CN109242357B - Process editing method of MES system - Google Patents

Process editing method of MES system Download PDF

Info

Publication number
CN109242357B
CN109242357B CN201811248310.XA CN201811248310A CN109242357B CN 109242357 B CN109242357 B CN 109242357B CN 201811248310 A CN201811248310 A CN 201811248310A CN 109242357 B CN109242357 B CN 109242357B
Authority
CN
China
Prior art keywords
model
setting
training
process model
unique
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.)
Active
Application number
CN201811248310.XA
Other languages
Chinese (zh)
Other versions
CN109242357A (en
Inventor
张昊
张权
杨祝建
邓晨华
杨明明
王晓阳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
GUANGZHOU CH CONTROL TECHNOLOGY CO LTD
Original Assignee
GUANGZHOU CH CONTROL TECHNOLOGY CO LTD
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by GUANGZHOU CH CONTROL TECHNOLOGY CO LTD filed Critical GUANGZHOU CH CONTROL TECHNOLOGY CO LTD
Priority to CN201811248310.XA priority Critical patent/CN109242357B/en
Publication of CN109242357A publication Critical patent/CN109242357A/en
Application granted granted Critical
Publication of CN109242357B publication Critical patent/CN109242357B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06316—Sequencing of tasks or work
    • 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
    • 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 process editing method of an MES system, which comprises the following steps: acquiring all process flows and graphs corresponding to the process flows, and establishing a process model through data processing and feature extraction; and applying the established process model in the MES system, setting association between the process setting area and the graphic area by calling the process model, generating a corresponding graphic, and directly editing the process flow on the MES system. The method of the invention configures the process setting area and the graphic area for each process, sets the front and back related processes for each process by establishing the process model, applying the process model on the MES, configuring the MES, setting the associated index and the like, and the graphic area produces the graphics according to the setting condition of the process setting area.

Description

Process editing method of MES system
Technical Field
The invention relates to the field of production processes, in particular to a process editing method of an MES system.
Background
The workflow technology of the production process of the process enterprise aims to solve the production coordination problem of a plurality of links in the production process of a complex process. Manufacturing Execution System (MES) is an information execution system facing to the inter-vehicle layer between a plan management system on the upper layer of a production type enterprise and an industrial control system on the field layer, and performs production information management and data processing on the whole production process from production order issuing to product completion, including the contents of order data issuing, production instruction data issuing, production process execution monitoring, field production data acquisition, data statistical processing and reporting, and the like, and is the key for realizing production information processing and man-machine interaction in a complex flow production process.
Although the MES system can improve the production efficiency when introduced into modern enterprises, the existing MES system manages fixed process flows, when other process flows need to be managed, the process flows are troublesome to replace, the MES system does not support direct editing of the process flows on the system, and the MES system is basically designed according to functional modules, so that the problems that each business flow link of the MES is isolated to a certain extent, the logical relationship of each business link is unclear and the like are caused, and the MES system is prevented from being further popularized and applied in modern enterprises.
Disclosure of Invention
The invention provides a process editing method of an MES system, which aims at the problems in the prior art, and provides a process editing method of an MES system, wherein a process model is established and applied to the MES system to configure the MES system, set a process setting area and a graph area, set an association index and the like, the process setting area is configured with front and back associated processes for each process, the graph area produces graphs according to the setting condition of the process setting area, and the process flow is directly started through the process model, so that when other process flows need to be managed, the process flow can be directly edited, the working efficiency is higher, the operation is more convenient, and the process execution is more intelligent.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a process editing method of MES system comprises:
s1, acquiring all process flows and graphs corresponding to the process flows, and establishing a process model through data processing and feature extraction;
and S2, applying the established process model in the MES system, setting association between the process setting area and the graphic area by calling the process model, generating a corresponding graphic, and directly editing the process flow on the MES system.
Further, the step S1 specifically includes:
s11, acquiring all process flows and corresponding graphs in the production process, respectively extracting data characteristics of the process flows and the corresponding graphs, and establishing a unique association line;
s12, establishing a procedure sample database, wherein the sample database comprises a training database and a testing database, and procedure flow data and graphic data are respectively stored in the training database and the testing database;
s13, establishing a procedure model, transmitting the data in the training database to the procedure model for repeated training, extracting a feature integration data structure, and stopping training until a training threshold and training accuracy are reached;
and S14, transmitting the data in the test database to the integrated process model, repeatedly testing the process model, and optimizing the process model until reaching the test threshold and the test accuracy.
Further, the step S11 specifically includes:
s111, extracting concept keywords of the process flow and concept keywords of the extracted graph by using a statistical method, and integrating the corresponding process flow concept keywords and the corresponding graph concept keywords into a set for storage;
s112, similarity algorithm calculation is carried out on each set, and every two sets with similarity values of 5% are extracted and fused into one type;
s113, repeating the step S112 until the fusion of all the sets is completed, and forming the similarity level type of the clusters;
and S114, assigning a unique identification code to the formed-grade category, wherein the unique identification code corresponds to the unique process flow and the corresponding graph to form a unique correlation line.
Further, the training threshold in the step S13 is 20 ten thousand times, the training accuracy is 90%, the testing threshold in the step S14 is 20 ten thousand times, and the testing accuracy is 90%.
Further, the statistical method in step S111 adopts a cross-frequency statistical method.
Further, the cross-frequency statistical method adopted in step S111 may be replaced by an information entropy statistical method.
Further, the similarity calculation method in step S112 adopts a cosine similarity formula and a pearson correlation formula.
Further, the step S2 specifically includes:
s21, applying the tested process model to an MES system, and debugging the process model until initialization is successful;
s22, configuring the process setting area and the graphic area on the system, relating the front and back processes;
s23, setting the process flow characteristics in the process model in the process setting area, and setting the image characteristics in the process model in the graphic area;
and S24, arranging the unique associated line corresponding to the process in the cell, establishing an index, starting the cell to fill the connection index, triggering the associated line, and starting the corresponding process flow and the corresponding graph through the unique associated line.
Further, the step S22 specifically includes:
s221, dividing each process, assigning values according to the sequence executed by the process, and associating the processes before and after the process through the assignment;
s222, configuring an access path for a process setting area on the system, and setting the access path of the process setting area in a process model;
and S223, setting a calling path for the graphic area on the system, wherein the calling path is connected with the unique association line, and starting the process model through the unique association line.
A process editing method of MES system comprises:
s1, acquiring all process flows and graphs corresponding to the process flows, and establishing a process model through data processing and feature extraction;
and S2, applying the established process model in the MES system, setting association between the process setting area and the graphic area by calling the process model, generating a corresponding graphic, and directly editing the process flow on the MES system.
The step S1 specifically includes:
s11, acquiring all process flows and corresponding graphs in the production process, respectively extracting data characteristics of the process flows and the corresponding graphs, and establishing a unique association line;
s12, establishing a procedure sample database, wherein the sample database comprises a training database and a testing database, and procedure flow data and graphic data are respectively stored in the training database and the testing database;
s13, establishing a procedure model, transmitting the data in the training database to the procedure model for repeated training, extracting a feature integration data structure, and stopping training until a training threshold and training accuracy are reached;
and S14, transmitting the data in the test database to the integrated process model, repeatedly testing the process model, and optimizing the process model until reaching the test threshold and the test accuracy.
The step S11 specifically includes:
s111, extracting concept keywords of the process flow and concept keywords of the extracted graph by using a statistical method, and integrating the corresponding process flow concept keywords and the corresponding graph concept keywords into a set for storage;
s112, similarity algorithm calculation is carried out on each set, and every two sets with similarity values of 5% are extracted and fused into one type;
s113, repeating the step S112 until the fusion of all the sets is completed, and forming the similarity level type of the clusters;
and S114, assigning a unique identification code to the formed-grade category, wherein the unique identification code corresponds to the unique process flow and the corresponding graph to form a unique correlation line.
The step S2 specifically includes:
s21, applying the tested process model to an MES system, and debugging the process model until initialization is successful;
s22, configuring the process setting area and the graphic area on the system, relating the front and back processes;
s23, setting the process flow characteristics in the process model in the process setting area, and setting the image characteristics in the process model in the graphic area;
and S24, arranging the unique associated line corresponding to the process in the cell, establishing an index, starting the cell to fill the connection index, triggering the associated line, and starting the corresponding process flow and the corresponding graph through the unique associated line.
The step S22 specifically includes:
s221, dividing each process, assigning values according to the sequence executed by the process, and associating the processes before and after the process through the assignment;
s222, configuring an access path for a process setting area on the system, and setting the access path of the process setting area in a process model;
and S223, setting a calling path for the graphic area on the system, wherein the calling path is connected with the unique association line, and starting the process model through the unique association line.
The training threshold in the step S13 is 20 ten thousand times, the training accuracy is 90%, the testing threshold in the step S14 is 20 ten thousand times, and the testing accuracy is 90%.
The statistical method in step S111 adopts a cross-frequency statistical method.
The cross-frequency statistical method adopted in step S111 may be replaced by an information entropy statistical method.
The similarity calculation method in step S112 adopts a cosine similarity formula and a pearson correlation formula.
Compared with the prior art, the invention configures the process related to each process for the process setting area by establishing the process model and applying the process model to the MES system, configures the MES system, sets the process setting area and the graph area, sets the correlation index and the like, configures the front and back correlated processes for each process for the process setting area, produces the graph for the graph area according to the setting condition of the process setting area, and directly starts the process flow through the process model, thereby realizing that the process flow can be directly edited when other process flows need to be managed, ensuring higher working efficiency, more convenient operation and more intelligent process execution.
Drawings
FIG. 1: the invention is a general structure diagram of a process editing method of an MES system;
FIG. 2: is a flow chart of the specific general steps of the procedure editing method;
FIG. 3: a detailed flowchart of step S1 of the process editing method of the present invention;
FIG. 4: a detailed flowchart of step S11 of the process editing method of the present invention;
FIG. 5: a detailed flowchart of step S2 of the process editing method of the present invention;
FIG. 6: a detailed flowchart of step S22 of the process editing method of the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
Referring to fig. 1 and 2, the invention is a process editing method of MES system, including: acquiring all process flows and graphs corresponding to the process flows in production, and establishing a process model through data processing and feature extraction; and applying the established process model in the MES system, setting association between the process setting area and the graphic area by calling the process model, generating a corresponding graphic, and directly editing the process flow on the MES system.
Referring to fig. 3, in an embodiment, establishing a process model includes: acquiring all process flows and corresponding graphs in the production process, respectively extracting data characteristics of the process flows and the corresponding graphs, and establishing a unique association line; establishing a process sample database, wherein the sample database comprises a training database and a testing database, and storing process flow data and graphic data in the training database and the testing database respectively; establishing a process model, transmitting data in the training database to the process model for repeated training, extracting a feature integration data structure, and stopping training until a training threshold of 20 ten thousand times and a training accuracy of 90 percent are reached; and transmitting the data in the test database to the integrated process model, repeatedly testing the process model, and optimizing the process model until the test threshold value of 20 ten thousand times and the test accuracy of 90% are reached.
Referring to fig. 4, in an embodiment, establishing a unique association line includes: extracting concept keywords of the process flow and concept keywords of the extracted graph by using a string frequency statistical method or an information entropy statistical method, and integrating the corresponding process flow concept keywords and the corresponding graph concept keywords into a set for storage; calculating each set by using a cosine similarity algorithm and a Pearson correlation algorithm, and extracting pairwise sets with similarity values of 5% difference and fusing the pairwise sets into a class;
cosine similarity algorithm:
Figure BDA0001841051270000081
pearson correlation algorithm:
Figure BDA0001841051270000082
wherein: A. b is a characteristic numerical value of every two adjacent sets, the similarity between every two sets is obtained through calculation, and every two sets with the difference of 5% are fused into one class; x, Y are feature values of two adjacent sets; calculating to obtain the similarity between every two sets of clusters, and fusing every two sets of clusters with the difference of 5% into one set; and (6) circulating.
Repeating the steps until the fusion of all the sets is completed to form a similarity-level cluster; and giving a unique identification code to the formed grades of the clusters, wherein the unique identification code corresponds to the unique process flow and the corresponding graph to form a unique association line.
Referring to FIG. 5, in an embodiment, the built process model is applied in an MES system, comprising: applying the tested process model to an MES system, and debugging the process model until initialization is successful; configuring a process setting area and a graphic area on a system, and associating the previous and subsequent processes; setting the process flow characteristics in the process model in a process setting area, and setting the image characteristics in the process model in a graphic area; and setting the unique associated line corresponding to the process in the cell, establishing an index, starting the cell to fill the connection index, triggering the associated line, and starting the corresponding process flow and the corresponding graph through the unique associated line.
Referring to fig. 6, in an embodiment, configuring a process setup area and a graphics area on a system includes: dividing each process, assigning values according to the sequence of the process execution, and associating the previous process and the next process through the assignment; configuring an access path for a process setting area on a system, and setting the access path of the process setting area in a process model; and setting a calling path for the graphic area on the system, wherein the calling path is connected with the unique association line, and starting the process model through the unique association line.
The invention provides a process editing method of an MES system, which has the following advantages: by establishing the process model, applying the process model to the MES system, configuring the MES system, setting a process setting area and a graphic area, setting an association index and the like, configuring a front-back association process for each process in the process setting area, producing a graphic in the graphic area according to the setting condition of the process setting area, and directly starting a process flow through the process model, the process flow can be directly edited when other process flows need to be managed, so that the working efficiency is higher, the operation is more convenient, and the execution process is more intelligent.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that are within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A process editing method for MES system, comprising:
s1, acquiring all process flows and graphs corresponding to the process flows, and establishing a process model through data processing and feature extraction;
s2, applying the established process model in an MES system, setting association between the process setting area and the graphic area by calling the process model, generating a corresponding graphic, and realizing direct editing of the process flow on the MES system;
the step S1 specifically includes:
s11, acquiring all process flows and corresponding graphs in the production process, respectively extracting data characteristics of the process flows and the corresponding graphs, and establishing a unique association line;
s12, establishing a procedure sample database, wherein the sample database comprises a training database and a testing database, and procedure flow data and graphic data are respectively stored in the training database and the testing database;
s13, establishing a procedure model, transmitting the data in the training database to the procedure model for repeated training, extracting a feature integration data structure, and stopping training until a training threshold and training accuracy are reached;
s14, transmitting the data in the test database to the integrated process model, repeatedly testing the process model, and optimizing the process model until reaching the test threshold and the test accuracy;
the step S11 specifically includes:
s111, extracting concept keywords of the process flow and concept keywords of the extracted graph by using a statistical method, and integrating the corresponding process flow concept keywords and the corresponding graph concept keywords into a set for storage;
s112, similarity algorithm calculation is carried out on each set, and every two sets with similarity values of 5% are extracted and fused into one type;
s113, repeating the step S112 until the fusion of all the sets is completed, and forming the similarity level type of the clusters;
and S114, assigning a unique identification code to the formed-grade category, wherein the unique identification code corresponds to the unique process flow and the corresponding graph to form a unique correlation line.
2. The process editing method according to claim 1, wherein the training threshold in the step S13 is 20 ten thousand times, the training accuracy is 90%, the testing threshold in the step S14 is 20 ten thousand times, and the testing accuracy is 90%.
3. The process editing method according to claim 1, wherein the statistical method in step S111 is a cross-frequency statistical method or an entropy statistical method.
4. The process editing method according to claim 1, wherein the similarity calculation method in step S112 employs a cosine similarity formula and a pearson correlation formula.
5. The process editing method according to claim 1, wherein the step S2 specifically includes:
s21, applying the tested process model to an MES system, and debugging the process model until initialization is successful;
s22, configuring the process setting area and the graphic area on the system, relating the front and back processes;
s23, setting the process flow characteristics in the process model in the process setting area, and setting the image characteristics in the process model in the graphic area;
and S24, arranging the unique associated line corresponding to the process in the cell, establishing an index, starting the cell to fill the connection index, triggering the associated line, and starting the corresponding process flow and the corresponding graph through the unique associated line.
6. The process editing method according to claim 5, wherein the step S22 specifically includes:
s221, dividing each process, assigning values according to the sequence executed by the process, and associating the processes before and after the process through the assignment;
s222, configuring an access path for a process setting area on the system, and setting the access path of the process setting area in a process model;
and S223, setting a calling path for the graphic area on the system, wherein the calling path is connected with the unique association line, and starting the process model through the unique association line.
7. A process editing method for MES system, comprising:
s1, acquiring all process flows and graphs corresponding to the process flows, and establishing a process model through data processing and feature extraction;
s2, applying the established process model in an MES system, setting association between the process setting area and the graphic area by calling the process model, generating a corresponding graphic, and realizing direct editing of the process flow on the MES system;
the step S1 specifically includes:
s11, acquiring all process flows and corresponding graphs in the production process, respectively extracting data characteristics of the process flows and the corresponding graphs, and establishing a unique association line;
s12, establishing a procedure sample database, wherein the sample database comprises a training database and a testing database, and procedure flow data and graphic data are respectively stored in the training database and the testing database;
s13, establishing a procedure model, transmitting the data in the training database to the procedure model for repeated training, extracting a feature integration data structure, and stopping training until a training threshold and training accuracy are reached;
s14, transmitting the data in the test database to the integrated process model, repeatedly testing the process model, and optimizing the process model until reaching the test threshold and the test accuracy;
the step S11 specifically includes:
s111, extracting concept keywords of the process flow and concept keywords of the extracted graph by using a statistical method, and integrating the corresponding process flow concept keywords and the corresponding graph concept keywords into a set for storage;
s112, similarity algorithm calculation is carried out on each set, and every two sets with similarity values of 5% are extracted and fused into one type;
s113, repeating the step S112 until the fusion of all the sets is completed, and forming the similarity level type of the clusters;
s114, giving a unique identification code to the formed-grade category, wherein the unique identification code corresponds to a unique process flow and a corresponding graph to form a unique association line;
the step S2 specifically includes:
s21, applying the tested process model to an MES system, and debugging the process model until initialization is successful;
s22, configuring the process setting area and the graphic area on the system, relating the front and back processes;
s23, setting the process flow characteristics in the process model in the process setting area, and setting the image characteristics in the process model in the graphic area;
s24, arranging the unique association line corresponding to the process in the cell, establishing an index, starting the cell to fill the connection index, thereby triggering the association line, and starting the corresponding process flow and the corresponding graph through the unique association line;
the step S22 specifically includes:
s221, dividing each process, assigning values according to the sequence executed by the process, and associating the processes before and after the process through the assignment;
s222, configuring an access path for a process setting area on the system, and setting the access path of the process setting area in a process model;
s223, setting a calling path for the graphic area on the system, wherein the calling path is connected with the unique association line and starts the process model through the unique association line;
the training threshold in the step S13 is 20 ten thousand times, the training accuracy is 90%, the testing threshold in the step S14 is 20 ten thousand times, and the testing accuracy is 90%;
the statistical method in the step S111 adopts a cross-frequency statistical method or an information entropy statistical method;
the similarity calculation method in step S112 adopts a cosine similarity formula and a pearson correlation formula.
CN201811248310.XA 2018-10-25 2018-10-25 Process editing method of MES system Active CN109242357B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811248310.XA CN109242357B (en) 2018-10-25 2018-10-25 Process editing method of MES system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811248310.XA CN109242357B (en) 2018-10-25 2018-10-25 Process editing method of MES system

Publications (2)

Publication Number Publication Date
CN109242357A CN109242357A (en) 2019-01-18
CN109242357B true CN109242357B (en) 2021-05-11

Family

ID=65081916

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811248310.XA Active CN109242357B (en) 2018-10-25 2018-10-25 Process editing method of MES system

Country Status (1)

Country Link
CN (1) CN109242357B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112036693B (en) * 2020-07-28 2022-04-08 成都飞机工业（集团）有限责任公司 Process sequencing method based on finite weighting process fault rate

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101266486A (en) * 2008-05-08 2008-09-17 上海交通大学 Semiconductor manufacture flow path assistant decision-making system
CN103034134A (en) * 2011-09-29 2013-04-10 西门子公司 Method for setting an operating status
CN104808629A (en) * 2015-04-20 2015-07-29 广东工业大学 Flexible robot manufacturing unit scheduling method
CN204680057U (en) * 2015-05-27 2015-09-30 深圳市深科特信息技术有限公司 MES lean production manufacturing execution system
CN105184013A (en) * 2015-09-28 2015-12-23 武汉开目信息技术有限责任公司 Process model positive-sequence generating method and device based on cut body
CN106960088A (en) * 2017-03-13 2017-07-18 西北工业大学 The three-dimensional process model automatic update method changed towards process
CN108090303A (en) * 2018-01-05 2018-05-29 江苏科技大学 A kind of three-dimensional process for machining information reuse method towards marine diesel important Parts

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP2237197A1 (en) * 2009-03-31 2010-10-06 Siemens Aktiengesellschaft Method for evaluating key production indicators (KPI) in a manufacturing execution system (MES)

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101266486A (en) * 2008-05-08 2008-09-17 上海交通大学 Semiconductor manufacture flow path assistant decision-making system
CN103034134A (en) * 2011-09-29 2013-04-10 西门子公司 Method for setting an operating status
CN104808629A (en) * 2015-04-20 2015-07-29 广东工业大学 Flexible robot manufacturing unit scheduling method
CN204680057U (en) * 2015-05-27 2015-09-30 深圳市深科特信息技术有限公司 MES lean production manufacturing execution system
CN105184013A (en) * 2015-09-28 2015-12-23 武汉开目信息技术有限责任公司 Process model positive-sequence generating method and device based on cut body
CN106960088A (en) * 2017-03-13 2017-07-18 西北工业大学 The three-dimensional process model automatic update method changed towards process
CN108090303A (en) * 2018-01-05 2018-05-29 江苏科技大学 A kind of three-dimensional process for machining information reuse method towards marine diesel important Parts

Also Published As

Publication number Publication date
CN109242357A (en) 2019-01-18

Similar Documents

Publication Publication Date Title
CN109861844B (en) Cloud service problem fine-grained intelligent tracing method based on logs
CN102117306B (en) Method and system for monitoring ETL (extract-transform-load) data processing process
CN109582793A (en) Model training method, customer service system and data labeling system, readable storage medium storing program for executing
CN110659173A (en) Operation and maintenance system and method
CN108512689A (en) Micro services business monitoring method and server
CN105373472B (en) A kind of method of testing and test system of the statistical accuracy based on database
CN108345670B (en) Service hotspot discovery method for 95598 power work order
CN106844190A (en) test script automatic generation method and device
CN105868311A (en) Data analyzing method and device
CN116127899B (en) Chip design system, method, electronic device, and storage medium
CN109669975B (en) Industrial big data processing system and method
CN105574032A (en) Rule matching operation method and device
CN109242357B (en) Process editing method of MES system
CN114868092A (en) Data management platform, intelligent defect analysis system, intelligent defect analysis method, computer program product and method for defect analysis
Lyu et al. A data-driven approach for identifying possible manufacturing processes and production parameters that cause product defects: A thin-film filter company case study
CN109508953B (en) Form template generation method
CN109522536B (en) Automatic form filling method
CN102054047B (en) Extracting method for service-configurable service rule
CN108959574A (en) A kind of test data analysis method and system
CN109376159B (en) Batch record automatic generation method
CN115733780A (en) Dynamic self-adaptive method, system, equipment and medium based on flexible Ethernet
CN109492209B (en) Automatic generation method of form signature column
CN114219259A (en) Process route generation method, management system and medium
CN109446182B (en) Composite acquisition method
CN112860899A (en) Label generation method and device, computer equipment and computer readable storage medium

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
GR01 Patent grant
GR01 Patent grant
PE01 Entry into force of the registration of the contract for pledge of patent right
PE01 Entry into force of the registration of the contract for pledge of patent right

Denomination of invention: A method of process editing in MES system

Effective date of registration: 20220518

Granted publication date: 20210511

Pledgee: Bank of China Limited Guangzhou Yuexiu Branch

Pledgor: GUANGZHOU CH CONTROL TECHNOLOGY Co.,Ltd.

Registration number: Y2022980005858