CN113139734A - Intelligent manufacturing management system based on data mining - Google Patents
Intelligent manufacturing management system based on data mining Download PDFInfo
- Publication number
- CN113139734A CN113139734A CN202110482032.XA CN202110482032A CN113139734A CN 113139734 A CN113139734 A CN 113139734A CN 202110482032 A CN202110482032 A CN 202110482032A CN 113139734 A CN113139734 A CN 113139734A
- Authority
- CN
- China
- Prior art keywords
- module
- equipment
- scrapping
- management system
- data mining
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 60
- 238000007418 data mining Methods 0.000 title claims abstract description 17
- 230000007547 defect Effects 0.000 claims abstract description 31
- 238000007726 management method Methods 0.000 claims abstract description 30
- 238000001514 detection method Methods 0.000 claims abstract description 28
- 230000002950 deficient Effects 0.000 claims abstract description 22
- 238000012423 maintenance Methods 0.000 claims abstract description 15
- 238000009776 industrial production Methods 0.000 claims abstract description 14
- 238000010219 correlation analysis Methods 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 9
- 238000013079 data visualisation Methods 0.000 claims description 5
- 239000000284 extract Substances 0.000 claims description 4
- 210000001503 joint Anatomy 0.000 claims description 2
- 230000002035 prolonged effect Effects 0.000 claims description 2
- 238000000034 method Methods 0.000 abstract description 7
- 230000008569 process Effects 0.000 abstract description 5
- 238000012098 association analyses Methods 0.000 abstract description 3
- 238000005498 polishing Methods 0.000 description 7
- 230000006399 behavior Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 3
- 230000001360 synchronised effect Effects 0.000 description 3
- 230000032683 aging Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000004615 ingredient Substances 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000010146 3D printing Methods 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Manufacturing & Machinery (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- General Factory Administration (AREA)
Abstract
The invention relates to the field of intelligent management and manufacturing, and discloses an intelligent manufacturing management system based on data mining. The quality detection module detects product data, finds products with defects, the production acquisition module acquires various data on an industrial production line, the association analysis module combines and analyzes the product data and the industrial production line data, whether the product data are artificial reasons or equipment reasons is analyzed, if the product data are artificial reasons, the product data are submitted to the rectification scheme module, a corresponding rectification scheme is formulated, and if the product data are equipment reasons, the product data are submitted to the operation and maintenance management module to process the equipment. The invention improves the efficiency of product quality detection, analyzes the reasons of defective products, eliminates the hidden danger of defective products and improves the yield of products.
Description
Technical Field
The invention relates to the field of intelligent management and manufacturing, and particularly discloses an intelligent manufacturing management system based on data mining.
Background
With the increasingly intense global market competition, the manufacturing industry is faced with more stringent requirements in the fields of improving product quality, increasing production benefits, reducing production cost, reducing resource consumption and the like. Manufacturing enterprises borrow the continuous innovation of manufacturing technology, and through introducing emerging technologies such as internet of things, big data, 3D printing and cloud computing, the transparency, the intellectualization and the global optimization of the production process are realized to meet the challenges, so that a new round of industrial revolution is initiated.
At present, in an enterprise with a certain scale, various computer management software such as Enterprise Resource Planning (ERP), Manufacturing Execution System (MES), Equipment Management System (EMS) and the like are usually used for assisting management, but systems which are convenient to manage and control manufacturing quality are few, some defective products are often produced in industrial production due to equipment reasons or manual reasons, in the past, due to lack of data support, only the reasons for generating defects are manually checked, and manual checking is low in efficiency and difficult to operate, so that quality control of the products is often unsatisfactory. Although some intelligent systems can detect and record quality defects, most of analyses in the prior art depend on preset conditions for matching, the degree of intelligence is low, deep analysis on the causes of quality problems is lacked, and the accuracy of the found causes is poor.
Disclosure of Invention
The invention aims to provide an intelligent manufacturing management system based on data mining, which tracks the reasons of defective products through data mining and provides a corresponding solution for the reasons of defective products.
The application provides the following technical scheme:
an intelligent manufacturing management system based on data mining comprises a quality detection module, a production acquisition module, an association analysis module and an rectification scheme module;
the quality detection module is used for carrying out quality detection on the product and counting defect data by acquiring the appearance of the product;
the production acquisition module is used for acquiring data of products with defects corresponding to the industrial production line;
the correlation analysis module is used for generating a correlation relationship according to the defect data and the data corresponding to the industrial production line and analyzing the reasons for generating the defects;
and the rectification scheme module is used for generating a corresponding rectification scheme according to the reason for generating the defects.
The principle and the advantages of the invention are as follows: by collecting data of product defects and data on an industrial production line, the two data are subjected to correlation analysis, the reasons for generating the defects are searched, and an rectification and improvement scheme is made according to the reasons for generating the defects. Compared with the prior art, the efficiency of checking the defect causes is improved, a corresponding correction scheme is formulated, the number of defective products in the production process in the future is reduced, and the production yield is improved.
Further, the quality detection module comprises an image data visualization module and is used for shooting a product picture, extracting the characteristics of the product picture through the product picture and analyzing the product defects according to the characteristics.
The image data visualization module is used for shooting the picture of the product, extracting the characteristic of the picture of the product according to an image recognition algorithm and outputting the analysis result of the corresponding product defect, so that the product is detected instead of manpower, and the detection efficiency is improved.
The staff training module comprises an operation specification module and a timing module;
the operation specification module is used for displaying the operation specification of the staff before the production equipment is started;
and the timing module is used for setting the display time of the operation specification of the display staff, and the production equipment can be started after the display time passes.
For product defects caused by employee operation specifications, the employee operation specifications are displayed to the employee before the employee starts the equipment, and the specification awareness of the employee is enhanced.
Further, the staff training module also comprises a key point reminding module and an intelligent question and answer module;
the key reminding module is used for adjusting the display time of each station according to the proportion of defective products produced by each station of the industrial production line;
the intelligent question-answering module extracts questions from the staff operation specifications, after the staff operation specifications are displayed, the questions are put forward, the next display time is shortened if the answers are correct, and the next display time is prolonged if the answers are wrong.
For industrial production lines with a large number of defective products, staff spend more time to read staff operation specifications, memory is deepened, the learning condition of the staff is detected through an intelligent question-answering module, and the time of next learning is increased or reduced according to the learning condition of the staff.
The staff training module further comprises a behavior detection module, wherein the behavior detection module is used for acquiring whether the operation specification of the staff production process meets the requirement, if so, the next display time is reduced, and if not, the next display time is increased.
After the condition that the employee actually applies to the production process after learning the employee operation specification is detected, the situation that the employee remembers the hard back once when learning is avoided, and the employee cannot be understood through the whole process.
The system further comprises an operation and maintenance management module, wherein the operation and maintenance management module comprises an equipment tracing module, an equipment defining module and an equipment associating module;
the equipment tracing module traces the equipment for producing the defective products according to the industrial production line data acquired by the production acquisition module;
the equipment definition module is used for detecting various parameters of the equipment and dividing the equipment into adjustable equipment and scrappable equipment according to the equipment parameters;
and the equipment association module is used for searching other equipment which can scrap the equipment in the same batch.
The method comprises the steps of tracing back the equipment for producing the product to the product defect which is generated due to equipment reasons, analyzing whether the equipment can be normally produced or can not be repaired after being repaired, defining the equipment which can not be adjusted as scrappable equipment, and searching equipment which is synchronous with the scrappable equipment, wherein the synchronous equipment is close to the scrappable equipment in terms of service time and service frequency, detecting whether the equipment has the same fault, analyzing whether the synchronous equipment is the scrappable equipment, and eliminating potential hidden danger.
Furthermore, the operation and maintenance management module further comprises a scrapping plan module, and the scrapping plan module makes a scrapping plan according to the detected number of the scrappable devices.
And for equipment which cannot be repaired, a scrap plan is made for scrap.
Furthermore, the scrapping plan module also comprises a scrapping degree detection module and a time length estimation module, wherein the scrapping degree detection module is used for determining the fault degree of the scrappable equipment according to the equipment parameters, and the time length estimation module is used for estimating the residual time length used by the equipment according to the fault degree of the equipment and increasing the workload of the rest of equipment by taking the shortest time length as a standard.
Furthermore, the scrapping plan module also comprises a scrapping degree detection module, the scrapping degree detection module is used for determining the fault degree of the scrappable equipment according to the equipment parameters and ranking the scrappable equipment according to the fault degree from high to low, the scrapping plan module sets a scrapping period, and the scrappable equipment is scrapped according to the scrapping period and the fault degree ranking.
The scrappable equipment is scrapped in batches, the equipment with high fault degree is scrapped preferentially, the equipment with low fault degree is scrapped in sequence, and the reduction of the production quantity caused by scrapping the equipment is reduced.
Further, the operation and maintenance management module further comprises an equipment purchasing module, the equipment purchasing module is in butt joint with a third-party supplier, corresponding equipment is scheduled for the third-party supplier according to the scrapping plan, and the scheduled equipment is scheduled for the third-party supplier before the scrapping plan is executed.
When the equipment is scrapped, the corresponding equipment is purchased from a third-party supplier, the vacancy is filled, and the reduction of the production amount caused by scrapping the equipment is further reduced.
Drawings
FIG. 1 is a logic diagram of a first embodiment of the present invention;
FIG. 2 is a logic block diagram of an employee training module according to an embodiment of the present invention;
FIG. 3 is a logic diagram of a second embodiment of the present invention;
fig. 4 is a logic block diagram of an operation and maintenance management module according to a second embodiment of the present invention.
Detailed Description
The following is further detailed by way of specific embodiments:
example one
An embodiment substantially as shown in figure 1: the system comprises a quality detection module, a production acquisition module, a correlation analysis module, a rectification scheme module and a staff training module.
The quality detection module comprises an image data visualization module, the image data visualization module shoots a product photo, characteristics in the product are extracted, such as the outline, the color, the shape, the pattern, the surface smoothness and the like of the product are identified, the image identification algorithm is used for converting the image information into character information according to the input characteristics, and the defective product is found out according to the character information and is characterized by stains, defects, gaps, breakage and the like.
The production acquisition module is used for acquiring data of an industrial production line, including output, equipment running conditions, staff operation conditions, product numbers, production time and the like.
The correlation analysis module is used for combining the characteristics of defective products with the production acquisition module, analyzing the reasons of the defective products, mainly including human reasons and equipment reasons, for example, the output of the defective products is obviously greater than that of other production lines on the production line of the same product, and through correlation analysis, the production line is considered to be defective products caused by too high output speed; or if the output of a certain device is lower than that of other devices and the product has defects, the device may be in failure or the parameter setting is abnormal.
And the rectification scheme module is used for formulating a corresponding rectification scheme according to the reasons analyzed by the association analysis module, and the rectification scheme module is used for formulating a rectification scheme aiming at the product defects formed by human reasons, so that the rectification scheme is formulated, the reasons for the product defects formed by human reasons comprise too high production speed, untight production process, incorrect ingredient proportion, incorrect equipment operation mode and the like, and corresponding staff operation specifications are formulated according to the reasons.
The staff training module, as shown in fig. 2, includes a key point reminding module, a timing module, an operation specification module, an intelligent question and answer module, and a behavior collection module.
The operation specification module is used for displaying the employee operation specification established by the rectification scheme module for the employee before the employee starts the production equipment, the timing module can set the viewing time, when the viewing time is over, the employee can start the equipment, and the key reminding module sets the time according to the proportion of defective products produced by the employee, for example, if 5 products are defective products in every 100 products on average, the viewing time is set to be 5 minutes, and if 10 products are defective products, the viewing time is 10 minutes. After the watching time is finished, the intelligent question-answering module extracts questions from the staff operation specifications and enables the staff to answer the questions, if the answers are correct, the next watching time is reduced by one minute, and if the answers are wrong, the next watching time is increased by one minute. After the equipment is started, the behavior acquisition module acquires the content of whether the operation of the staff accords with the staff operation specification, such as whether the ingredient proportion is correct, whether the polishing time is up, and the like. If the time meets the standard, the next watching time is reduced by one minute, and if the time does not meet the standard, the next watching time is increased by one minute.
The specific implementation process is as follows:
the quality detection module shoots a product X through a camera, extracts the outline, the color, the shape, the pattern and the surface smoothness in a product picture, and analyzes that the surface of the product is rough and does not meet the specification according to the existing picture recognition algorithm.
Shooting through the industrial camera on the production line that employee A is not conform to the standard in the production grinding process, not grinding according to normal flow, 3 products have the rough defect in surface in its every 100 products.
And obtaining the product X through correlation analysis, wherein the surface of the product X is rough because the production process of the employee A is not polished according to a normal flow, and a corresponding polishing operation specification is established for the employee.
When staff A just starts the equipment of polishing next time, need watch 3 minutes and polish the operation standard after can start the equipment to watch and accomplish and can follow and polish and draw a question and let staff A answer from the operation standard, after staff A answered correctly, the time that it watched the operation standard of polishing before starting the equipment next time reduces to 2 minutes.
Then detect staff A at the operation flow of the in-process of polishing, propose in the discovery operation of polishing the operation standard and all polish the inside and outside surface of product, and staff A only polishes the outside surface, does not accord with the flow in the operation of polishing standard, watches before the equipment of next start-up and polish the time of operation standard and increase to 3 minutes.
Example two
The second embodiment is basically as shown in fig. 3, and is different from the first embodiment in that the second embodiment further includes an operation and maintenance management module.
The operation and maintenance management module is shown in fig. 4, and includes an equipment tracing module, an equipment defining module, an equipment associating module, a scrap planning module, and an equipment purchasing module, and a series of adjustments are made for product defects caused by equipment reasons.
The equipment tracing module traces the defective product produced by the equipment according to the information such as the equipment running condition, the product number, the production time and the like acquired by the production acquisition module, and traces the equipment for producing the defective product.
The equipment definition module acquires various parameters of the equipment, such as current, voltage, amplitude, rotating speed, pressure intensity, pressure, component integrity and the like, judges the type of equipment fault according to the parameters, and judges whether the equipment fault can be maintained or not, if the equipment fault is caused by wrong equipment setting or damage of a certain component, the problem can be solved through maintenance, the equipment is defined as adjustable equipment, and operation and maintenance personnel are informed to maintain the equipment in the future; if the equipment cannot be solved through maintenance due to equipment aging and the like, the equipment is defined as scrappable equipment.
The equipment association module is used for searching other equipment purchased with the equipment for producing the defective products in the same period according to the historical equipment purchase order, and then detecting whether the other equipment in the same period has the same problem or not through the equipment definition module, wherein the equipment with the same problem is also defined as scrappable equipment or adjustable equipment.
And the scrapping plan module is used for making a scrapping plan according to the detected number of the scrappable devices, and in the embodiment, scrapping treatment is uniformly performed on all the scrappable devices.
And the equipment purchasing module is butted with the third-party supplier, presets corresponding equipment to the third-party supplier according to the scrap plan, and schedules the preset equipment to the third-party supplier when the scrap plan is executed.
The scrapping plan module further comprises a scrapping degree detection module and a time length estimation module, the scrapping degree detection module is used for determining the fault degree of the scrappable equipment according to the equipment parameters, the time length estimation module estimates the residual time length used by the equipment according to the fault degree of the equipment, and the time length estimation module increases the workload of the rest of equipment by taking the shortest time length as a standard.
The specific implementation process is as follows:
the correlation analysis module analyzes that the defect of the product Y is caused by equipment reasons, the equipment tracing module finds out the equipment M for producing the product Y according to the equipment running condition shot by the production acquisition module, the equipment M is acquired by the equipment definition module, and the abnormality of each parameter is found through the parameters of current, voltage, amplitude, rotating speed and the like of the acquisition equipment M, so that the defect is caused by the aging of the equipment, the defect cannot be solved through repair, and the equipment M is defined as scrappable equipment. And then according to the historical purchase order of the equipment, finding 10 pieces of equipment in the same period as the equipment M, collecting parameters such as current, voltage, amplitude, rotating speed and the like of the 10 pieces of equipment, wherein the parameters of 3 pieces of equipment are normal, one parameter of 4 pieces of equipment is abnormal, judging the equipment as adjustable equipment, informing operation and maintenance personnel to maintain the equipment in the future, and defining the 3 pieces of equipment as scrappable equipment if the remaining 3 pieces of equipment are the same as the equipment M and all parameters are abnormal.
The 4 devices are scrapped uniformly after three days according to a plan, 4 same devices are scheduled for a third-party supplier at the same time, and the devices are called from the third-party supplier one day before scrapping treatment.
EXAMPLE III
The difference between this embodiment and the third embodiment is that: the scrap plan module also comprises a scrap degree detection module.
The scrapping degree detection module judges the equipment fault level according to the equipment parameters, in the embodiment, 10 equipment in total are scrapped, the fault level is judged according to the equipment rotating speed, the lower the rotating speed is, the higher the fault level is, the scrapping plan module is set to scrap every 3 days, and 2 equipment are scrapped every time from high to low according to the fault level ranking. And simultaneously reserving 10 corresponding devices for the third-party supplier, and calling two devices for the third-party supplier on the day before each scrapping.
The foregoing are merely exemplary embodiments of the present invention, and no attempt is made to show structural details of the invention in more detail than is necessary for the fundamental understanding of the art, the description taken with the drawings making apparent to those skilled in the art how the several forms of the invention may be embodied in practice with the teachings of the invention. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. An intelligent manufacturing management system based on data mining is characterized in that: the system comprises a quality detection module, a production acquisition module, a correlation analysis module and an rectification scheme module;
the quality detection module is used for carrying out quality detection on the product and counting defect data by acquiring the appearance of the product;
the production acquisition module is used for acquiring data of products with defects corresponding to the industrial production line;
the correlation analysis module is used for generating a correlation relationship according to the defect data and the data corresponding to the industrial production line and analyzing the reasons for generating the defects;
and the rectification scheme module is used for generating a corresponding rectification scheme according to the reason for generating the defects.
2. The intelligent data mining-based manufacturing management system of claim 1, wherein: the quality detection module comprises an image data visualization module and is used for shooting a product picture, extracting the characteristics of the product picture through the product picture and analyzing the product defects according to the characteristics.
3. The intelligent data mining-based manufacturing management system of claim 2, wherein: the staff training module comprises an operation specification module and a timing module;
the operation specification module is used for displaying the operation specification of the staff before the production equipment is started;
and the timing module is used for setting the display time of the operation specification of the display staff, and the production equipment can be started after the display time passes.
4. The intelligent data mining-based manufacturing management system of claim 3, wherein: the staff training module also comprises a key point reminding module and an intelligent question and answer module;
the key reminding module is used for adjusting the display time of each station according to the proportion of defective products produced by each station of the industrial production line;
the intelligent question-answering module extracts questions from the staff operation specifications, after the staff operation specifications are displayed, the questions are put forward, the next display time is shortened if the answers are correct, and the next display time is prolonged if the answers are wrong.
5. The intelligent data mining-based manufacturing management system of claim 4, wherein: the staff training module further comprises a behavior detection module, the behavior detection module is used for acquiring whether the operation specification of the staff production process meets the requirement, if so, the next display time is reduced, and if not, the next display time is increased.
6. The intelligent data mining-based manufacturing management system of claim 2, wherein: the operation and maintenance management module comprises an equipment tracing module, an equipment definition module and an equipment association module;
the equipment tracing module traces the equipment for producing the defective products according to the industrial production line data acquired by the production acquisition module;
the equipment definition module is used for detecting various parameters of the equipment and dividing the equipment into adjustable equipment and scrappable equipment according to the equipment parameters;
and the equipment association module is used for searching other equipment which can scrap the equipment in the same batch.
7. The intelligent data mining-based manufacturing management system of claim 6, wherein: the operation and maintenance management module further comprises a scrapping plan module, and the scrapping plan module makes a scrapping plan according to the detected number of the scrappable devices.
8. The intelligent data mining-based manufacturing management system of claim 7, wherein: the scrapping plan module further comprises a scrapping degree detection module and a time length estimation module, the scrapping degree detection module is used for determining the fault degree of the scrappable equipment according to the equipment parameters, the time length estimation module estimates the residual time length used by the equipment according to the fault degree of the equipment, and the time length estimation module increases the workload of the rest of equipment by taking the shortest time length as a standard.
9. The intelligent data mining-based manufacturing management system of claim 7, wherein: the scrapping planning module ranks from high to low according to the fault degree, sets a scrapping period, and scrapps the scrappable equipment according to the scrapping period and the fault degree rank.
10. The intelligent data mining-based manufacturing management system of claim 8, wherein: the operation and maintenance management module further comprises an equipment purchasing module, the equipment purchasing module is in butt joint with a third-party supplier, corresponding equipment is scheduled for the third-party supplier according to the scrapping plan, and the scheduled equipment is scheduled for the third-party supplier before the scrapping plan is executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110482032.XA CN113139734A (en) | 2021-04-30 | 2021-04-30 | Intelligent manufacturing management system based on data mining |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110482032.XA CN113139734A (en) | 2021-04-30 | 2021-04-30 | Intelligent manufacturing management system based on data mining |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113139734A true CN113139734A (en) | 2021-07-20 |
Family
ID=76816729
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110482032.XA Pending CN113139734A (en) | 2021-04-30 | 2021-04-30 | Intelligent manufacturing management system based on data mining |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113139734A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837598A (en) * | 2021-09-18 | 2021-12-24 | 北京精密机电控制设备研究所 | Information acquisition method based on intelligent manufacturing |
| CN114637270A (en) * | 2022-05-17 | 2022-06-17 | 成都秦川物联网科技股份有限公司 | Intelligent manufacturing industry Internet of things based on distributed control and control method |
| CN116579769A (en) * | 2023-07-13 | 2023-08-11 | 南通远征冷冻设备有限公司 | Production control method of refrigeration equipment |
| CN117074845A (en) * | 2023-10-16 | 2023-11-17 | 广州市零脉信息科技有限公司 | Electronic component quality detection platform |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0863224A (en) * | 1994-08-19 | 1996-03-08 | Fujitsu Ltd | Equipment operation management device |
| CN1213113A (en) * | 1997-08-29 | 1999-04-07 | 日本电气株式会社 | Manufacturing process change control apparatus and its manufacturing process change control method |
| JP2003303016A (en) * | 2002-02-05 | 2003-10-24 | Tokyo Gas Co Ltd | Equipment management system, equipment management server, equipment management method, controller, program, and recording medium |
| CN101221636A (en) * | 2006-12-27 | 2008-07-16 | 株式会社东芝 | Data analysis apparatus and method |
| KR20090071685A (en) * | 2007-12-28 | 2009-07-02 | 한국기계연구원 | Estimating superannuation method of facilities |
| CN103632058A (en) * | 2013-12-03 | 2014-03-12 | 中国特种设备检测研究院 | Dynamic risk evaluation system of pressure-bearing device |
| CN106529152A (en) * | 2016-11-04 | 2017-03-22 | 合肥观池信息科技有限责任公司 | Hospital digital logistics equipment management system |
| CN106952033A (en) * | 2017-03-16 | 2017-07-14 | 沈阳新代信息技术有限公司 | A kind of mixing plant product quality monitoring system and method based on multi-data source |
| CN107121606A (en) * | 2017-04-24 | 2017-09-01 | 国网浙江省电力公司台州供电公司 | For Substation Operating and the intelligent terminal of tour |
| CN108960452A (en) * | 2018-07-26 | 2018-12-07 | 合肥云联电子科技有限公司 | A kind of intelligent monitoring system of industrial control equipment |
| US20190086905A1 (en) * | 2017-09-21 | 2019-03-21 | Fuji Xerox Co., Ltd. | Quality control apparatus |
| CN109542289A (en) * | 2018-11-30 | 2019-03-29 | Oppo（重庆）智能科技有限公司 | Operating method, device, equipment and the storage medium of MES |
| CN110276599A (en) * | 2019-06-28 | 2019-09-24 | 重庆回形针信息技术有限公司 | Management system and method with ability of self-teaching |
| CN110276410A (en) * | 2019-06-27 | 2019-09-24 | 京东方科技集团股份有限公司 | Determine method, apparatus, electronic equipment and the storage medium of poor prognostic cause |
| CN111242464A (en) * | 2020-01-08 | 2020-06-05 | 广东省智能制造研究所 | Ceramic bathroom quality management method based on SPC |
| CN111948994A (en) * | 2020-08-12 | 2020-11-17 | 杭州百子尖科技股份有限公司 | Industrial production line closed-loop automatic quality control method based on data integration and correlation analysis |
| CN112379198A (en) * | 2020-11-05 | 2021-02-19 | 华润电力技术研究院有限公司 | Method and system for evaluating residual life of equipment |
| CN112712256A (en) * | 2020-12-29 | 2021-04-27 | 广东电网有限责任公司 | Method for evaluating overhaul sequence of low-voltage distribution network equipment |
-
2021
- 2021-04-30 CN CN202110482032.XA patent/CN113139734A/en active Pending
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0863224A (en) * | 1994-08-19 | 1996-03-08 | Fujitsu Ltd | Equipment operation management device |
| CN1213113A (en) * | 1997-08-29 | 1999-04-07 | 日本电气株式会社 | Manufacturing process change control apparatus and its manufacturing process change control method |
| JP2003303016A (en) * | 2002-02-05 | 2003-10-24 | Tokyo Gas Co Ltd | Equipment management system, equipment management server, equipment management method, controller, program, and recording medium |
| CN101221636A (en) * | 2006-12-27 | 2008-07-16 | 株式会社东芝 | Data analysis apparatus and method |
| KR20090071685A (en) * | 2007-12-28 | 2009-07-02 | 한국기계연구원 | Estimating superannuation method of facilities |
| CN103632058A (en) * | 2013-12-03 | 2014-03-12 | 中国特种设备检测研究院 | Dynamic risk evaluation system of pressure-bearing device |
| CN106529152A (en) * | 2016-11-04 | 2017-03-22 | 合肥观池信息科技有限责任公司 | Hospital digital logistics equipment management system |
| CN106952033A (en) * | 2017-03-16 | 2017-07-14 | 沈阳新代信息技术有限公司 | A kind of mixing plant product quality monitoring system and method based on multi-data source |
| CN107121606A (en) * | 2017-04-24 | 2017-09-01 | 国网浙江省电力公司台州供电公司 | For Substation Operating and the intelligent terminal of tour |
| US20190086905A1 (en) * | 2017-09-21 | 2019-03-21 | Fuji Xerox Co., Ltd. | Quality control apparatus |
| CN108960452A (en) * | 2018-07-26 | 2018-12-07 | 合肥云联电子科技有限公司 | A kind of intelligent monitoring system of industrial control equipment |
| CN109542289A (en) * | 2018-11-30 | 2019-03-29 | Oppo（重庆）智能科技有限公司 | Operating method, device, equipment and the storage medium of MES |
| CN110276410A (en) * | 2019-06-27 | 2019-09-24 | 京东方科技集团股份有限公司 | Determine method, apparatus, electronic equipment and the storage medium of poor prognostic cause |
| CN110276599A (en) * | 2019-06-28 | 2019-09-24 | 重庆回形针信息技术有限公司 | Management system and method with ability of self-teaching |
| CN111242464A (en) * | 2020-01-08 | 2020-06-05 | 广东省智能制造研究所 | Ceramic bathroom quality management method based on SPC |
| CN111948994A (en) * | 2020-08-12 | 2020-11-17 | 杭州百子尖科技股份有限公司 | Industrial production line closed-loop automatic quality control method based on data integration and correlation analysis |
| CN112379198A (en) * | 2020-11-05 | 2021-02-19 | 华润电力技术研究院有限公司 | Method and system for evaluating residual life of equipment |
| CN112712256A (en) * | 2020-12-29 | 2021-04-27 | 广东电网有限责任公司 | Method for evaluating overhaul sequence of low-voltage distribution network equipment |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113837598A (en) * | 2021-09-18 | 2021-12-24 | 北京精密机电控制设备研究所 | Information acquisition method based on intelligent manufacturing |
| CN113837598B (en) * | 2021-09-18 | 2024-04-16 | 北京精密机电控制设备研究所 | Information acquisition method based on intelligent manufacturing |
| CN114637270A (en) * | 2022-05-17 | 2022-06-17 | 成都秦川物联网科技股份有限公司 | Intelligent manufacturing industry Internet of things based on distributed control and control method |
| US11681283B1 (en) | 2022-05-17 | 2023-06-20 | Chengdu Qinchuan Iot Technology Co., Ltd. | Intelligent manufacturing industrial Internet of Things based on distributed control, control methods and media thereof |
| CN116579769A (en) * | 2023-07-13 | 2023-08-11 | 南通远征冷冻设备有限公司 | Production control method of refrigeration equipment |
| CN116579769B (en) * | 2023-07-13 | 2023-11-24 | 南通远征冷冻设备有限公司 | Production control method of refrigeration equipment |
| CN117074845A (en) * | 2023-10-16 | 2023-11-17 | 广州市零脉信息科技有限公司 | Electronic component quality detection platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113139734A (en) | Intelligent manufacturing management system based on data mining | |
| CN110709688B (en) | Method for predicting defects in an assembly unit | |
| US20230039805A1 (en) | Method and device for testing product quality | |
| US12020415B2 (en) | Method for monitoring manufacture of assembly units | |
| US10423669B2 (en) | Manufacturing process visualization apparatus and method | |
| CN111353983A (en) | Defect detection and identification method and device, computer readable medium and electronic equipment | |
| CN111289538A (en) | PCB element detection system and detection method based on machine vision | |
| CN115034525B (en) | Steel pipe order production period prediction monitoring system and method based on data analysis | |
| US20220051051A1 (en) | Method and assistance system for parameterizing an anomaly detection method | |
| CN114359235A (en) | Wood surface defect detection method based on improved YOLOv5l network | |
| CN117483209B (en) | Method and system for controlling production quality of reflective film based on machine vision | |
| CN111768080B (en) | Method and system for evaluating finished progress of manufactured product | |
| CN117555937A (en) | Data acquisition method and system for product recommendation | |
| CN105303565A (en) | Product appearance LOGO detection method | |
| CN114359250B (en) | Method and device for determining defect shape of wafer, electronic equipment and storage medium | |
| CN111212434A (en) | WIFI module quality prediction method, device, equipment and storage medium | |
| CN110992333B (en) | COF Film defect AITraining database and application | |
| CN114860802A (en) | Fusion method and system of time sequence pedestrian volume data and scalar label number | |
| Kulińska et al. | Analysis of solutions dedicated to non-conformity prevention | |
| CN114637893A (en) | FMEA database system and processing method thereof | |
| Piyasinchart et al. | Detection of Defects in Work-in-Process Products Using Deep Learning: A Case of a Water Tap Production Line | |
| CN113256267A (en) | Project data processing method and system | |
| CN111474181A (en) | Panel defect detecting and classifying system in box forming process stage | |
| CN110689058B (en) | AI algorithm-based environment detection method | |
| CN118072111B (en) | Intelligent vehicle frame deburring system and method based on artificial intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |