CN110989537A - Production data processing method, apparatus, medium, and system - Google Patents
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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]
- G05B19/41885—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 modeling, simulation of the manufacturing system
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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
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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Abstract
The invention discloses a production data processing method, relates to the technical field of industrial automation, and is used for solving the problem that the existing method for integrating equipment data on a production line is lacked, and the method comprises the following steps: obtaining production data according to a product batch, and adding a timestamp to the production data to obtain production data to be analyzed, wherein the production data comprises quality parameters and production equipment parameters; forwarding the production data to be analyzed to Logstash through Kafka for data standardization processing; and storing the standardized production data to be analyzed by a time sequence database, and numbering the standardized production data to be analyzed. The invention also discloses an electronic device, a computer storage medium and a system. The invention integrates the quality parameters and the equipment parameters, thereby providing a basis for the quality tracing of the product.
Description
Technical Field
The invention relates to the technical field of industrial automation, in particular to a production data processing method, equipment, medium and system.
Background
Under the strategic guidance of the guidelines of "china manufacturing 2025", deep fusion of big data technology and traditional manufacturing enterprises is becoming increasingly important.
In a conventional manufacturing enterprise, a production line is the most important part of the enterprise. The production line is also called as an assembly line, and refers to an industrial production mode, that is, each production unit only focuses on the work of processing a certain segment, so as to improve the work efficiency and the yield. In order to complete the intelligent upgrade of the production line, the production business datamation must be completed first to construct a production big data analysis system.
Present intelligent production line all can gather the equipment data on the production line to in the control of carrying out equipment, but because each equipment data type that current data gathered is different, and the data of gathering lack the time dimension, lead to need expend great resource and handle data when the quality is traceed back.
Disclosure of Invention
In order to overcome the defects of the prior art, one of the objectives of the present invention is to provide a production data processing method, which facilitates the quality tracing of the product by performing data standardization processing and numbering on the production data.
One of the purposes of the invention is realized by adopting the following technical scheme:
a production data processing method comprising the steps of:
obtaining production data according to a product batch, and adding a timestamp to the production data to obtain production data to be analyzed, wherein the production data comprises quality parameters and production equipment parameters;
forwarding the production data to be analyzed to Logstash through Kafka for data standardization processing;
and storing the standardized production data to be analyzed by a time sequence database, and numbering the standardized production data to be analyzed.
Further, the quality parameter is a quality parameter of a current batch of products identified by the sorting machine, and the production equipment parameter is a PLC parameter of the production line equipment.
Further, the timestamp is added by calling the data acquisition time of the MES system.
Further, the method comprises the following steps of obtaining production data according to the product batch, adding a timestamp to the production data to obtain the production data to be analyzed:
the production data to be analyzed is sent to Kafka by Nginx.
Further, the production data is divided into semi-finished product production data and finished product production data.
Further, numbering the production data to be analyzed after the standardization treatment, comprising the following steps:
respectively numbering the semi-finished product production data and the finished product production data to be analyzed;
adding a batch number to the serial numbers of the semi-finished product production data and the finished product production data to be analyzed according to the production batch;
and constructing a numbering dictionary of the production batch according to the batch number.
Further, the time-series database is InfluxDB.
It is a further object of the present invention to provide an electronic device for performing one of the above objects, comprising a processor, a storage medium, and a computer program, the computer program being stored in the storage medium, the computer program, when executed by the processor, implementing the above-mentioned production data processing method.
It is a further object of the present invention to provide a computer-readable storage medium storing one of the objects of the invention, on which a computer program is stored, which computer program, when being executed by a processor, realizes the above-mentioned production data processing method.
It is a fourth object of the present invention to provide a production data processing system, comprising: the system comprises a relay server, a data processing end and a remote server;
the relay server is used for acquiring production data and forwarding the production data to the data processing terminal through Nginx, wherein the production data comprises quality parameters and production equipment parameters; the data processing end receives the production data through Kafka, performs data standardization through Logstash, stores the production data after data standardization and uploads the production data to the remote server; and the remote server is used for numbering the received production data and constructing a numbering dictionary according to the production batch.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides a processing method of production data, which is characterized in that a data transmission channel is constructed for the production data through Kafka, the data is standardized through Logstash, a time sequence database can ensure that the stored production data is related to the acquisition time of the production data, and the production data is numbered to facilitate the data tracing. The invention integrates the production data on the production line, integrates the quality parameters and the equipment data on the production line according to batches, and provides complete analysis data for quality tracing and big data analysis of products.
Drawings
FIG. 1 is a flowchart of a production data processing method according to the first embodiment;
FIG. 2 is a flow chart of a method for numbering production data according to the first embodiment;
fig. 3 is a block diagram showing the configuration of an electronic apparatus according to the second embodiment;
FIG. 4 is a block diagram showing a configuration of a production data processing system according to a fourth embodiment.
Detailed Description
The present invention will now be described in more detail with reference to the accompanying drawings, in which the description of the invention is given by way of illustration and not of limitation. The various embodiments may be combined with each other to form other embodiments not shown in the following description.
Example one
The embodiment one provides a production data processing method, which aims to integrate production data so as to provide a basis for subsequent big data analysis.
Referring to fig. 1, a method for processing production data includes the following steps:
s110, obtaining production data according to a product batch, and adding a timestamp to the production data to obtain production data to be analyzed, wherein the production data comprises quality parameters and production equipment parameters;
the quality parameters are the quality parameters of the products in the current batch identified by the sorting machine, and the production equipment parameters are the PLC parameters of the production line equipment.
The existing intelligent production line can automatically detect the quality of products, and generally can detect the quality through a sorting machine, so that the quality parameters in the embodiment are the quality parameters of the products in the current batch identified by the sorting machine, and the production equipment parameters are the PLC parameters of the production line equipment. By matching the production equipment parameters with the quality parameters, support is provided for subsequent big data analysis.
The PLC is a programmable logic controller, equipment is generally provided with a PLC control module on the current automatic production line, and a PLC control system is a novel industrial control device of a generation formed by introducing a microelectronic technology, a computer technology, an automatic control technology and a communication technology on the basis of a traditional sequence controller, and aims to replace sequential control functions of relays, execution logic, timing, counting and the like and establish a flexible remote control system. The method has the characteristics of strong universality, convenience in use, wide application range, high reliability, strong anti-interference capability, simple programming and the like. Taking a silicon wafer production line as an example, through the PLC control module installed on the silicon wafer sorting machine, not only the quality parameter (for example, the conductivity of the silicon wafer automatically derived by the sorting machine through the appearance) automatically detected by the sorting machine can be obtained, but also PLC data of other devices on the production line, for example, data of pressure, temperature, voltage, ingot casting ingredient proportion, and the like of the ingot casting furnace, or data of the rotation speed, cutting angle, cutting speed, and the like of the cutting machine can be obtained, and certainly, the collection of the production data can also be performed through the PLC control module on each device.
The timestamp in S110 is added by calling the data collection time of the MES system.
The MES system is a manufacturing execution system, is a system for standardized management of workshop production and manufacturing, can optimize and manage the production and manufacturing processes and processes in enterprises, and can store data in a time dimension. Therefore, the MES system stores the products of each batch according to the completion time, and the synchronization of the time can be completed by calling the data acquisition time of the MES system.
It should be noted that some existing MES systems can directly collect quality parameters and production equipment parameters, and production data in this embodiment can be obtained only by directly calling data in the MES systems, and the production data does not need to be acquired by a PLC control module on the equipment.
In S110, obtaining production data according to the product batch, and adding a timestamp to the production data to obtain production data to be analyzed, further including the following steps:
the production data to be analyzed is sent to Kafka by Nginx.
Nginx is a high-performance HTTP and reverse proxy web server, and simultaneously provides IMAP/POP3/SMTP service, and has a load balancing function, so that the problem of server overload caused by excessive data volume can be prevented by forwarding production data through Nginx.
The Kafka described above is used to construct and provide a stable and high-performance data channel for each pipeline device. Kafka is a distributed, partitioned, multi-replica, multi-subscriber, zookeeper-based coordinated distributed logging system (which may also be referred to as a message queuing system), which may be commonly used for web/Nginx logs, access logs, message services, and the like. Kafka is mainly applied to log collection systems and message systems. Kafka has high throughput, reliability, and replication characteristics, and therefore Kafka is used as a medium for data transmission in the present embodiment.
It should be noted that sending data to Kafka through nginn can be implemented by installing a nginn-Kafka plug-in nginn, and the plug-in can implement writing data received by nginn directly into Kafka, so that the transmission time of transmitting data to Kafka by nginn is reduced, and data is not easy to lose.
S120, forwarding the production data to be analyzed to Logstash through Kafka for data standardization processing;
since there are a plurality of formats of PLC parameters, it is necessary to unify the data formats of these parameters, that is, to perform data standardization processing in S120. In the embodiment, the data is standardized by the Logstash. The Logstash is an open source data collection engine and has a real-time pipeline function. Logstash can dynamically unify data from different data sources and normalize the data to a selected destination, i.e., a timing database in this embodiment. In the process of transmitting data from a source to a repository, the Logstash filter can analyze each event, identify named fields to construct a structure, and convert the named fields into a universal format; logstash is capable of dynamically converting and parsing data and is not affected by format or complexity.
And S130, storing the standardized production data through a time sequence database, and numbering the standardized production data.
The time sequence database is called as a time sequence database. The time series database is mainly used for storing data with time tags (i.e., time stamps in the present embodiment), and the data with time tags is also called time series data. The timing database in this embodiment is infiluxdb. The InfluxDB is a time sequence database and is used for processing mass writing and load query. The InfluxDB is intended to be used as a back-end storage for any use case involving large amounts of time-stamped data, including DevOps monitoring, application metrics, Internet of things sensor data and real-time analytics. The InfluxDB has the advantages of no special dependence (depending on environments, such as Java), self-contained data expiration function, native HTTP support (built-in HTTP API), strong SQL-like syntax, and support of a series of functions, such as min, max, sum, count, mean, and the like, and does not need to be provided with additional plug-ins, so the InfluxDB is used as a time sequence storage database in the embodiment.
Referring to fig. 2, S130 specifically includes the following steps:
s1301, numbering the semi-finished product production data and the finished product production data to be analyzed respectively;
generally, on a production line, finished products in products of the same batch are processed from semi-finished products, and the quality parameters of the finished products and the semi-finished products are different (generally, semi-finished products or finished product production equipment are provided with a sorting machine, and the sorting machine can automatically identify the quality parameters of finished products or semi-finished products), taking a silicon wafer production line as an example, a silicon ingot produced by an ingot casting machine is a semi-finished product, a silicon wafer produced after cutting by a cutting machine is a finished product, and generally, on the production line, the semi-finished products or finished products produced by each equipment are sorted by the sorting machine, so that the semi-finished products or finished products can be distinguished by different sorting machines under normal conditions, and different quality parameters are obtained; numbering the finished products and semi-finished products separately may facilitate distinguishing between different semi-finished products/finished products.
S1302, adding a batch number to the number of the semi-finished product production data and the number of the finished product production data to be analyzed according to the production batch;
in a production line, each batch of products has a batch number, and in this embodiment, the batch number is added to the serial number of the production data, so that the products of each batch can be distinguished during quality tracing.
And S1303, constructing a numbering dictionary of the production batch according to the batch number.
The numbering dictionary in S1303 stores the production data of each batch according to the batch, specifically: and storing the finished product numbers and the semi-finished product numbers of the same batch according to the time stamps to obtain a number dictionary of each batch, wherein the finished product numbers and the semi-finished product numbers are silicon wafer numbers, crystal bar numbers, silicon ingot numbers and the like by taking data generated by a silicon wafer production line as an example. Therefore, during subsequent inquiry, the production data of a certain batch of finished products and semi-finished products can be inquired through the batch number and the serial number.
It should be noted that, because the sorting machine performs quality detection on each semi-finished product or finished product, the above numbering is performed on each semi-finished product or finished product so as to distinguish a plurality of products produced at the same time.
Example two
Fig. 3 is a schematic structural diagram of an electronic device according to a second embodiment of the present invention, as shown in fig. 3, the electronic device includes a processor 210, a memory 220, an input device 230, and an output device 240; the number of processors 210 in the computer device may be one or more, and one processor 210 is taken as an example in fig. 3; the processor 210, the memory 220, the input device 230, and the output device 240 in the electronic apparatus may be connected by a bus or other means, and the bus connection is taken as an example in fig. 3.
The memory 220, which is a computer-readable storage medium, may be used to store software programs, computer-executable programs, and modules. The processor 210 executes various functional applications and data processing of the electronic device by executing the software programs, instructions and modules stored in the memory 220, that is, implements the production data processing method of the first embodiment.
The memory 220 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 220 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 220 may further include memory located remotely from the processor 210, which may be connected to the electronic device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 230 may be used to receive input of user identification information, quality parameters, production equipment parameters, and the like. The output device 240 may include a display device such as a display screen.
EXAMPLE III
The third embodiment of the present invention further provides a storage medium containing computer-executable instructions, where the storage medium is used for a computer to execute a method for processing production data, and the method includes:
obtaining production data according to a product batch, and adding a timestamp to the production data to obtain production data to be analyzed, wherein the production data comprises quality parameters and production equipment parameters;
forwarding the production data to be analyzed to Logstash through Kafka for data standardization processing;
and storing the standardized production data to be analyzed by a time sequence database, and numbering the standardized production data to be analyzed.
Example four
The fourth embodiment of the invention also provides a production data processing system.
Referring to fig. 4, the production data processing system includes: the system comprises a relay server, a data processing end and a remote server;
the relay server is used for acquiring production data and forwarding the production data to the data processing terminal through Nginx, wherein the production data comprises quality parameters and production equipment parameters; the data processing end receives the production data through Kafka, performs data standardization through Logstash, stores the production data after data standardization and uploads the production data to the remote server; and the remote server is used for numbering the received production data and constructing a numbering dictionary according to the production batch.
The relay server may be a server responsible for data collection, or may be a PLC control system on a production line.
The data processing end is usually a single server, and the server is provided with Kafka and Logstash plug-ins, and the work of data storage is completed through the server.
The remote server is usually a cloud analysis platform and is used for receiving the production data sent by the data processing end and numbering the data, and usually, subsequent big data analysis can be directly performed through the remote server. The remote server can also store the received data, so that the problem that the quality tracing cannot be carried out due to data loss of the data processing end can be prevented.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also execute the relevant operations in the production data processing method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling an electronic device (which may be a mobile phone, a personal computer, a server, or a network device) to execute various implementations of the present invention
Various other modifications and changes may be made by those skilled in the art based on the above-described technical solutions and concepts, and all such modifications and changes should fall within the scope of the claims of the present invention.
Claims (10)
1. A method of processing production data, comprising the steps of:
obtaining production data according to a product batch, and adding a timestamp to the production data to obtain production data to be analyzed, wherein the production data comprises quality parameters and production equipment parameters;
forwarding the production data to be analyzed to Logstash through Kafka for data standardization processing;
and storing the standardized production data to be analyzed by a time sequence database, and numbering the standardized production data to be analyzed.
2. The method of claim 1, wherein the quality parameter is a product quality parameter of a current lot identified by the sorter, and the production equipment parameter is a production line equipment PLC parameter.
3. The production data processing method of claim 1, wherein the time stamp is added by calling a data collection time of the MES system.
4. The method of claim 1, wherein the production data is obtained from a production lot and a timestamp is added to the production data to obtain production data to be analyzed, further comprising the steps of:
the production data to be analyzed is sent to Kafka by Nginx.
5. The production data processing method according to any one of claims 1 to 4, wherein the production data is divided into semi-finished product production data and finished product production data.
6. The method for processing production data according to claim 5, wherein numbering the production data to be analyzed after the standardization process, comprises the steps of:
respectively numbering the semi-finished product production data and the finished product production data to be analyzed;
adding a batch number to the serial numbers of the semi-finished product production data and the finished product production data to be analyzed according to the production batch;
and constructing a numbering dictionary of the production batch according to the batch number.
7. The production data processing method of claim 1, wherein the time-series database is infiluxdb.
8. An electronic device comprising a processor, a storage medium, and a computer program, the computer program being stored in the storage medium, wherein the computer program, when executed by the processor, implements the production data processing method of any one of claims 1 to 7.
9. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing the production data processing method according to any one of claims 1 to 7.
10. A production data processing system, comprising: the system comprises a relay server, a data processing end and a remote server;
the relay server is used for acquiring production data and forwarding the production data to the data processing terminal through Nginx, wherein the production data comprises quality parameters and production equipment parameters; the data processing end receives the production data through Kafka, performs data standardization through Logstash, stores the production data after data standardization and uploads the production data to the remote server; and the remote server is used for numbering the received production data and constructing a numbering dictionary according to the production batch.
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| CN109743199A (en) * | 2018-12-25 | 2019-05-10 | 中国联合网络通信集团有限公司 | Containerization management system based on micro services |
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