CN114153991A - Knowledge graph method based on intelligent manufacturing scene - Google Patents
Knowledge graph method based on intelligent manufacturing scene Download PDFInfo
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- CN114153991A CN114153991A CN202111528053.7A CN202111528053A CN114153991A CN 114153991 A CN114153991 A CN 114153991A CN 202111528053 A CN202111528053 A CN 202111528053A CN 114153991 A CN114153991 A CN 114153991A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 94
- 238000000034 method Methods 0.000 title claims abstract description 16
- 239000002994 raw material Substances 0.000 claims abstract description 32
- 238000003860 storage Methods 0.000 claims abstract description 15
- 238000007689 inspection Methods 0.000 claims abstract description 9
- 230000003203 everyday effect Effects 0.000 claims description 3
- 238000009826 distribution Methods 0.000 abstract description 3
- 230000000694 effects Effects 0.000 description 3
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/36—Creation of semantic tools, e.g. ontology or thesauri
- G06F16/367—Ontology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/17—Details of further file system functions
- G06F16/172—Caching, prefetching or hoarding of files
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- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
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- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
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- 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
Abstract
The invention discloses a knowledge graph method based on an intelligent manufacturing scene, which comprises the following steps: s1, knowledge of production tasks; s2, real-time production data; s3, raw material reject ratio and equipment fault data; s4, job diary data. According to the invention, the production task list is automatically generated through the production task module, then raw materials, station alarm data, quality inspection data and operators are supervised, and work diaries are generated for storage, so that the automatic distribution of production tasks is realized, and the production process of each part is traced.
Description
Technical Field
The invention relates to a knowledge graph method based on an intelligent manufacturing scene.
Background
The intelligent manufacturing is a powerful tool for realizing automation, intellectualization, precision and greening of the generation process by upgrading and transforming the traditional manufacturing industry, is an important support for cultivating and developing strategic emerging industries, and is an important mark for measuring the national industrialization level.
At present, the intelligent manufacturing function is single, and the reject ratio of a production line cannot be traced. Therefore, a knowledge graph method based on an intelligent manufacturing scenario is in urgent need to be proposed.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a knowledge graph method based on an intelligent manufacturing scene.
In order to solve the technical problems, the invention provides the following technical scheme:
the invention provides a knowledge graph method based on an intelligent manufacturing scene, which comprises the following steps:
s1, knowledge of production tasks: the production task module generates a production task list according to production requirements and equipment data of the intelligent manufacturing production line by adopting a production strategy;
s2, real-time production data: the data acquisition module acquires serial number data, station alarm data, raw material reject ratio loss data, quality inspection report data, production personnel operation data and quality inspection personnel operation data of each type of raw materials in different batches on the intelligent manufacturing production line;
s3, raw material reject ratio and equipment fault data: the data processing module obtains the reject ratio loss data corresponding to each type of raw materials in different batches from all the data collected in the step S2 through a production strategy, and obtains the equipment fault condition on the corresponding station according to the station alarm data;
s4, job diary data: the storage module generates a working diary file from data acquired on an intelligent manufacturing production line every day and stores the working diary file.
As a preferable technical scheme of the invention, the method also comprises the following steps:
s5, device life data: the data acquisition module acquires the machine number, the purchase time and the production time of the equipment on all stations of the intelligent manufacturing production line, and sends out a replacement prompt when the equipment reaches the service life according to the service life of the equipment.
As a preferable technical scheme of the invention, the method also comprises the following steps:
s6, raw material storage data: supplier data of raw materials, raw material names, raw material quantities and raw material storage positions are stored in a storage module.
As a preferable technical scheme of the invention, the method also comprises the following steps:
s7, production progress data: and adjusting the production task list according to the finished production progress.
Compared with the prior art, the invention has the following beneficial effects:
according to the invention, the production task list is automatically generated through the production task module, then raw materials, station alarm data, quality inspection data and operators are supervised, and work diaries are generated for storage, so that the automatic distribution of production tasks is realized, and the production process of each part is traced.
Drawings
FIG. 1 is a schematic diagram of one embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1
To achieve the object of the present invention, as shown in fig. 1, the present embodiment provides a method for knowledge mapping based on intelligent manufacturing scenario, which includes the following steps:
s1, knowledge of production tasks: the production task module generates a production task list according to production requirements and equipment data of the intelligent manufacturing production line by adopting a production strategy;
s2, real-time production data: the data acquisition module acquires serial number data, station alarm data, raw material reject ratio loss data, quality inspection report data, production personnel operation data and quality inspection personnel operation data of each type of raw materials in different batches on the intelligent manufacturing production line;
s3, raw material reject ratio and equipment fault data: the data processing module obtains the reject ratio loss data corresponding to each type of raw materials in different batches from all the data collected in the step S2 through a production strategy, and obtains the equipment fault condition on the corresponding station according to the station alarm data;
s4, job diary data: the storage module generates a working diary file from data acquired on an intelligent manufacturing production line every day and stores the working diary file.
According to the embodiment, the production task list is automatically generated through the production task module, then raw materials, station alarm data, quality inspection data and operators are supervised, a working diary is generated for storage, automatic production task distribution is realized, and the production process of each part is traced.
Example 2
In order to further optimize the implementation effect of the present invention, the rest is the same as embodiment 1, except that the embodiment further comprises the following steps:
s5, device life data: the data acquisition module acquires the machine number, the purchase time and the production time of the equipment on all stations of the intelligent manufacturing production line, and sends out a replacement prompt when the equipment reaches the service life according to the service life of the equipment.
Example 3
In order to further optimize the implementation effect of the present invention, the rest is the same as the embodiment 2, except that the embodiment further comprises the following steps:
s6, raw material storage data: supplier data of raw materials, raw material names, raw material quantities and raw material storage positions are stored in a storage module.
Various information of the raw materials is stored, so that a production task list is convenient to generate, and searching and management are also convenient.
Example 4
In order to further optimize the implementation effect of the present invention, the rest is the same as embodiment 3, except that the embodiment further comprises the following steps:
s7, production progress data: and adjusting the production task list according to the finished production progress.
And the production task list is adjusted in time according to the completed production progress, so that the production efficiency is further improved.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A knowledge graph method based on an intelligent manufacturing scene is characterized by comprising the following steps:
s1, knowledge of production tasks: the production task module generates a production task list according to production requirements and equipment data of the intelligent manufacturing production line by adopting a production strategy;
s2, real-time production data: the data acquisition module acquires serial number data, station alarm data, raw material reject ratio loss data, quality inspection report data, production personnel operation data and quality inspection personnel operation data of each type of raw materials in different batches on the intelligent manufacturing production line;
s3, raw material reject ratio and equipment fault data: the data processing module obtains the reject ratio loss data corresponding to each type of raw materials in different batches from all the data collected in the step S2 through a production strategy, and obtains the equipment fault condition on the corresponding station according to the station alarm data;
s4, job diary data: the storage module generates a working diary file from data acquired on an intelligent manufacturing production line every day and stores the working diary file.
2. The intellectual property map method based on intelligent manufacturing scenario as claimed in claim 1 further comprising the steps of:
s5, device life data: the data acquisition module acquires the machine number, the purchase time and the production time of the equipment on all stations of the intelligent manufacturing production line, and sends out a replacement prompt when the equipment reaches the service life according to the service life of the equipment.
3. The intellectual property map method based on intelligent manufacturing scenario as claimed in claim 1 further comprising the steps of:
s6, raw material storage data: supplier data of raw materials, raw material names, raw material quantities and raw material storage positions are stored in a storage module.
4. The intellectual property map method based on intelligent manufacturing scenario as claimed in claim 1 further comprising the steps of:
s7, production progress data: and adjusting the production task list according to the finished production progress.
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| CN202111528053.7A CN114153991A (en) | 2021-12-14 | 2021-12-14 | Knowledge graph method based on intelligent manufacturing scene |
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| CN202111528053.7A CN114153991A (en) | 2021-12-14 | 2021-12-14 | Knowledge graph method based on intelligent manufacturing scene |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20200054065A (en) * | 2018-11-09 | 2020-05-19 | 한국기술교육대학교 산학협력단 | System for smart learning factory |
| CN111930088A (en) * | 2020-08-18 | 2020-11-13 | 苏州麻雀智能科技有限公司 | Edge management system |
| CN112580989A (en) * | 2020-12-23 | 2021-03-30 | 南京绿投科技有限公司 | Cloud platform data management system and management method based on industrial big data |
| CN112966836A (en) * | 2021-02-03 | 2021-06-15 | 天津杰士电池有限公司 | Internet of things-based battery production recovery overall process tracing method and system |
| CN113534760A (en) * | 2021-08-02 | 2021-10-22 | 上海奇梦网络科技有限公司 | Manufacturing industry factory management system based on digital twin platform |
-
2021
- 2021-12-14 CN CN202111528053.7A patent/CN114153991A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20200054065A (en) * | 2018-11-09 | 2020-05-19 | 한국기술교육대학교 산학협력단 | System for smart learning factory |
| CN111930088A (en) * | 2020-08-18 | 2020-11-13 | 苏州麻雀智能科技有限公司 | Edge management system |
| CN112580989A (en) * | 2020-12-23 | 2021-03-30 | 南京绿投科技有限公司 | Cloud platform data management system and management method based on industrial big data |
| CN112966836A (en) * | 2021-02-03 | 2021-06-15 | 天津杰士电池有限公司 | Internet of things-based battery production recovery overall process tracing method and system |
| CN113534760A (en) * | 2021-08-02 | 2021-10-22 | 上海奇梦网络科技有限公司 | Manufacturing industry factory management system based on digital twin platform |
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