 2017-12-15 |
| CN113220776A (en) | 2021-08-06 |
| CN107480244B (en) | 2020-12-18 |
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