CN113721560B - Open collaborative manufacturing cloud architecture system oriented to bottom data and implementation method - Google Patents
Open collaborative manufacturing cloud architecture system oriented to bottom data and implementation method Download PDFInfo
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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
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Abstract
The open collaborative manufacturing cloud architecture system facing the bottom data is applied to an intelligent workshop and comprises an intelligent analysis module, a data service module, a workshop management module and a workshop core function module, wherein the intelligent analysis module is used for collecting, processing and integrating big data in the workshop manufacturing process; the data service module is used for storing, processing and applying the manufacturing process data uploaded by the intelligent analysis module, and the workshop management module integrates functions of ERP, PLM and the like; the workshop MES core function module and the workshop management module work cooperatively and are used for executing and managing the production process of a manufacturing enterprise; the intelligent analysis module provides that the process data acquisition model screens and integrates the original data to form a process data set which can be used for analysis, so that a manager can grasp the current production capacity level of the manufacturing system dynamically.
Description
Technical Field
The invention relates to the field of big data processing, in particular to an open collaborative manufacturing cloud architecture system facing to bottom data and applied to an intelligent workshop and an implementation method.
Background
Today science and technology are rapidly developing, and industry traditional work forms face unprecedented challenges, especially in terms of ensuring profitability and long-term viability, owners of industries such as chemical industry, electric power, transportation, manufacturing, and the like.
The intelligent workshop takes the whole improvement of product production as a core, focuses on the improvement of production management capability, the improvement of product quality, the improvement of customer demand oriented timely delivery capability, the improvement of product inspection equipment capability, the improvement of safety production capability, the improvement of production equipment capability, the improvement of workshop informatization construction, the improvement of workshop logistics capability, the improvement of workshop energy management capability and the like.
The big data of the manufacturing process is taken as a type of production resource of an intelligent workshop, is a basis for reflecting the essential characteristics and the operation rules of a production system, and is widely focused in the industry. However, the prior art has few researches on large data of discrete manufacturing, has defects in information integration and penetration, cannot overcome the difficulty brought by the complexity of a manufacturing system from the characteristic of the discrete manufacturing flow, and has insufficient prediction research on the whole process of the manufacturing process and the production capacity of the manufacturing system.
Disclosure of Invention
The invention provides an open collaborative manufacturing cloud architecture system for bottom data and an implementation method thereof, which are applied to an intelligent workshop and aim to solve the complexity problems of data acquisition and integration of the intelligent workshop discrete manufacturing system.
The embodiment of the invention provides an open collaborative manufacturing cloud architecture system facing to bottom data, which comprises the following components: a big data modeling analysis system applied to an intelligent workshop is characterized in that the system comprises an intelligent analysis module, a data service module, a workshop management module and a workshop core function module,
the intelligent analysis module is used for collecting, processing and integrating workshop manufacturing process data generated by the automatic equipment, the Internet of things equipment and the intelligent sensing equipment of the manufacturing resource layer, and uploading the processed manufacturing process data and the original data to the data service module;
the data service module is used for storing, processing and applying the manufacturing process data uploaded by the intelligent analysis module;
the plant management module integrates the management modules of enterprise resource planning Enterprise Resource Planning ERP, product lifecycle management Product Lifecycle Management PLM, and computer-aided process design Computer Aided Process Planning CAPP functions;
the workshop MES core function module and the workshop management module work cooperatively and are used for executing and managing the production process of a manufacturing enterprise, and specifically comprises the steps of workshop progress management, task management, equipment management, process management, tooling management and personnel management;
the intelligent analysis module can integrate the acquired manufacturing process data into a procedure manufacturing process data set according to the manufacturing process data acquisition model according to the respective feature description, and store and analyze the procedure manufacturing process data set.
The embodiment of the invention also provides a realization method applied to the open collaborative manufacturing cloud architecture system facing the bottom data, which comprises the following steps of
(1) Collecting manufacturing process data;
(2) And integrating the collected manufacturing process data into a procedure manufacturing process data set according to the respective characteristic description and a manufacturing process data collection model, and storing and analyzing the procedure manufacturing process data set, wherein the manufacturing process data collection model is as follows:
M＝(I，V，T，D，L，F)
m is a manufacturing process data acquisition model, I is an entity data attribute and comprises a data number; v is a data type; t is the occurrence time; d is a data description; l is the occurrence position; f is the functional classification. The model describes the internal data composition of the data object from a space-time and a functional multi-angle.
The manufacturing process data collected comprises four layers of data of equipment, a process, indexes and a user, wherein the equipment layer comprises real-time collected equipment state time sequence data, the process layer comprises collected related materials, personnel operation and sensing control data, the index layer comprises on-line quality monitoring data, production efficiency and progress, and the user layer comprises feedback data of the user on products.
In addition, as can be seen from other embodiments, the method further comprises the steps of realizing communication with various types of equipment through an RS232 interface, collecting state data in the processing and production processes of the equipment in real time, simultaneously obtaining data of the intelligent ammeter, related industrial sensors and equipment tools through an RS485 or ModBus protocol, and collecting flow information, personnel information and process quality real-time input information of the products in real time through intelligent labels such as RFID.
Additional aspects, features and/or advantages of the invention 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 invention.
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These and/or other aspects, features and advantages of the present invention will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, wherein:
fig. 1 is a schematic diagram of an open collaborative manufacturing cloud architecture system for underlying data according to an embodiment of the present invention
FIG. 2 is a flow chart of data collection by an intelligent analysis module in an open collaborative manufacturing cloud architecture system for bottom data provided by an embodiment of the invention
Detailed Description
Reference will now be made in detail to exemplary embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the like elements throughout. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The exemplary embodiments are described below to explain the present invention by referring to the figures.
The open collaborative manufacturing cloud architecture system for the bottom data, which adopts a discrete manufacturing intelligent workshop, aims at completing a specified production plan, and is an organic whole body with discrete processing assembly characteristics and composed of manufacturing resources which participate in procedure manufacturing, wherein the discrete manufacturing refers to the production of a single item, the data input of the manufacturing resources in the manufacturing process is intermittent, the products are complex and various, and the data quantity of the derived manufacturing process is large and non-uniform. The intelligent workshop open type collaborative manufacturing cloud architecture system and the implementation method thereof can be used for realizing the collection, marking and encapsulation of multi-level manufacturing process big data based on a workshop intelligent terminal construction process big data business process framework based on the workshop intelligent terminal and taking the system pre-known maintenance requirement as a background, constructing a set of working condition visualization monitoring system suitable for intelligent workshop discrete manufacturing production management, and realizing multi-level and multi-target working condition intelligent monitoring and pre-known maintenance.
Taking a device part workshop as an example, as shown in fig. 1, the system comprises an intelligent analysis module, a data service module, a workshop management module and a workshop core function module, wherein the intelligent analysis module is used for collecting, processing and integrating workshop manufacturing process big data generated by automatic equipment, internet of things equipment, intelligent sensing equipment and the like of a manufacturing resource layer, and uploading the processed manufacturing process data and the original data to the data service module together so as to ensure real-time performance, integrity and correctness of manufactured data management.
The data service module is used for storing, processing and applying the manufacturing process data uploaded by the intelligent analysis module. Firstly, the module adopts a distributed file system (hadoop distributed file system) HDFS to store uploaded source data, then realizes data processing based on a core computing framework (MapReduce), and finally further organizes the processed data by an open source database (hadoop database) HBase, and provides an access interface and a data basis for a workshop management module and a workshop MES core function layer.
The plant management module integrates enterprise resource planning (Enterprise Resource Planning) ERP, product lifecycle management (Product Lifecycle Management) PLM, and computer aided process design (Computer Aided Process Planning) CAPP functional management modules.
The workshop MES core function module and the workshop management module work cooperatively and are mainly used for executing and managing the production process of manufacturing enterprises, and specifically comprise the progress management, task management, equipment management, process management, tooling management and personnel management of workshops.
The manufacturing process data is refined into four layers of data of equipment, process, index and user, wherein the equipment layer can comprise real-time acquired equipment state time sequence data, such as millisecond-level equipment vibration, power and the like; the process layer can comprise collected related materials, personnel operation and sensing control data; the index layer can comprise on-line quality monitoring data, production efficiency, progress and the like; the user layer includes user feedback data for the product.
The manufacturing process data has the characteristics of large time space span, multiple correlations and strong mechanism, the uncertainty of the manufacturing process data is caused by the complexity and the diversity of discrete manufactured products, the specification of the discrete manufactured process data is small, and a better data model needs to be established so as to improve the utilization reliability of the process big data.
As shown in fig. 2, the intelligent analysis module specifically realizes communication with various types of equipment through an RS232 interface, acquires state data in the process of equipment processing and production in real time, acquires data of an intelligent ammeter, related industrial sensors and equipment tools through an RS485 or ModBus protocol, and acquires product circulation information, personnel information, process quality real-time input information and the like in real time through intelligent labels such as RFID.
On the other hand, the intelligent analysis module can uniformly describe and package the data characteristics through the manufacturing process data acquisition model, integrate the data characteristics into a process manufacturing process data set, and then identify, mark and quantify discrete characteristic values by using a clustering-based method, so that the reliability analysis of the manufacturing system is supported through data.
Specifically, the manufacturing process data acquisition model is:
M＝(I，V，T，D，L，F)
m is a manufacturing process data acquisition model, I is an entity data attribute and comprises a data number; v is a data type; t is the occurrence time; d is a data description; l is the occurrence position; f is the functional classification. The model describes the internal data composition of the data object from a space-time and a functional multi-angle.
For example, in the process of machining a part, the data generated after the intelligent analysis module performs data collection is recorded as m= (12, double, t1, 2000.0, M1, s), and the data represents that at time t1, the spindle rotation speed s=2000 revolutions per minute of the grinding machine ml.
In the process of executing the process manufacturing process data integration, the intelligent analysis module writes the data into corresponding process manufacturing process and equipment history information data sets through reduction operation according to entity data attributes in the data records, and finally integrates the data into the process manufacturing process data sets and the equipment production process data sets, so that the data can be conveniently and rapidly transformed and called.
Through the screening, packaging and integration with the original data, a process data set which can be used for analysis is formed, the intelligent analysis module can search, analyze and evaluate the data set by configuring matching conditions of similar procedures, and early warning is carried out on procedures which are not good in matching, so that a manager can grasp the current production capacity level of the manufacturing system dynamically.
According to the invention, an open collaborative manufacturing cloud architecture system facing to the bottom data based on an intelligent workshop is constructed, and particularly, an intelligent analysis module is arranged for collecting, preprocessing, modeling and packaging and abnormality early warning of big data of a discrete manufacturing workshop, so that the quality of collecting the big data in the production process of the discrete manufacturing workshop is improved, and the integration, uploading, management and analysis of the data are facilitated.
Although a few exemplary embodiments of the present invention have been shown and described, it would be appreciated by those skilled in the art that changes may be made in these exemplary embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the claims and their equivalents.
Claims (4)
1. An open collaborative manufacturing cloud architecture system facing to bottom data is characterized in that the system is applied to an intelligent workshop and comprises an intelligent analysis module, a data service module, a workshop management module and a workshop core function module,
the intelligent analysis module is used for collecting, processing and integrating workshop manufacturing process data generated by the automatic equipment, the Internet of things equipment and the intelligent sensing equipment of the manufacturing resource layer, and uploading the processed manufacturing process data and the original data to the data service module;
the data service module is used for storing, processing and applying the manufacturing process data uploaded by the intelligent analysis module;
the workshop management module integrates enterprise resource planning Enterprise resource planning ERP, product lifecycle management, product Lifecycle management PLM and a management module of computer aided process planning CAPP function;
the workshop MES core function module and the workshop management module work cooperatively and are used for executing and managing the production process of a manufacturing enterprise, and specifically comprises the steps of workshop progress management, task management, equipment management, process management, tooling management and personnel management;
the intelligent analysis module can integrate the acquired manufacturing process data into a procedure manufacturing process data set according to the respective characteristic description and the manufacturing process data acquisition model, and store, analyze and apply the procedure manufacturing process data set;
the data service module stores the uploaded source data by adopting a distributed file system hadoopdi stributedfilesystemHDFS, then realizes data processing based on a core computing framework MapReduce, and finally further organizes the processed data by an open source database hadoopdatabaseHBase, and provides an access interface and a data basis for a workshop management module and a workshop MES core functional layer;
the acquired manufacturing process data comprises four layers of data of equipment, a process, indexes and a user, wherein the equipment layer comprises real-time acquired equipment state time sequence data, the process layer comprises acquired related material, personnel operation and sensing control data, the index layer comprises on-line quality monitoring data, production efficiency and progress, and the user layer comprises feedback data of the user on a product;
the intelligent analysis module is particularly communicated with various types of equipment through an RS232 interface, acquires state data in the processing and production process of the equipment in real time, acquires data of an intelligent ammeter, related industrial sensors and equipment tools through an RS485 or ModBus protocol, and acquires real-time flow information, personnel information and process quality real-time input information of the products through an RFID intelligent tag;
the manufacturing process data acquisition model for processing the acquired manufacturing process data by the intelligent analysis module is as follows:
M＝(I，V，T，D，L，F)
m is a manufacturing process data acquisition model, I is an entity data attribute and comprises a data number; v is a data type; t is the occurrence time; d is a data description; l is the occurrence position; f is functional classification; the model describes the internal data composition of the data object from a space-time and a functional multi-angle.
2. An implementation method applied to the open collaborative manufacturing cloud architecture system facing the underlying data as set forth in claim 1, the method comprising
(1) Collecting manufacturing process data;
(2) And integrating the collected manufacturing process data into a procedure manufacturing process data set according to the respective characteristic description and a manufacturing process data collection model, and storing and analyzing the procedure manufacturing process data set, wherein the manufacturing process data collection model is as follows:
M＝(I，V，T，D，L，F)
m is a manufacturing process data acquisition model, I is an entity data attribute and comprises a data number; v is a data type; t is the occurrence time; d is a data description; l is the occurrence position; f is functional classification; the model describes the internal data composition of the data object from a space-time and a functional multi-angle.
3. The method of claim 2, wherein the collected manufacturing process data comprises four levels of equipment, process, index, and user data, wherein the equipment layer comprises real-time collected equipment status time sequence data, the process layer comprises collected related materials, personnel operation, and sensing control data, the index layer comprises on-line quality monitoring data, production efficiency, and progress, and the user layer comprises user feedback data for the product.
4. The method of claim 3, further comprising the steps of realizing communication with various types of equipment through an RS232 interface, collecting state data in the processing and production process of the equipment in real time, simultaneously obtaining data of the intelligent ammeter, related industrial sensors and equipment tools through an RS485 or ModBus protocol, and collecting flow information, personnel information and process quality real-time input information of the products in real time through intelligent labels such as RFID.
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