CN116719289A - Production management system and method based on process control - Google Patents
Production management system and method based on process control Download PDFInfo
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- 238000004886 process control Methods 0.000 title claims abstract description 57
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- 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], 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], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- 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
- G05B2219/32—Operator till task planning
- G05B2219/32339—Object oriented modeling, design, analysis, implementation, simulation language
Abstract
The invention discloses a production management system and a production management method based on process control, wherein the system comprises a data fusion module, a database, a process control and data analysis module and a visual UI module, wherein the process control and data analysis module and the data fusion module are interacted with the database; the data fusion module is used for receiving the original data with the time stamp collected in the production environment, detecting the state or the linear value of the original data, alarming for abnormality and persistence of the data; the process control and data analysis module is used for obtaining persistent original data, calculating control limits with time sequence attributes and checking the calculated control limits by using configured rules so as to reflect the production state of the production environment; the visual UI module is used for displaying data information and achieving interaction. The invention can realize tracing, defect regression and predictive maintenance on the production and manufacturing links, and improves the intellectualization of the manufacturing field and optimizes the production efficiency.
Description
Technical Field
The invention belongs to the field of intelligent manufacturing, and particularly relates to a production management system and method based on process control.
Background
Systems currently existing in the industry: the data acquisition and monitoring control system is a control system that collects, saves digital data generated in a remote device to a persistence tool through a certain transmission protocol, and monitors and analyzes the remote device. The system becomes an indispensable tool in the modern production process, is widely applied to the traditional industries of power, petrifaction and the like, and plays an important role in manufacturing display panels and integrated circuits. The data acquisition and monitoring system can send out an alarm according to the acquired remote equipment state, and can judge and analyze the equipment state according to the set threshold value so as to give an alarm.
Traditional data acquisition and monitoring systems focus on acquiring and storing data, but production facilities are always signalling when they will fail and how to run more smoothly for a longer time, because traditional data acquisition is weaker than analysis of data and control of production, which is not possible, and has the following drawbacks:
1. secondary processing of the data is required to adapt to the third party tool requirement format.
2. The data is typically stored in a persistence layer and a third party tool of the data acquisition and monitoring system, respectively, resulting in redundancy.
3. The third party tool has poor analysis and processing instantaneity and weak analysis capability.
4. Multiple different systems are required to be deployed, resources are occupied, and meanwhile, information interaction between a persistence layer of a data acquisition and monitoring system and a third party tool is complicated, so that later maintenance and problem positioning are inconvenient.
Disclosure of Invention
The invention aims to provide a production management system and method based on process control, which are used for tracing, defect regression and predictive maintenance on production and manufacturing links based on collected process control data, so that the intellectualization of the manufacturing field is improved and the production efficiency is optimized.
The invention is realized mainly by the following technical scheme:
the production management system based on the process control comprises a data fusion module, a database, a process control and data analysis module and a visual UI module, wherein the data fusion module is respectively connected with the database and the visual UI module, the process control and data analysis module and the data fusion module are interacted with the database, and the database is used for storing the value of the original data change in a time linear relation; the data fusion module is used for receiving the original data with the time stamp collected in the production environment, detecting the state or the linear value of the original data, alarming for abnormality and persistence of the data; the process control and data analysis module is used for obtaining persistent original data, calculating control limits with time sequence attributes and checking the calculated control limits by using configured rules so as to reflect the production state of the production environment; the visual UI module is used for displaying data information and achieving interaction.
In order to better realize the invention, the data fusion module further comprises a data receiving unit, a data classifying unit, a data cutting unit, a state detecting unit, a threshold detecting unit, an alarm unit, a data change detecting unit and a data persistence unit; the data classification unit is used for classifying the data into associated corresponding production manufacturing type data and production equipment type data; the data cutting unit is used for realizing data segmentation, namely cutting production and manufacturing type data into beat data according to production street beats on a production line, and cutting production equipment type data according to modeling data of the whole factory; the state detection unit is used for carrying out state detection on the equipment state data, the threshold detection unit is used for carrying out threshold detection on the product data, and the data change detection unit is used for carrying out comparison on the equipment state data and the linear time backtracking previous data to determine whether the comparison is changed or not; the data persistence unit is used for differentially storing the equipment state data and fully storing the product data. Wherein, the manufacturing type data are produced: product data, production equipment type data: device status data.
In order to better realize the invention, the data fusion module further comprises a data cleaning unit, wherein the data cleaning unit is used for effectively processing the data obtained by the data receiving unit.
In order to better implement the invention, further, the calling mode of the process control and data analysis module is any one of triggering type, timing type and sampling type.
In order to better realize the invention, the system further comprises a right control module which is used for providing an authentication function and realizing the right verification at the nodes sending the alarm, the visual UI login, the visual display and the visual interaction.
The invention is realized mainly by the following technical scheme:
a production management method based on process control is carried out by adopting the system and comprises the following steps:
step S100: receiving collected production environment original data, wherein each type of collected data is provided with a time stamp, classifying the original data and obtaining production manufacturing type data and production equipment type data;
step S200: data cutting: dividing the production and manufacturing type data into beat data according to production street beats on a production line, and dividing the production equipment type data according to modeling data of the whole factory; differential storage is carried out on equipment state data, and full-quantity storage is carried out on product data;
step S300: configuring a production model through a visual UI page, and setting analysis rules; and (3) for analysis configuration, extracting the data stored in the step S200, calculating the control limit of the current data, wherein the control limit is time-series, checking the calculated control limit by using the configured rule, and if the control limit is abnormal, sending an alarm and lasting abnormal data.
In order to better implement the present invention, further, in step S200, for the cut data, status detection is performed on the device status data, threshold detection is performed on the product data, if abnormality is detected, abnormal information is recorded and an alarm is sent after permission verification; if no abnormality is detected, judging whether the equipment state data is changed compared with the previous data of the linear time backtracking, and if so, storing the changed data.
In order to better implement the present invention, further, in the step S300, the extracted original data is subjected to removal of an extreme error value and re-quantization; a continuously collected set of data is divided into four halves, wherein the smallest 25% of the halves are the first half, the largest 25% are the third half, and the middle 50% are the second half and the fourth half, and if the subsequently received data is beyond the first half or the third half, the data can be considered as an artificial extreme error value.
In order to better implement the present invention, further, in the step S300, for the production manufacturing type data, the control limits include an upper control limit and a lower control limit:
upper control limit = process parameter mean +3 standard deviation
Lower control limit = process parameter mean-3 standard deviation
For production equipment type data, the control limits include an upper control limit and a lower control limit:
upper control limit = average value of product parameters detected by sensor +3 standard deviation
Lower control limit = average-3 x standard deviation of product parameters detected by the sensor
Where x represents each data point in the sample,represents the average value of the samples, and n represents the size of the samples.
The beneficial effects of the invention are as follows:
the invention can be used for processing the data generated by each flow, and predictive maintenance is realized by analyzing the data and actively controlling the process, so that the intellectualization of the manufacturing field is improved and the production efficiency is optimized. The invention can analyze and process the data based on the collected manufacturing field, thereby tracing the production and manufacturing links and carrying out predictive maintenance on the defect regression. The system can analyze different production processes independently or simultaneously, and locate the low-efficiency process in the production link. The system can perform positive feedback control on the production process, monitor the abnormality in the production process in real time and send out early warning through various communication modes. The system can forecast part of defects in the production process in advance by analyzing the control parameters in the production process.
The invention provides basis for production process control through the data fusion module, the process control and the data analysis module, quantitatively analyzes the production process, predicts abnormality, conveniently traces and locates defects, achieves predictive maintenance and changes the situation of weaker data analysis in the production process. The invention can be based on process control, so that each link of production and manufacture can be traced, the defects of the product are easy to locate, and the production can be predictively maintained. The invention can perform positive feedback control based on the analysis of the production process result. The method predicts part of defects in the production process by classifying, cutting and detecting the state of the production field class data and combining with the control parameter analysis in the production process. The invention can monitor the equipment of the production system or the abnormality of the production line and send out early warning through various communication modes. The invention predicts partial anomalies in the manufacturing field by calculating control limits and matching rules of response. And performing process control on the production process by calculating a process capability index. In the field of production and manufacturing, production process data are stored in real time, and synchronous and asynchronous analysis can be performed. The invention can be configured with various early warning modes (e-mail, third party tools for providing API, etc.). Applying process control analysis provides hot plug configuration. The process control analysis method and the service support provide reports and are visually displayed. The process control analysis service and the data fusion use the same persistent address, so that the data is prevented from being transferred.
Drawings
FIG. 1 is a schematic diagram of a management system of the present invention;
FIG. 2 is a flow chart of data fusion;
FIG. 3 is a flow chart of process control and data analysis.
Detailed Description
Example 1:
a production management system based on process control, as shown in figure 1, comprises a data fusion module, a process control and data analysis module and a visual UI module.
Preferably, as shown in fig. 2, the data fusion module includes functions of data receiving, data cleaning, state detection, threshold detection, data persistence, and the like. Raw data of acquisition equipment in a production environment is received in a data fusion module.
Detecting and alarming the original data state or the linear value, and simultaneously, persisting the changed data.
Preferably, values of the original data change are stored in a database in a time linear relation, the data fusion process and the data analysis process are interacted with the same database, the data transfer consumption is reduced, and the processing efficiency is improved.
Preferably, as shown in fig. 3, the process control and data analysis module is configured to obtain the persistent original data in the data fusion module, and calculate the control limit of the current data after the reconstruction; checking the production state of the calculated control limit reaction production environment by using the configured rule, and judging that the production state is abnormal after exceeding the control; responsive alarms are issued synchronously according to the configured alarm dispatch type.
Preferably, the visualization UI provides the following functions:
1: and displaying the persistent original data of the data fusion module, and displaying the state of the production equipment in the form of a chart and the like according to the state.
2: and displaying alarm information and data beyond control.
3: rules and parameters thereof are configured.
4: normal and abnormal data trace source.
Preferably, the system further comprises a rights control module for providing authentication functions to the acquisition and monitoring service and the process control analysis service in separate APIs. And providing permission verification at nodes such as an acquisition and monitoring service sending alarm, a process control analysis service sending alarm, a visual UI login, a visual UI abnormal data viewing and the like.
The invention provides basis for production process control through the data fusion module, the process control and the data analysis module, quantitatively analyzes the production process, predicts abnormality, conveniently traces and locates defects, achieves predictive maintenance and changes the situation of weaker data analysis in the production process. The invention can be based on process control, so that each link of production and manufacture can be traced, the defects of the product are easy to locate, and the production can be predictively maintained. The invention can perform positive feedback control based on the analysis of the production process result. The method predicts part of defects in the production process by classifying, cutting and detecting the state of the production field class data and combining with the control parameter analysis in the production process. The invention can monitor the equipment of the production system or the abnormality of the production line and send out early warning through various communication modes. The invention predicts partial anomalies in the manufacturing field by calculating control limits and matching rules of response. And performing process control on the production process by calculating a process capability index. In the field of production and manufacturing, production process data are stored in real time, and synchronous and asynchronous analysis can be performed. The invention can be configured with various early warning modes (e-mail, third party tools for providing API, etc.). Applying process control analysis provides hot plug configuration. The process control analysis method and the service support provide reports and are visually displayed. The process control analysis service and the data fusion use the same persistent address, so that the data is prevented from being transferred.
Example 2:
a production management method based on process control is carried out by adopting the system and comprises the following steps:
step 1: as shown in fig. 2, the data fusion module receives the production environment raw data collected by the collection device. And middleware such as RabbitMQ or Kafka is adopted, a sender is used as a producer, and the acquisition and monitoring service is used as a receiver mode to realize data transmission.
Optionally, other message middleware and other message reception modes are supported.
Aiming at the production and manufacturing field:
the data fusion module classifies the data:
1, producing manufacturing type data, wherein the data mainly comprise data related to systems such as MES and the like;
and 2, producing equipment type data, wherein the data mainly comprise data generated by equipment in a production line and various equipment state data.
Step 2: different types of data will be cut differently
1, the production manufacturing type data are divided into beat data according to production street beats on a production line.
2, the production equipment type data is cut according to the modeling data of the whole factory (the modeling data of the P.S factory refers to the hierarchy of the factory to be modeled, including groups, factories areas, production lines, etc.), and the data of each model is separated independently.
Step 3: the data generated by the production equipment comprises equipment self state data and product data, the equipment state data is used for carrying out state detection, and the equipment numerical value is used for carrying out threshold detection; if the abnormality is detected, recording the abnormality information and sending an alarm after the authority verification.
Step 4: and comparing the currently received data with the previous data of the production equipment state data and the linear time backtracking, and if so, storing the changed data. The change rate of the equipment state in the production environment is generally low, and the data redundancy is reduced in a differential storage mode; and the product production data are stored in full quantity and are analyzed and used later.
Step 5: as shown in fig. 3, the process control and data analysis module reads the analysis type configuration.
Optionally: and supporting triggering type calling of the process control analysis service, namely triggering the process analysis service when the data fusion module receives data and executing in a serial mode.
Optionally: and supporting the timed invocation of the process control analysis service, namely, the data fusion module does not trigger the process analysis service immediately after receiving the data. And setting a timing task, and automatically triggering a process control analysis service after the time interval is met.
Optionally: and supporting sampling to call the process control analysis service, namely, the data fusion module does not trigger the process analysis service immediately after receiving the data. The data fusion module receives a certain amount of data and then samples a piece of data to trigger a process control analysis service.
Step 6: the production model in the production and manufacturing field is configured through a visual UI page, and rules are set, such as etching depth or film thickness value must be larger than 0 and smaller than thickness value specified by the process, normal production takt range, fluid level and temperature normal range, sound wave range, axial vibration normal range and the like.
Step 7: due to erroneous extreme values, which may be caused by sporadic anomalies of personnel or equipment in the manufacturing field, the normal values set according to step 6 are removed and re-quantized. Such data can be judged to be abnormal and give an alarm in the acquisition and monitoring service, the data can bring serious distortion to the calculation of control data, and the error omission can not be caused after the data is removed. The specific identification method comprises the following steps: a continuously collected set of data is divided into four halves, wherein the smallest 25% of the data is the first half, the largest 25% of the data is the third half, and the middle 50% of the data is the second and fourth halves, and if the subsequent data is beyond the first half or the third half, the data can be considered as a human error extreme value.
Step 8: and (3) independently calculating the control limit after the production manufacturing type data and the production equipment type data are reorganized. The control limit is a predetermined range for determining whether or not the process is within the control range when performing the process control. The control limits are typically determined by calculating a statistical indicator (e.g., mean or standard deviation) for a given sample.
The production type data are mainly data reported by the MES, parameter data of products in different batches and under different production lines are continuously counted when the control limit is calculated, for example, parameter data of different processes on the production line reported by the MES (the process parameter data of the production line can be dynamic, fine adjustment of the process can be carried out in consideration of the process in the production process, particularly adjustment of the process parameters after the factory finds out the defects of the products is necessary for continuously calculating the different process parameter data of the production line reported by the MES, corresponding control limit can be further calculated, and a certain process can be predicted to be problematic in time), and the production type data are calculated:
upper control limit = such as: mean +3 standard deviation of process parameters reported by MES
Lower control limit = such as: mean value-3 standard deviation of process parameters reported by MES
Wherein, the average value and the standard deviation are statistical indexes of the sample, and the calculation formula of the standard deviation is as follows:
where x represents each data point in the sample,x represents the average value of the samples, and n represents the size of the samples.
The standard deviation is calculated by the following steps: the average of the samples is first calculated, then the difference between each data point and the average is calculated, and the difference is squared. Next, the sum of squares of each difference is summed and divided by the sample size. Finally, the standard deviation of the sample is obtained by summing the obtained root numbers.
3 is an empirical constant, which is determined according to the characteristics of data distribution, such as data conforming to normal distribution, deviation distribution and the like, and the data conforming to normal distribution in the manufacturing field can satisfy more than 99.7% of data within a control limit range by using 3.
The production equipment type data mainly include data of various sensors in the production equipment, such as product thickness reported by laser sensors in a production line, and calculate:
upper control limit = such as: product thickness average +3 standard deviation reported by laser sensor
Lower control limit = such as: product thickness average value reported by laser sensor-3 standard deviation
Wherein, the average value and the standard deviation are statistical indexes of the sample, and the calculation formula of the standard deviation is as follows:
where x represents each data point in the sample,represents the average value of the samples, and n represents the size of the samples.
The calculation method of the standard deviation is the same as that described above, and therefore will not be repeated.
The need to prepare for tracing and locating defects is considered, and meanwhile the production equipment type data and the production type data need to be corresponding. The specific corresponding method comprises the following steps: each type of data is collected with a time stamp, and the calculated control limit of each type also indicates which time to calculate, i.e. the calculated control limit is time-sequential.
In general, the MES system has digitally modeled factories, each factory, factory area, production line, and various sensors under the production line have built related virtual models and have made one-to-one digital mapping, i.e. a model relation table is built, so that it is known which factory area a certain sensor belongs to, which production line the certain sensor belongs to, and also it is known which production equipment and sensors are participating in a certain process on the production line in a certain time period.
The two calculated control limit data are matched one by one according to the model data in the pre-condition and the time sequence, so that the control limit of the production equipment can be known to be the control limit data of the production type of which factory, factory area, production line and batch are related at a certain time point, and otherwise, the control limit data of the production equipment related at a certain time point under the batch can be found out through the control limit data of the batch.
And (3) lasting the calculated control limit data, respectively checking the type control limit data of the production equipment and the type control limit data of the production and manufacture, and checking whether the continuously collected data exceeds or is lower than the upper control limit or the lower control limit.
Step 9: if the control limit is checked according to the rule in the step 8 to exceed the control limit, the possible equipment and a certain production line of production are estimated automatically, the problem alarm information of a certain link of the process is sent in a configuration mode, based on the alarm, actions can be quickly taken, and required maintenance and response schemes (including manual and automatic maintenance measures and fine adjustment of the process parameter data of the product) can be formulated. The anomaly data (continuously collected production equipment type data and continuously collected production manufacturing type data) are persisted synchronously.
Further: the alarm mode and the alarm information format can be configured on the UI interface.
Step 10: if the control limit is not exceeded according to the rule check in step 8, the normal data (the continuously collected production equipment type data and the continuously collected production manufacturing type data) are persisted, and samples are provided for the recalculated control limit of the next time sequence.
The advantages of this embodiment are as follows:
1: the data fusion and the data analysis provide basis for the control of the production process, a detection method is provided for quantitatively analyzing the production process, predicting abnormality, tracing and positioning defects conveniently, predictive maintenance is achieved, and the situation that the data analysis is weaker in the production process is changed.
2: the invention improves the utilization rate of the existing production line, reduces the downtime and prolongs the service life of important assets.
3: the invention can help to make an effective maintenance plan, not rain silk, operation of the tent, but post-remediation of the fire-fighting type.
4: the data fusion module and the process control analysis service interact with the unified persistent address, so that the data transfer process is eliminated, and the space consumption is reduced.
5: various ways of receiving data are provided including, but not limited to, message queue middleware, third party APIs.
6: the process control analysis function provides a switching pattern that can be selectively enabled or disabled.
7: a variety of process control analysis function call modes are provided, including triggered, timed, and sampling.
8: the verification rule can be configured, the standard rule can be automatically generated, and other rules can be realized by configuring different parameters on the basis of the standard rule.
9: the alarm mode and format are configurable, and various alarm modes are provided, including mail and third party API.
The foregoing description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and any simple modification, equivalent variation, etc. of the above embodiment according to the technical matter of the present invention fall within the scope of the present invention.
Claims (9)
1. The production management system based on the process control is characterized by comprising a data fusion module, a database, a process control and data analysis module and a visual UI module, wherein the data fusion module is respectively connected with the database and the visual UI module, the process control and data analysis module and the data fusion module are interacted with the database, and the database is used for storing the value of the original data change in a time linear relation; the data fusion module is used for receiving the original data with the time stamp collected in the production environment, detecting the state or the linear value of the original data, alarming for abnormality and persistence of the data; the process control and data analysis module is used for obtaining persistent original data, calculating control limits with time sequence attributes and checking the calculated control limits by using configured rules so as to reflect the production state of the production environment; the visual UI module is used for displaying data information and achieving interaction.
2. The process control-based production management system of claim 1, wherein the data fusion module comprises a data receiving unit, a data sorting unit, a data cutting unit, a status detection unit, a threshold detection unit, an alarm unit, a data change detection unit, a data persistence unit; the data classification unit is used for classifying the data into associated corresponding production manufacturing type data and production equipment type data; the data cutting unit is used for realizing data segmentation, namely cutting production and manufacturing type data into beat data according to production street beats on a production line, and cutting production equipment type data according to modeling data of the whole factory; the state detection unit is used for carrying out state detection on the equipment state data, the threshold detection unit is used for carrying out threshold detection on the product data, and the data change detection unit is used for carrying out comparison on the equipment state data and the linear time backtracking previous data to determine whether the comparison is changed or not; the data persistence unit is used for differentially storing the equipment state data and fully storing the product data.
3. The process control based production management system of claim 2, wherein the data fusion module further comprises a data cleansing unit for efficiently processing the data obtained by the data receiving unit.
4. The process control based production management system of claim 1, wherein the call mode of the process control and data analysis module is any one of triggered, timed, and sampling.
5. The process control based production management system of claim 1, further comprising a rights control module for providing authentication functionality to enable rights verification at nodes sending alarms, visual UI login, visual presentation, and visual interaction.
6. A process control based production management method using the system of any one of claims 1-5, comprising the steps of:
step S100: receiving collected production environment original data, wherein each type of collected data is provided with a time stamp, classifying the original data and obtaining production manufacturing type data and production equipment type data;
step S200: data cutting: dividing the production and manufacturing type data into beat data according to production street beats on a production line, and dividing the production equipment type data according to modeling data of the whole factory; differential storage is carried out on equipment state data, and full-quantity storage is carried out on product data;
step S300: configuring a production model through a visual UI page, and setting analysis rules; and (3) for analysis configuration, extracting the data stored in the step S200, calculating the control limit of the current data, wherein the control limit is time-series, checking the calculated control limit by using the configured rule, and if the control limit is abnormal, sending an alarm and lasting abnormal data.
7. The process control-based production management method according to claim 6, wherein in step S200, for the cut data, status detection is performed on the device status data, threshold detection is performed on the product data, if abnormality is detected, abnormality information is recorded and an alarm is sent after authority verification; if no abnormality is detected, judging whether the equipment state data is changed compared with the previous data of the linear time backtracking, and if so, storing the changed data.
8. The process control-based production management method according to claim 6, wherein in the step S300, the extracted raw data is subjected to extreme error value removal and re-quantization; a continuously collected set of data is divided into four halves, wherein the smallest 25% of the halves are the first half, the largest 25% are the third half, and the middle 50% are the second half and the fourth half, and if the subsequently received data is beyond the first half or the third half, the data can be considered as an artificial extreme error value.
9. The process control-based production management method according to claim 6, wherein in the step S300, the control limits include an upper control limit and a lower control limit for production and manufacturing type data:
upper control limit = process parameter mean +3 standard deviation
Lower control limit = process parameter mean-3 standard deviation
For production equipment type data, the control limits include an upper control limit and a lower control limit:
upper control limit = average value of product parameters detected by sensor +3 standard deviation
Lower control limit = average-3 x standard deviation of product parameters detected by the sensor
Where x represents each data point in the sample,represents the average value of the samples, and n represents the size of the samples.
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