CN113189942A - Intelligent industrial data analysis system and method - Google Patents
Intelligent industrial data analysis system and method Download PDFInfo
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- CN113189942A CN113189942A CN202110338477.0A CN202110338477A CN113189942A CN 113189942 A CN113189942 A CN 113189942A CN 202110338477 A CN202110338477 A CN 202110338477A CN 113189942 A CN113189942 A CN 113189942A
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- 238000007405 data analysis Methods 0.000 title claims abstract description 33
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- 238000004519 manufacturing process Methods 0.000 claims abstract description 64
- 238000007726 management method Methods 0.000 claims abstract description 10
- 238000009776 industrial production Methods 0.000 claims abstract description 9
- 238000005457 optimization Methods 0.000 claims abstract description 8
- 238000012800 visualization Methods 0.000 claims abstract description 8
- 238000010219 correlation analysis Methods 0.000 claims abstract description 6
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- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention provides an industrial data intelligent analysis system and a method, wherein the system comprises: the industrial data acquisition module is used for collecting industrial production equipment, production lines, warehouses, suppliers and product sales data based on an industrial management system database, a front-end control system, an industrial sensor and an instrument; the data storage module is used for classifying and storing the industrial data through a distributed HDFS cluster; the intelligent analysis module is used for performing problem analysis, problem early warning, correlation analysis, value analysis and result prediction on industrial data based on the constructed data analysis model, and generating an optimization strategy and production guidance aiming at an analysis result; and the visualization module is used for establishing the directional association between the industrial data and the analysis result and displaying the analysis result in a visualization mode in the form of a chart and a three-dimensional model. The scheme can be used for carrying out multi-dimensional comprehensive analysis and utilization on a large amount of large-scale industrial data, fully mining and utilizing the value of the industrial data and providing guidance for subsequent production.
Description
Technical Field
The invention relates to the technical field of industrial Internet of things, in particular to an industrial data intelligent analysis system and method.
Background
With the rapid development of informatization technology and industrial automation, the industrial internet of things gradually becomes the development trend of the future industry. Based on the acquisition and analysis of industrial data, the state of factory equipment and production line can be known in real time, production guidance can be provided for enterprises, and enterprise benefits and core competitiveness are provided.
However, at present, the industrial data analysis can only be performed on a single enterprise or a small-scale plant, and the analysis method also remains in the analysis of a single data index, so that it is difficult to effectively mine and utilize a large amount of industrial data when the production data analysis of a large-scale enterprise or a large number of plants is faced, and the data analysis mode is single.
Disclosure of Invention
In view of this, embodiments of the present invention provide an intelligent industrial data analysis system and method, so as to solve the problems that the existing industrial data analysis method is difficult to effectively mine and utilize, and the data analysis mode is single.
In a first aspect of embodiments of the present invention, an industrial data intelligent analysis system is provided, including:
the industrial data acquisition module is used for collecting industrial production equipment, production lines, warehouses, suppliers and product sales data based on an industrial management system database, a front-end control system, an industrial sensor and an instrument;
the data storage module is used for classifying and storing the industrial data through a distributed HDFS cluster;
the intelligent analysis module is used for constructing a data analysis model, performing problem analysis, problem early warning, correlation analysis, value analysis and result prediction on industrial data based on the data analysis model, and generating an optimization strategy and production guidance aiming at an analysis result;
and the visualization module is used for establishing the directional association between the industrial data and the analysis result and displaying the analysis result in a visualization mode in the form of a chart and a three-dimensional model.
In a second aspect of the embodiments of the present invention, there is provided an intelligent industrial data analysis method, including:
collecting industrial production equipment, production lines, warehouses, suppliers and product sales data based on an industrial management system database, a front-end control system, an industrial sensor and an instrument;
classifying and storing industrial data through a distributed HDFS cluster;
constructing a data analysis model, performing problem analysis, problem early warning, correlation analysis, value analysis and result prediction on industrial data based on the data analysis model, and generating an optimization strategy and production guidance aiming at an analysis result;
and establishing directed correlation between the industrial data and the analysis result, and displaying the analysis result in a visual mode in the form of a chart and a three-dimensional model.
In a third aspect of the embodiments of the present invention, there is provided an electronic device, which at least includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the steps of the method according to the first aspect of the embodiments of the present invention are implemented.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method provided in the first aspect of the embodiments of the present invention.
In the embodiment of the invention, by acquiring the industrial big data, after the industrial big data are classified and stored, performing problem analysis, problem early warning, association analysis, value analysis and result prediction on the industrial data based on the data analysis model, and performing visual display, multi-dimensional comprehensive analysis can be performed on a large amount of large-scale industrial data, the value of the industrial data is fully mined and utilized, reliable basis is provided for subsequent production guidance, and the enterprise income is further improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings described below are only 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 industrial data analysis system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an intelligent industrial data analysis method according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The terms "comprises" and "comprising," when used in this specification and claims, and in the accompanying drawings and figures, are intended to cover non-exclusive inclusions, such that a process, method or system, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements. In addition, "first" and "second" are used to distinguish different objects, and are not used to describe a specific order.
Referring to fig. 1, fig. 1 is a schematic structural diagram of an intelligent industrial data analysis system according to an embodiment of the present invention, including:
an industrial data acquisition module 110, configured to collect industrial production equipment, production lines, warehouses, suppliers, and product sales data based on an industrial management system database, a front-end control system, an industrial sensor, and an instrument;
the industrial related data stored in the industrial management system, such as the production execution system MES, the warehouse management system WMS and other industrial systems, stores corresponding industrial management data. The industrial control system comprises corresponding industrial control parameters, such as control parameters of production equipment such as a mechanical arm and the like, and the industrial sensor and the instrument are used for acquiring working parameters of the equipment during actual operation and detection data of specific operation data.
Preferably, in an embodiment, the industrial data intelligent analysis system further includes a product tracing module, where the product tracing module includes a primary tracing unit and a secondary tracing unit;
the primary tracing unit is used for establishing a primary incidence relation between a product and production equipment and a production line, wherein the primary incidence relation at least comprises product production time information, product detection information, production equipment parameters and production line parameters, and associates the product with a corresponding product warehouse;
the secondary tracing unit is used for establishing an association relationship between a supplier and product production raw materials, production equipment and equipment parts, wherein the association relationship at least comprises supplier information, supply contract information and supply mark information, and associates the product production raw materials, the production equipment and the equipment parts with corresponding warehouses.
The first-level tracing is used for tracing the production product to determine the problems in the production process and tracing the same batch of products, and the second-level tracing is used for tracing the raw materials, equipment, parts and the like provided by suppliers so as to trace the problems of the equipment and the production material and optimize the supply scheme.
The data storage module 120 is used for classifying and storing the industrial data through a distributed HDFS cluster;
the HDFS cluster is a distributed file storage system and has the advantages of high fault tolerance, low deployment cost, high consistency and support of multiple hardware platforms. By adopting the distributed HDFS cluster, the load of mass data network transmission and data processing can be reduced, and meanwhile, distributed storage and processing can be performed on different regions, different projects and different types of industrial data.
Preferably, industrial data is cleaned through data disassembly, data association, intelligent matching, semantic recognition and attribute screening, and data standardization is achieved. For the pre-stored industrial data, due to numerous and complicated sources and different data structures, data cleaning is needed, useless data are clear, and the accuracy of data analysis results is improved.
The intelligent analysis module 130 is used for constructing a data analysis model, performing problem analysis, problem early warning, association analysis, value analysis and result prediction on industrial data based on the data analysis model, and generating an optimization strategy and production guidance aiming at an analysis result;
specifically, based on a machine learning method, regression analysis, cluster analysis, relevance analysis and comparative analysis are performed on industrial data through multi-dimensional data mining to construct a problem analysis model, a problem early warning model, a relevance analysis model, a value analysis model and a result prediction model, and iterative updating is performed through newly acquired industrial data.
And performing problem analysis aiming at industrial data, positioning problems, identifying risks and further performing associated risk analysis.
And setting rules for the stored industrial data, monitoring in real time to perform early warning, and if the data meet the preset rules, acquiring key data and associated data to perform problem early warning analysis.
Relevance analysis is carried out on different types of industrial data, relations among different data are established, the mutual influence relation among different data is determined, and optimization and adjustment of industrial production are facilitated.
And (3) judging the data value of a certain item or a certain type of industrial data, so as to improve the acquisition proportion according to the data value, enhance analysis and adjust the production strategy according to the data, such as judging the value of raw material data, product sales data and the like.
The method can predict the product yield, sales, revenues, cost and profits according to the existing industrial data, and can predict the results of a certain factory or a certain type of enterprises,
And the visualization module 140 is used for establishing directional association between the industrial data and the analysis result, and displaying the analysis result in a visualization manner in the form of a chart or a three-dimensional model.
And correlating the original industrial data, the statistical data of different categories and the analysis result data, and displaying through a chart and a three-dimensional model. Wherein, the chart and the model can be dynamically displayed, and can be displayed in different dimension directions, such as a pie chart, a bar chart, a production process schematic diagram, a statistical table and the like. Different display data can be linked to the original statistical data, and can be independently displayed aiming at a certain data or a certain type of data, and the display mode can be selected.
The method provided by the embodiment can realize the acquisition, storage, analysis and display of industrial data, can carry out multi-dimensional mining analysis on massive different industrial data, and can provide reliable production guidance for enterprises.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, but should not constitute any limitation to the implementation process of the embodiments of the present invention,
fig. 2 is a schematic flow chart of an intelligent industrial data analysis method according to an embodiment of the present invention, where the method includes:
s201, collecting industrial production equipment, production lines, warehouses, suppliers and product sales data based on an industrial management system database, a front-end control system, an industrial sensor and an instrument;
preferably, establishing a primary association relationship between the product and the production equipment and the production line, wherein the primary association relationship at least comprises product production time information, product detection information, production equipment parameters and production line parameters, and associating the product with a corresponding product warehouse;
and establishing an association relationship between a supplier and the raw material, the production equipment and the equipment parts for product production, wherein the association relationship at least comprises supplier information, supply contract information and supply target information, and associating the raw material, the production equipment and the equipment parts for product production with the corresponding warehouse.
S202, classifying and storing industrial data through a distributed HDFS cluster;
preferably, industrial data is cleaned through data disassembly, data association, intelligent matching, semantic recognition and attribute screening, and data standardization is achieved.
S203, constructing a data analysis model, performing problem analysis, problem early warning, correlation analysis, value analysis and result prediction on industrial data based on the data analysis model, and generating an optimization strategy and production guidance aiming at an analysis result;
specifically, based on a machine learning method, regression analysis, cluster analysis, relevance analysis and comparative analysis are performed on industrial data through multi-dimensional data mining to construct a problem analysis model, a problem early warning model, a relevance analysis model, a value analysis model and a result prediction model, and iterative updating is performed through newly acquired industrial data.
And S204, establishing directed association between the industrial data and the analysis result, and displaying the analysis result in a visual mode in a chart and three-dimensional model mode.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
It is understood that, in one embodiment, the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program performs steps S201 to S204 in the first embodiment, and the processor implements intelligent analysis of industrial data when executing the computer program.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by using a program to instruct related hardware, where the program may be stored in a computer-readable storage medium, and when the program is executed, the program includes steps S201 to S204, and the storage medium includes, for example, ROM/RAM.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; 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 (8)
1. An industrial data intelligent analysis system, comprising:
the industrial data acquisition module is used for collecting industrial production equipment, production lines, warehouses, suppliers and product sales data based on an industrial management system database, a front-end control system, an industrial sensor and an instrument;
the data storage module is used for classifying and storing the industrial data through a distributed HDFS cluster;
the intelligent analysis module is used for constructing a data analysis model, performing problem analysis, problem early warning, correlation analysis, value analysis and result prediction on industrial data based on the data analysis model, and generating an optimization strategy and production guidance aiming at an analysis result;
and the visualization module is used for establishing the directional association between the industrial data and the analysis result and displaying the analysis result in a visualization mode in the form of a chart and a three-dimensional model.
2. The system of claim 1, wherein the industrial data intelligent analysis system further comprises a product tracing module, wherein the product tracing module comprises a primary tracing unit and a secondary tracing unit;
the primary tracing unit is used for establishing a primary incidence relation between a product and production equipment and a production line, wherein the primary incidence relation at least comprises product production time information, product detection information, production equipment parameters and production line parameters, and associates the product with a corresponding product warehouse;
the secondary tracing unit is used for establishing an association relationship between a supplier and product production raw materials, production equipment and equipment parts, wherein the association relationship at least comprises supplier information, supply contract information and supply mark information, and associates the product production raw materials, the production equipment and the equipment parts with corresponding warehouses.
3. The system of claim 1, wherein before the classifying and storing the industrial data by the distributed HDFS cluster, further comprises:
the industrial data is cleaned through data disassembly, data association, intelligent matching, semantic recognition and attribute screening, and data standardization is achieved.
4. The system of claim 1, wherein the constructing of the data analysis model, and the performing of problem analysis, problem pre-warning, association analysis, value analysis and result prediction on the industrial data based on the data analysis model comprises:
based on a machine learning method, through multi-dimensional data mining, regression analysis, cluster analysis, relevance analysis and comparative analysis are carried out on industrial data to construct a problem analysis model, a problem early warning model, a relevance analysis model, a value analysis model and a result prediction model, and iteration updating is carried out through newly acquired industrial data.
5. An intelligent industrial data analysis method is characterized by comprising the following steps:
collecting industrial production equipment, production lines, warehouses, suppliers and product sales data based on an industrial management system database, a front-end control system, an industrial sensor and an instrument;
classifying and storing industrial data through a distributed HDFS cluster;
constructing a data analysis model, performing problem analysis, problem early warning, correlation analysis, value analysis and result prediction on industrial data based on the data analysis model, and generating an optimization strategy and production guidance aiming at an analysis result;
and establishing directed correlation between the industrial data and the analysis result, and displaying the analysis result in a visual mode in the form of a chart and a three-dimensional model.
6. The method of claim 5, wherein collecting industrial production equipment, production lines, warehouses, suppliers, product sales data further comprises:
establishing a primary association relation between a product and production equipment and a production line, wherein the primary association relation at least comprises product production time information, product detection information, production equipment parameters and production line parameters, and associating the product with a corresponding product warehouse;
and establishing an association relationship between a supplier and the raw material, the production equipment and the equipment parts for product production, wherein the association relationship at least comprises supplier information, supply contract information and supply target information, and associating the raw material, the production equipment and the equipment parts for product production with the corresponding warehouse.
7. The method according to claim 5, wherein before the classifying and storing the industrial data by the distributed HDFS cluster, the method further comprises:
the industrial data is cleaned through data disassembly, data association, intelligent matching, semantic recognition and attribute screening, and data standardization is achieved.
8. The method of claim 5, wherein the constructing the data analysis model, and performing problem analysis, problem pre-warning, association analysis, value analysis and result prediction on the industrial data based on the data analysis model comprises:
based on a machine learning method, through multi-dimensional data mining, regression analysis, cluster analysis, relevance analysis and comparative analysis are carried out on industrial data to construct a problem analysis model, a problem early warning model, a relevance analysis model, a value analysis model and a result prediction model, and iteration updating is carried out through newly acquired industrial data.
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| CN116132317A (en) * | 2022-12-12 | 2023-05-16 | 南京理工大学 | Industrial Internet data acquisition analysis and visualization integrated system and deployment method thereof |
| CN116523466A (en) * | 2023-05-06 | 2023-08-01 | 福建凯邦锦纶科技有限公司 | Production data tracing system and method based on big data |
| CN117575623A (en) * | 2023-11-08 | 2024-02-20 | 浙江伟众科技有限公司 | Air conditioner hose product manufacturing traceability management system |
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