CN111932071A - Industrial production quality analysis early warning method and system - Google Patents
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Abstract
The invention provides an industrial production quality analysis early warning method and system, which can realize the analysis and early warning of the quality of industrial production products, and analyze the product data produced on site by industrial equipment through an industrial production quality early warning analysis platform. And then, a data instrument panel is created to combine and display various data fields, defective products and reported waste products of the products are finally analyzed, the production efficiency and the yield of the equipment are monitored, the equipment with high waste products is found in time, and problems are found in time. Automatic carry out parameter optimization to production facility, improve the product yields, reduction in production cost reduces the artifical quality inspector of mill, improves the mill intellectuality.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to an industrial production quality analysis early warning method and system.
Background
Currently, industrial enterprises still stay in an original state and rely on a large amount of manpower. In the past, most of the management and control of enterprises on the quality of industrial products are preset or changed by the experience of teachers, so that a large amount of manpower and time are wasted, and the actual operators are required to have strong experience. Moreover, the experience is often not reproducible, and the person can only have a certain experience through long-time and large-scale accumulation. Industrial production data are dispersed in different enterprise equipment or systems, and early warning means usually identify through a large number of teachers, and then manually calculate the optimization strategy of the production equipment, so that the hysteresis is realized.
Patent document CN110879820A provides an industrial data processing method and device, which collects industrial data generated in at least a part of production processes; associating each industrial data according to the process time of each acquired industrial data; the method is characterized in that each industrial data after time correlation is subjected to abnormity detection, discrete production processes are spliced through an effective data processing technology, and a full production link is analyzed to achieve the purpose of quick positioning.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide an industrial production quality analysis early warning method and system.
The industrial production quality analysis early warning method provided by the invention comprises the following steps:
accessing a data source: configuring different access interfaces according to different data sources, setting an automatic operation process in the access interfaces, and accessing the data sources from an industrial production data platform;
a data set creating step: creating a data set by using a table or SQL in a data source, and performing association and renaming operations on a plurality of data sets to form a basic data set;
analyzing and early warning steps: displaying the basic data set in a form of a chart board, highlighting key fields in the basic data set, adding an analysis early warning identifier on the chart board, and automatically generating an analysis early warning result;
adjusting and optimizing equipment: setting a threshold value, judging the analysis early warning result to form a tuning parameter, and issuing the tuning parameter to the industrial production equipment through the data gateway so as to tune the industrial production equipment.
Preferably, the data source comprises any one or more of an on-cloud data source, a local self-built data source, a file-type data source and an application data source of an industrial production device.
Preferably, the creating a data set step comprises:
selecting data: logging in an industrial analysis early warning system, selecting a working space, selecting a data source, wherein the data source is a relational or non-relational database, finding a target data table, selecting operation and creating a data set;
and (3) associating data: the data set is associated with a plurality of data tables from the same data source, and the plurality of data tables are associated in a snowflake model or star model mode through an association table function;
and a data caching step: starting the data set cache, presetting cache time, and after the preset cache time is exceeded, the cache is invalid,
preferably, the analyzing and early warning step comprises:
determining a detection range: determining a detection range and detection content of product quality analysis early warning, wherein the detection range of the product quality analysis early warning is set based on actual use places, and the product defect rates accepted by different products are different;
a state collecting step: collecting historical operation data of the plant equipment within the product quality detection range, wherein the historical operation data comprises the normal operation state, performance, yield and equipment operation temperature of the plant equipment;
setting an early warning step: setting an early warning threshold, wherein the early warning threshold is set based on the analysis result of the historical operation data of the factory equipment in the equipment detection range; and if the early warning threshold value is exceeded, sending an early warning notice.
Preferably, the tuning device step includes:
presetting parameters: historical data analysis is carried out on all equipment of the factory within set time, the historical data analysis comprises the productivity, the equipment performance, the temperature and the cost for producing a single product, the optimal parameters in the historical data are found out by analyzing the historical data to form an analysis result, and the optimal parameters are set as preset parameters and stored;
a step of determining equipment: determining equipment needing to be tuned, and performing parameter tuning on the equipment exceeding a threshold value according to an analysis result;
parameter issuing step: and issuing the parameters to industrial production equipment through the data gateway service, and changing the equipment parameters to achieve the optimal parameters.
The invention provides an industrial production quality analysis early warning system, which comprises:
the data source accessing module: configuring different access interfaces according to different data sources, setting an automatic operation process in the access interfaces, and accessing the data sources from an industrial production data platform;
a create dataset module: creating a data set by using a table or SQL in a data source, and performing association and renaming operations on a plurality of data sets to form a basic data set;
an analysis early warning module: displaying the basic data set in a form of a chart board, highlighting key fields in the basic data set, adding an analysis early warning identifier on the chart board, and automatically generating an analysis early warning result;
a tuning equipment module: setting a threshold value, judging the analysis early warning result to form a tuning parameter, and issuing the tuning parameter to the industrial production equipment through the data gateway so as to tune the industrial production equipment.
Preferably, the create data set module comprises:
selecting a data module: selecting a working space where industrial production equipment is located, selecting a data source, wherein the data source is a relational or non-relational database, finding a target data table, selecting operation, and creating a data set;
a correlation data module: the data set is associated with a plurality of data tables from the same data source, and the data tables are associated in a snowflake model or star model mode through an association table function to form a basic data set;
a data caching module: and starting the data set cache, presetting cache time, and after the preset cache time is exceeded, the cache is invalid, and re-triggering the query to generate a new cache.
Only when the report page triggers the query, a new cache is generated, and the same SQL query statement is fetched from the cache. The cache takes the data set as granularity, and all graph cache data related to the data set can be cleared when the cache is cleared. After the predetermined buffering time is exceeded, all graph buffers of the data set are cleared.
Preferably, the analysis and early warning module includes:
a detection range determining module: determining a detection range and detection content of product quality analysis early warning, wherein the detection range of the product quality analysis early warning is set based on actual use places, and the product defect rates accepted by different products are different;
a collection status module: collecting historical operation data of the plant equipment within the product quality detection range, wherein the historical operation data comprises the normal operation state, performance, yield and equipment operation temperature of the plant equipment;
setting an early warning module: setting an early warning threshold, wherein the early warning threshold is set based on the analysis result of the historical operation data of the factory equipment in the equipment detection range; and if the early warning threshold value is exceeded, sending an early warning notice.
Preferably, the tuning device module includes:
a preset parameter module: analyzing historical data of all factory equipment within set time, wherein the historical data analysis comprises capacity, equipment performance, temperature and cost for producing a single product, finding out optimal parameters in the historical data by analyzing the historical data to form an analysis result, and setting the optimal parameters as preset parameters and storing the optimal parameters;
determining an equipment module: determining factory equipment needing to be adjusted and optimized, and performing parameter adjustment and optimization on the factory equipment exceeding the early warning threshold according to an analysis result;
a parameter issuing module: and issuing the parameters to the industrial production equipment through the data gateway service, and changing the parameters of the industrial production equipment to achieve the optimal parameters.
Compared with the prior art, the invention has the following beneficial effects:
1. the method can accurately acquire the production quality early warning information in real time by adopting industrial production quality analysis early warning, and automatically adjust and optimize the parameters of the equipment reaching the threshold value so as to ensure that the industrial equipment reaches the optimal state, and change the condition that the parameter adjustment and optimization can be carried out only through manual experience in the past.
2. The data source is automatically associated, early-warning and optimized, so that the unified management of industrial enterprise data and the closed loop of industrial data are realized.
3. The current industrial data is analyzed from a plurality of angles by setting a plurality of threshold values, the running state of the industrial equipment is fed back in real time, and real-time deviation correction is carried out to optimize production.
4. Automatic carry out parameter optimization to production facility, improve the product yields, reduction in production cost reduces the artifical quality inspector of mill, improves the mill intellectuality.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic diagram of the framework of the present invention.
FIG. 2 is a schematic process flow diagram of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
Example 1
The industrial production quality analysis early warning method is used for butting all data layers through a self-research industrial data analysis platform and allowing users to butt private data, rich data sources are provided for self-service data analysis, and the data sources are used as the basis of data analysis and generally used by technical users such as IT/data research and development. At present, the quality analysis of industrial production supports four types of data sources on the cloud, self-built data sources, file type data sources and application data sources. The method comprises the steps that product data produced on site by industrial equipment are analyzed through an industrial production quality early warning analysis platform, data connection is conducted on the industrial data and is connected to the industrial production quality early warning analysis platform, after the data are connected, a data set needs to be created, data modeling needs to be built, and preparation work of data analysis is completed. And then, a data instrument panel is created to combine and display various data fields, defective products and reported waste products of the products are finally analyzed, the production efficiency and the yield of the equipment are monitored, the equipment with high waste products is found in time, and problems are found in time.
As shown in fig. 2, the industrial production quality analysis early warning step includes:
step S1: and the hdfs data is accessed, and the operation flow of an analyst is simplified by configuring interface access, so that the analyst without development experience can use the data.
Step S2: and creating a data set, wherein the data set is used as a data source and a middle link of visual display, accepts the input of the data source and outputs a data table for the visual display. Generally, IT is used by users who need data processing, such as IT/data research/data analysts.
In data set management, a data set can be created using a table or SQL in a data source, and a series of operations such as association, secondary data processing analysis, editing, renaming, and the like can be performed on the data set.
Step S3: the data analysis comprises the following steps:
and S3.1, designing a diagram, wherein the diagram board displays the interaction of report data by adopting a flexible magnetic-pasting layout, and the diagram board not only can present the data in a visual mode, but also supports the highlighting of key fields in the data by screening and inquiring various data and using various data presentation modes.
From the aspect of data display, the chart board leads, drags and double-clicks the field to display the data more visually and clearly; from the aspect of data analysis, the interactive experience of the user is improved through friendly prompts. The display performance of the data is improved greatly, and the dynamic data can be inquired on an edit page of the instrument board.
And S3.2, analyzing and early warning, wherein the analyzing and early warning supports the analysis of the current industrial data from multiple angles, and the change trend, abnormal points and the like of the industrial data can be intuitively known through the function.
The analysis early warning mainly comprises five analysis modes of an auxiliary line, a trend line, prediction, anomaly detection and fluctuation reason analysis, and the analysis early warning can be automatically generated as long as a user selects the corresponding analysis early warning mode.
Step S4: analyzing the early warning data to optimize the production equipment, analyzing the early warning data to reach a certain threshold value, presetting parameters, then issuing the parameters to the industrial production equipment through the data gateway service, and performing parameter optimization on the production equipment.
Example 2
Embodiment 2 can be regarded as a preferable example of embodiment 1. The industrial production quality analysis early-warning system described in embodiment 2 utilizes the steps of the industrial production quality analysis early-warning method described in embodiment 1.
As shown in fig. 1, an industrial production quality analysis early warning system includes:
the data source accessing module: configuring different access interfaces according to different data sources, setting an automatic operation process in the access interfaces, and accessing the data sources from an industrial production data platform;
a create dataset module: creating a data set by using a table or SQL in a data source, and performing association and renaming operations on a plurality of data sets to form a basic data set;
an analysis early warning module: displaying the basic data set in a form of a chart board, highlighting key fields in the basic data set, adding an analysis early warning identifier on the chart board, and automatically generating an analysis early warning result; the analysis early warning mark comprises any one or more of an auxiliary line, a trend line, prediction, abnormity detection and fluctuation reason mark.
A tuning equipment module: setting a threshold value, judging the analysis early warning result to form a tuning parameter, and issuing the tuning parameter to the industrial production equipment through the data gateway so as to tune the industrial production equipment.
The data source comprises any one or more of an on-cloud data source, a local self-built data source, a file-type data source and an application data source of an industrial production device.
The create dataset module comprises:
selecting a data module: selecting a working space where industrial production equipment is located, selecting a data source, wherein the data source is a relational or non-relational database, finding a target data table, selecting operation, and creating a data set;
a correlation data module: the data set is associated with a plurality of data tables from the same data source, and the data tables are associated in a snowflake model or star model mode through an association table function to form a basic data set; for example, table a associates table B and table B associates table C, and the system automatically adds the associated fields to the dimension and metric list of table a in a folder manner.
A data caching module: and starting the data set cache, presetting cache time, and after the preset cache time is exceeded, the cache is invalid, and re-triggering the query to generate a new cache. The caching time is set to be 30 minutes or 1 hour, new caching is carried out only when the report page triggers query, and the number of the same SQL query statement is extracted from the caching. The cache takes the data set as granularity, and all graph cache data related to the data set can be cleared when the cache is cleared. After the predetermined buffering time is exceeded, all graph buffers of the data set are cleared.
The analysis early warning module comprises:
a detection range determining module: determining a detection range and detection content of product quality analysis early warning, wherein the detection range of the product quality analysis early warning is set based on actual use places, and the product defect rates accepted by different products are different;
a collection status module: collecting historical operation data of the plant equipment within the product quality detection range, wherein the historical operation data comprises the normal operation state, performance, yield and equipment operation temperature of the plant equipment;
setting an early warning module: setting an early warning threshold, wherein the early warning threshold is set based on the analysis result of the historical operation data of the factory equipment in the equipment detection range; and if the early warning threshold value is exceeded, sending an early warning notice. The notification is to notify the equipment management personnel by means of short message, telephone, WeChat and the like.
The tuning equipment module comprises:
a preset parameter module: analyzing historical data of all factory equipment within set time, wherein the historical data analysis comprises capacity, equipment performance, temperature and cost for producing a single product, finding out optimal parameters in the historical data by analyzing the historical data to form an analysis result, and setting the optimal parameters as preset parameters and storing the optimal parameters; for example, setting the time selection to be in the morning of each day.
Determining an equipment module: determining factory equipment needing to be adjusted and optimized, and performing parameter adjustment and optimization on the factory equipment exceeding the early warning threshold according to an analysis result;
a parameter issuing module: and issuing the parameters to the industrial production equipment through the data gateway service, and changing the parameters of the industrial production equipment to achieve the optimal parameters.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (10)
1. An industrial production quality analysis early warning method is characterized by comprising the following steps:
accessing a data source: configuring different access interfaces according to different data sources, setting an automatic operation process in the access interfaces, and accessing the data sources from an industrial production data platform;
a data set creating step: creating a data set by using a table or SQL in a data source, and performing association and renaming operations on a plurality of data sets to form a basic data set;
analyzing and early warning steps: displaying the basic data set in a form of a chart board, highlighting key fields in the basic data set, adding an analysis early warning identifier on the chart board, and automatically generating an analysis early warning result;
adjusting and optimizing equipment: setting a threshold value, judging the analysis early warning result to form a tuning parameter, and issuing the tuning parameter to the industrial production equipment through the data gateway so as to tune the industrial production equipment.
2. The industrial production quality analysis early warning method according to claim 1, wherein the data source comprises any one or more of an on-cloud data source, a local self-built data source, a file-type data source and an application data source of an industrial production device.
3. The industrial production quality analysis early warning method of claim 1, wherein the creating a data set step comprises:
selecting data: logging in an industrial analysis early warning system, selecting a working space, selecting a data source, wherein the data source is a relational or non-relational database, finding a target data table, selecting operation and creating a data set;
and (3) associating data: the data set is associated with a plurality of data tables from the same data source, and the plurality of data tables are associated in a snowflake model or star model mode through an association table function;
and a data caching step: and starting the data set cache, presetting cache time, and failing the cache after the preset cache time is exceeded.
4. The industrial production quality analysis early warning method according to claim 1, wherein the analysis early warning step comprises:
determining a detection range: determining a detection range and detection content of product quality analysis early warning, wherein the detection range of the product quality analysis early warning is set based on actual use places, and the product defect rates accepted by different products are different;
a state collecting step: collecting historical operation data of the plant equipment within the product quality detection range, wherein the historical operation data comprises the normal operation state, performance, yield and equipment operation temperature of the plant equipment;
setting an early warning step: setting an early warning threshold, wherein the early warning threshold is set based on the analysis result of the historical operation data of the factory equipment in the equipment detection range; and if the early warning threshold value is exceeded, sending an early warning notice.
5. The industrial production quality analysis early warning method according to claim 4, wherein the tuning device step comprises:
presetting parameters: historical data analysis is carried out on all equipment of the factory within set time, the historical data analysis comprises the productivity, the equipment performance, the temperature and the cost for producing a single product, the optimal parameters in the historical data are found out by analyzing the historical data to form an analysis result, and the optimal parameters are set as preset parameters and stored;
a step of determining equipment: determining equipment needing to be tuned, and performing parameter tuning on the equipment exceeding a threshold value according to an analysis result;
parameter issuing step: and issuing the parameters to industrial production equipment through the data gateway service, and changing the equipment parameters to achieve the optimal parameters.
6. An industrial production quality analysis early warning system, characterized by, includes:
the data source accessing module: configuring different access interfaces according to different data sources, setting an automatic operation process in the access interfaces, and accessing the data sources from an industrial production data platform;
a create dataset module: creating a data set by using a table or SQL in a data source, and performing association and renaming operations on a plurality of data sets to form a basic data set;
an analysis early warning module: displaying the basic data set in a form of a chart board, highlighting key fields in the basic data set, adding an analysis early warning identifier on the chart board, and automatically generating an analysis early warning result;
a tuning equipment module: setting a threshold value, judging the analysis early warning result to form a tuning parameter, and issuing the tuning parameter to the industrial production equipment through the data gateway so as to tune the industrial production equipment.
7. The industrial production quality analysis early warning system of claim 1, wherein the data source comprises any one or more of an on-cloud data source, a local self-built data source, a file-type data source, and an application data source of an industrial production device.
8. The industrial production quality analysis early warning system of claim 1, wherein the create data set module comprises:
selecting a data module: selecting a working space where industrial production equipment is located, selecting a data source, wherein the data source is a relational or non-relational database, finding a target data table, selecting operation, and creating a data set;
a correlation data module: the data set is associated with a plurality of data tables from the same data source, and the data tables are associated in a snowflake model or star model mode through an association table function to form a basic data set;
a data caching module: and starting the data set cache, presetting cache time, and after the preset cache time is exceeded, the cache is invalid, and re-triggering the query to generate a new cache.
Only when the report page triggers the query, a new cache is generated, and the same SQL query statement is fetched from the cache. The cache takes the data set as granularity, and all graph cache data related to the data set can be cleared when the cache is cleared. After the predetermined buffering time is exceeded, all graph buffers of the data set are cleared.
9. The industrial production quality analysis and forewarning system of claim 1, wherein the analysis and forewarning module comprises:
a detection range determining module: determining a detection range and detection content of product quality analysis early warning, wherein the detection range of the product quality analysis early warning is set based on actual use places, and the product defect rates accepted by different products are different;
a collection status module: collecting historical operation data of the plant equipment within the product quality detection range, wherein the historical operation data comprises the normal operation state, performance, yield and equipment operation temperature of the plant equipment;
setting an early warning module: setting an early warning threshold, wherein the early warning threshold is set based on the analysis result of the historical operation data of the factory equipment in the equipment detection range; and if the early warning threshold value is exceeded, sending an early warning notice.
10. The industrial production quality analysis early warning system of claim 9, wherein the tuning equipment module comprises:
a preset parameter module: analyzing historical data of all factory equipment within set time, wherein the historical data analysis comprises capacity, equipment performance, temperature and cost for producing a single product, finding out optimal parameters in the historical data by analyzing the historical data to form an analysis result, and setting the optimal parameters as preset parameters and storing the optimal parameters;
determining an equipment module: determining factory equipment needing to be adjusted and optimized, and performing parameter adjustment and optimization on the factory equipment exceeding the early warning threshold according to an analysis result;
a parameter issuing module: and issuing the parameters to the industrial production equipment through the data gateway service, and changing the parameters of the industrial production equipment to achieve the optimal parameters.
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| CN113723781A (en) * | 2021-08-19 | 2021-11-30 | 武汉慧远智控科技有限公司 | Product quality defect judgment system and method based on SPC analysis |
| CN114964594A (en) * | 2022-04-28 | 2022-08-30 | 海门市恒昌织带有限公司 | Safety braid dyeing quality detection method based on industrial data processing |
| CN115090855A (en) * | 2022-06-30 | 2022-09-23 | 中国联合网络通信集团有限公司 | Control method, device and equipment for part machining |
| CN117236799A (en) * | 2023-11-14 | 2023-12-15 | 山东焱鑫矿用材料加工有限公司 | Production quality control system of hollow grouting anchor cable |
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