CN111695880A - Production process monitoring method and system - Google Patents
Production process monitoring method and system Download PDFInfo
- Publication number
- CN111695880A CN111695880A CN202010552447.5A CN202010552447A CN111695880A CN 111695880 A CN111695880 A CN 111695880A CN 202010552447 A CN202010552447 A CN 202010552447A CN 111695880 A CN111695880 A CN 111695880A
- Authority
- CN
- China
- Prior art keywords
- production process
- target
- node
- monitoring
- behavior information
- 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.)
- Granted
Links
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 236
- 238000000034 method Methods 0.000 title claims abstract description 145
- 238000012544 monitoring process Methods 0.000 title claims abstract description 123
- 239000011159 matrix material Substances 0.000 claims abstract description 39
- 238000000605 extraction Methods 0.000 claims description 23
- 230000004931 aggregating effect Effects 0.000 claims description 17
- 238000005070 sampling Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 8
- 238000004590 computer program Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000013500 data storage Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000014759 maintenance of location Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 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
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- 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
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Data Mining & Analysis (AREA)
- Strategic Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- General Factory Administration (AREA)
Abstract
The embodiment of the invention provides a production process monitoring method and a production process monitoring system, which are characterized in that a process node production line graph corresponding to a production process sequence where a target production process is located is obtained, monitoring behavior information of an associated process section corresponding to a target production process section is extracted from the process node production line graph, the extracted monitoring behavior information is aggregated to generate matrix characteristic information corresponding to the target production process section, the target production process is classified according to the matrix characteristic information, and a monitoring passing result of the target production process is determined according to an obtained classification result. Thus, the accuracy of the monitoring passing result of the target production process can be improved.
Description
Technical Field
The invention relates to the technical field of information monitoring, in particular to a production process monitoring method and system.
Background
In the conventional technology, the production process is usually monitored and identified by a manual observation mode, which is limited by manual experience, and the accuracy of the obtained monitoring and identifying result is not high.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a method and a system for monitoring a production process, which can improve the accuracy of a monitoring passing result of a target production process.
According to an aspect of an embodiment of the present invention, there is provided a production process monitoring method applied to a server, the method including:
acquiring a process node production line graph corresponding to a production process sequence of a target production process; the process node production line graph is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section; the target production process section is a process section corresponding to the target production process;
and classifying the target production flow according to the matrix characteristic information, and determining a monitoring passing result of the target production flow according to the obtained classification result.
Optionally, before the obtaining of the process node production line graph corresponding to the production process sequence in which the target production process is located, the method further includes:
acquiring a process configuration parameter sequence corresponding to the production process sequence within a preset time period, and acquiring a plurality of production process sequences according to the process configuration parameter sequence;
determining a production flow in a plurality of production process sequences as a flow section of the flow node production line graph;
and according to the arrangement relation among the production processes in the production process sequences, constructing the association modes among the process sections, and determining the monitoring-oriented service of each association mode to obtain the process node production line graph.
Optionally, after the constructing association manners between the process segments according to the arrangement relationship among the production processes in the plurality of production process sequences and determining the monitoring oriented service of each association manner to obtain the process node production line graph, the method further includes:
for each association mode, counting the target feedback times of the source production process corresponding to the association mode feeding back to the target production process corresponding to the association mode according to a plurality of production process sequences;
counting the total feedback times of the source production process corresponding to the correlation mode to other production processes according to the plurality of production process sequences;
and determining the weight of the association mode according to the target feedback times and the total feedback times.
Optionally, before the extracting monitoring behavior information of an associated process segment corresponding to a target production process segment from the process node production line graph, aggregating the extracted monitoring behavior information, and generating matrix feature information corresponding to the target production process segment, the method further includes:
acquiring a target process monitoring script; the extracting of the monitoring behavior information of the associated process segment corresponding to the target production process segment from the process node production line graph, and the aggregating of the extracted monitoring behavior information to generate the matrix characteristic information corresponding to the target production process segment include:
inputting the process node production line graph into a monitoring behavior information extraction node of the target process monitoring script, extracting monitoring behavior information of a relevant process section corresponding to a target production process section through the monitoring behavior information extraction node, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section;
the classifying the target production process according to the matrix characteristic information and determining a monitoring passing result of the target production process according to the obtained classification result comprise:
and inputting the matrix characteristic information into the classification nodes of the target process monitoring script to obtain a classification result.
Optionally, the monitoring behavior information extraction node includes a plurality of network nodes; the step of extracting the monitoring behavior information of the associated process segment corresponding to the target production process segment through the monitoring behavior information extraction node, aggregating the extracted monitoring behavior information, and generating the matrix characteristic information corresponding to the target production process segment includes:
sequentially sampling candidate associated process sections corresponding to each network node in the process node production line graph from the top node of the monitoring behavior information extraction node to the bottom node of the monitoring behavior information extraction node to obtain a sub-graph corresponding to the process node production line graph;
and sequentially extracting the monitoring behavior information of the target associated flow section corresponding to each network node in the subgraph from the bottom node of the monitoring behavior information extraction node, and aggregating the monitoring behavior information to update the monitoring behavior information of the corresponding previous-stage associated flow section until the previous-stage associated flow section is the target production flow section, thereby generating the matrix characteristic information corresponding to the target production flow section.
According to another aspect of the embodiments of the present invention, there is provided a production process monitoring system, which is applied to a server, the system including:
the acquisition module is used for acquiring a process node production line graph corresponding to a production process sequence in which the target production process is located; the process node production line graph is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
the extraction module is used for extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, aggregating the extracted monitoring behavior information and generating matrix characteristic information corresponding to the target production process section; the target production process section is a process section corresponding to the target production process;
and the classification module is used for classifying the target production process according to the matrix characteristic information and determining a monitoring passing result of the target production process according to the obtained classification result.
According to another aspect of the embodiments of the present invention, a readable storage medium is provided, and the readable storage medium stores a computer program, and the computer program can be executed by a processor to perform the steps of the production flow monitoring method.
Compared with the prior art, the production process monitoring method and the system provided by the embodiment of the invention have the advantages that the process node production line graph corresponding to the production process sequence of the target production process is obtained, the monitoring behavior information of the associated process section corresponding to the target production process section is extracted from the process node production line graph, the extracted monitoring behavior information is aggregated, the matrix characteristic information corresponding to the target production process section is generated, the target production process is classified according to the matrix characteristic information, and the monitoring passing result of the target production process is determined according to the obtained classification result. Thus, the accuracy of the monitoring passing result of the target production process can be improved.
In order to make the aforementioned objects, monitoring behavior information, and advantages of the embodiments of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 illustrates a component schematic diagram of an electronic device provided by an embodiment of the invention;
FIG. 2 is a flow chart of a method for monitoring a production process according to an embodiment of the present invention;
fig. 3 shows a functional block diagram of a production process monitoring system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component schematic of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage media 106 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of server 100. In one case, when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media, the server 100 may perform any of the operations of the associated instructions. The server 100 further comprises one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114)). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the above-described components together.
The communication unit 122 may be implemented in any manner, such as over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, and so forth, governed by any protocol or combination of protocols.
Fig. 2 is a flow chart illustrating a production process monitoring method according to an embodiment of the present invention, which can be executed by the server 100 shown in fig. 1, and the detailed steps of the production process monitoring method are described as follows.
Step S110, a process node production line graph corresponding to a production process sequence where the target production process is located is obtained. The process node production line graph is obtained according to a production process sequence corresponding to the production process sequence in a preset time period.
Step S120, monitoring behavior information of the associated process section corresponding to the target production process section is extracted from the process node production line graph, the extracted monitoring behavior information is aggregated, and matrix characteristic information corresponding to the target production process section is generated. The target production process section is a process section corresponding to the target production process.
Step S130, classifying the target production process according to the matrix characteristic information, and determining a monitoring passing result of the target production process according to the obtained classification result.
Based on the above steps, in this embodiment, a process node production line graph corresponding to a production process sequence in which the target production process is located is obtained, then monitoring behavior information of an associated process segment corresponding to the target production process segment is extracted from the process node production line graph, the extracted monitoring behavior information is aggregated, matrix feature information corresponding to the target production process segment is generated, then the target production process is classified according to the matrix feature information, and a monitoring passing result of the target production process is determined according to an obtained classification result. Thus, the accuracy of the monitoring passing result of the target production process can be improved.
Optionally, before step S110, in this embodiment, a process configuration parameter sequence corresponding to the production process sequence in a preset time period may be further obtained, a plurality of production process sequences are obtained according to the process configuration parameter sequence, and then a production process in the plurality of production process sequences is determined as a process segment of the process node production line graph. Therefore, according to the arrangement relation among the production processes in the production process sequences, the association modes among the process sections are constructed, the monitoring-oriented service of each association mode is determined, and the process node production line graph is obtained.
Optionally, after the association manner between the process segments is constructed and the monitoring-oriented service of each association manner is determined according to the arrangement relationship between the production processes in the plurality of production process sequences, and the process node production line graph is obtained, for each association manner, the number of target feedback times that the source production process corresponding to the association manner feeds back to the target production process corresponding to the association manner may be counted according to the plurality of production process sequences, and then the total number of feedback times that the source production process corresponding to the association manner feeds back to other production processes may be counted according to the plurality of production process sequences, so that the weight of the association manner may be determined according to the target feedback times and the total number of feedback times.
Optionally, before the monitoring behavior information of the associated process segment corresponding to the target production process segment is extracted from the process node production line graph, the extracted monitoring behavior information is aggregated, and the matrix characteristic information corresponding to the target production process segment is generated, a target process monitoring script may also be obtained. Therefore, in the process of extracting the monitoring behavior information of the associated process section corresponding to the target production process section from the process node production line graph, aggregating the extracted monitoring behavior information and generating the matrix characteristic information corresponding to the target production process section, the process node production line graph can be input into the monitoring behavior information extraction node of the target process monitoring script, the monitoring behavior information of the associated process section corresponding to the target production process section is extracted through the monitoring behavior information extraction node, and the extracted monitoring behavior information is aggregated to generate the matrix characteristic information corresponding to the target production process section.
Meanwhile, in the process of classifying the target production process according to the matrix characteristic information and determining the monitoring passing result of the target production process according to the obtained classification result, the matrix characteristic information can be input into the classification nodes of the target process monitoring script to obtain the classification result.
Optionally, the monitoring behavior information extraction node includes a plurality of network nodes. In the process of extracting the monitoring behavior information of the associated process segment corresponding to the target production process segment through the monitoring behavior information extraction node, aggregating the extracted monitoring behavior information, and generating the matrix characteristic information corresponding to the target production process segment, the candidate associated process segments corresponding to the network nodes in the process node production line graph can be sequentially sampled from the top node of the monitoring behavior information extraction node to the bottom node of the monitoring behavior information extraction node, so as to obtain the sub-graph corresponding to the process node production line graph. And then, sequentially extracting the monitoring behavior information of the target associated flow section corresponding to each network node in the subgraph from the bottom node of the monitoring behavior information extraction node, and aggregating the monitoring behavior information to update the monitoring behavior information of the corresponding previous-stage associated flow section until the previous-stage associated flow section is the target production flow section, so as to generate the matrix characteristic information corresponding to the target production flow section.
Fig. 3 is a functional block diagram of a production process monitoring system 200 according to an embodiment of the present invention, where the functions implemented by the production process monitoring system 200 may correspond to the steps executed by the method. The production process monitoring system 200 may be understood as the server 100, or a processor of the server 100, or may be understood as a component that is independent from the server 100 or the processor and implements the functions of the present invention under the control of the server 100, as shown in fig. 3, the production process monitoring system 200 may include an obtaining module 210, an extracting module 220, and a classifying module 230, and the functions of the functional modules of the production process monitoring system 200 are described in detail below.
An obtaining module 210, configured to obtain a process node production line diagram corresponding to a production process sequence in which a target production process is located; the process node production line graph is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
an extracting module 220, configured to extract monitoring behavior information of an associated process segment corresponding to a target production process segment from the process node production line graph, aggregate the extracted monitoring behavior information, and generate matrix feature information corresponding to the target production process segment; the target production process section is a process section corresponding to the target production process;
the classifying module 230 is configured to classify the target production process according to the matrix feature information, and determine a monitoring passing result of the target production process according to an obtained classification result.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential monitoring behavior information of the present invention. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A production flow monitoring method is applied to a server, and the method comprises the following steps:
acquiring a process node production line graph corresponding to a production process sequence of a target production process; the process node production line graph is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section; the target production process section is a process section corresponding to the target production process;
and classifying the target production flow according to the matrix characteristic information, and determining a monitoring passing result of the target production flow according to the obtained classification result.
2. The method of claim 1, wherein before the obtaining the process node production line map corresponding to the production process sequence in which the target production process is located, the method further comprises:
acquiring a process configuration parameter sequence corresponding to the production process sequence within a preset time period, and acquiring a plurality of production process sequences according to the process configuration parameter sequence;
determining a production flow in a plurality of production process sequences as a flow section of the flow node production line graph;
and according to the arrangement relation among the production processes in the production process sequences, constructing the association modes among the process sections, and determining the monitoring-oriented service of each association mode to obtain the process node production line graph.
3. The method according to claim 2, wherein after the association between the process segments is constructed and the monitoring-oriented service of each association is determined according to the arrangement relationship between the production processes in the plurality of production process sequences, so as to obtain the process node production line graph, the method further comprises:
for each association mode, counting the target feedback times of the source production process corresponding to the association mode feeding back to the target production process corresponding to the association mode according to a plurality of production process sequences;
counting the total feedback times of the source production process corresponding to the correlation mode to other production processes according to the plurality of production process sequences;
and determining the weight of the association mode according to the target feedback times and the total feedback times.
4. The method of claim 1, wherein before the extracting monitoring behavior information of an associated process flow segment corresponding to a target production process flow segment from the process node production line graph, aggregating the extracted monitoring behavior information, and generating matrix characteristic information corresponding to the target production process flow segment, the method further comprises:
acquiring a target process monitoring script; the extracting of the monitoring behavior information of the associated process segment corresponding to the target production process segment from the process node production line graph, and the aggregating of the extracted monitoring behavior information to generate the matrix characteristic information corresponding to the target production process segment include:
inputting the process node production line graph into a monitoring behavior information extraction node of the target process monitoring script, extracting monitoring behavior information of a relevant process section corresponding to a target production process section through the monitoring behavior information extraction node, and aggregating the extracted monitoring behavior information to generate matrix characteristic information corresponding to the target production process section;
the classifying the target production process according to the matrix characteristic information and determining a monitoring passing result of the target production process according to the obtained classification result comprise:
and inputting the matrix characteristic information into the classification nodes of the target process monitoring script to obtain a classification result.
5. The method of claim 4, wherein the monitoring behavior information extraction node comprises a plurality of network nodes; the step of extracting the monitoring behavior information of the associated process segment corresponding to the target production process segment through the monitoring behavior information extraction node, aggregating the extracted monitoring behavior information, and generating the matrix characteristic information corresponding to the target production process segment includes:
sequentially sampling candidate associated process sections corresponding to each network node in the process node production line graph from the top node of the monitoring behavior information extraction node to the bottom node of the monitoring behavior information extraction node to obtain a sub-graph corresponding to the process node production line graph;
and sequentially extracting the monitoring behavior information of the target associated flow section corresponding to each network node in the subgraph from the bottom node of the monitoring behavior information extraction node, and aggregating the monitoring behavior information to update the monitoring behavior information of the corresponding previous-stage associated flow section until the previous-stage associated flow section is the target production flow section, thereby generating the matrix characteristic information corresponding to the target production flow section.
6. A production process monitoring system, applied to a server, the system comprising:
the acquisition module is used for acquiring a process node production line graph corresponding to a production process sequence in which the target production process is located; the process node production line graph is obtained according to a production process sequence corresponding to the production process sequence in a preset time period;
the extraction module is used for extracting monitoring behavior information of an associated process section corresponding to a target production process section from the process node production line graph, aggregating the extracted monitoring behavior information and generating matrix characteristic information corresponding to the target production process section; the target production process section is a process section corresponding to the target production process;
and the classification module is used for classifying the target production process according to the matrix characteristic information and determining a monitoring passing result of the target production process according to the obtained classification result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010552447.5A CN111695880B (en) | 2020-06-17 | 2020-06-17 | Production flow monitoring method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010552447.5A CN111695880B (en) | 2020-06-17 | 2020-06-17 | Production flow monitoring method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111695880A true CN111695880A (en) | 2020-09-22 |
| CN111695880B CN111695880B (en) | 2024-01-12 |
Family
ID=72481430
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010552447.5A Active CN111695880B (en) | 2020-06-17 | 2020-06-17 | Production flow monitoring method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111695880B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115600826A (en) * | 2022-12-14 | 2023-01-13 | 中建科技集团有限公司（Cn） | Production process monitoring and optimizing method based on reinforcement learning |
| CN117422585A (en) * | 2023-12-15 | 2024-01-19 | 中山市三乐电子有限公司 | State monitoring method and system of inductance production and manufacturing control system |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU4111496A (en) * | 1994-12-13 | 1996-07-03 | Arnott's Biscuits Limited | Data recognition system |
| AU2001277701A1 (en) * | 2000-08-02 | 2002-02-18 | Japan Tobacco Inc. | Marker for detecting plant genome polymorphism with the use of transposable element and method of constructing the same |
| CN101582824A (en) * | 2008-05-13 | 2009-11-18 | 施耐德电器工业公司 | Node number automatic distribution method for controlling secondary node equipment of local area network bus |
| CN102841586A (en) * | 2012-09-04 | 2012-12-26 | 昆山铁牛衬衫厂 | Monitoring system for spinning production |
| CN102946506A (en) * | 2012-10-22 | 2013-02-27 | 上海东方传媒集团有限公司 | Globalized monitoring system in fields of radio and television broadcasting |
| CN105893726A (en) * | 2014-11-21 | 2016-08-24 | 张琳 | Monitoring method of feature signal monitoring point |
| CN106557457A (en) * | 2016-11-28 | 2017-04-05 | 中国电子科技集团公司第二十八研究所 | A kind of system for automatically generating cross-platform Complicated Flow figure based on QT |
| CN107145963A (en) * | 2017-04-06 | 2017-09-08 | 浙江大学 | A kind of power distribution network method for searching path based on depth-first |
| CN107247653A (en) * | 2017-06-02 | 2017-10-13 | 郑州云海信息技术有限公司 | A kind of Fault Classification and device of data center's monitoring system |
| CN107756250A (en) * | 2017-11-08 | 2018-03-06 | 山东理工大学 | A kind of grinding power and energy consumption intelligent monitor system and decision-making technique |
| CN108038651A (en) * | 2017-12-29 | 2018-05-15 | 西华大学 | A kind of monitoring logistics transportation system for tracing and managing |
| CN110673577A (en) * | 2019-09-23 | 2020-01-10 | 北京化工大学 | Distributed monitoring and fault diagnosis method for complex chemical production process |
| EP3629247A1 (en) * | 2018-09-28 | 2020-04-01 | Siemens Healthcare GmbH | Method for adapting a connection weight and connection weight adaptation unit |
-
2020
- 2020-06-17 CN CN202010552447.5A patent/CN111695880B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU4111496A (en) * | 1994-12-13 | 1996-07-03 | Arnott's Biscuits Limited | Data recognition system |
| AU2001277701A1 (en) * | 2000-08-02 | 2002-02-18 | Japan Tobacco Inc. | Marker for detecting plant genome polymorphism with the use of transposable element and method of constructing the same |
| CN101582824A (en) * | 2008-05-13 | 2009-11-18 | 施耐德电器工业公司 | Node number automatic distribution method for controlling secondary node equipment of local area network bus |
| CN102841586A (en) * | 2012-09-04 | 2012-12-26 | 昆山铁牛衬衫厂 | Monitoring system for spinning production |
| CN102946506A (en) * | 2012-10-22 | 2013-02-27 | 上海东方传媒集团有限公司 | Globalized monitoring system in fields of radio and television broadcasting |
| CN105893726A (en) * | 2014-11-21 | 2016-08-24 | 张琳 | Monitoring method of feature signal monitoring point |
| CN106557457A (en) * | 2016-11-28 | 2017-04-05 | 中国电子科技集团公司第二十八研究所 | A kind of system for automatically generating cross-platform Complicated Flow figure based on QT |
| CN107145963A (en) * | 2017-04-06 | 2017-09-08 | 浙江大学 | A kind of power distribution network method for searching path based on depth-first |
| CN107247653A (en) * | 2017-06-02 | 2017-10-13 | 郑州云海信息技术有限公司 | A kind of Fault Classification and device of data center's monitoring system |
| CN107756250A (en) * | 2017-11-08 | 2018-03-06 | 山东理工大学 | A kind of grinding power and energy consumption intelligent monitor system and decision-making technique |
| CN108038651A (en) * | 2017-12-29 | 2018-05-15 | 西华大学 | A kind of monitoring logistics transportation system for tracing and managing |
| EP3629247A1 (en) * | 2018-09-28 | 2020-04-01 | Siemens Healthcare GmbH | Method for adapting a connection weight and connection weight adaptation unit |
| CN110673577A (en) * | 2019-09-23 | 2020-01-10 | 北京化工大学 | Distributed monitoring and fault diagnosis method for complex chemical production process |
Non-Patent Citations (2)
| Title |
|---|
| 王楠: ""基于实时状态信息的混流装配生产优化与仿真技术研究"", 《工程科技Ⅱ辑》, vol. 1, pages 23 - 137 * |
| 谌微微等: "《烟草商业企业卷烟物流成本控制与优化》", vol. 1, 西南交通大学出版社, pages: 23 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115600826A (en) * | 2022-12-14 | 2023-01-13 | 中建科技集团有限公司（Cn） | Production process monitoring and optimizing method based on reinforcement learning |
| CN117422585A (en) * | 2023-12-15 | 2024-01-19 | 中山市三乐电子有限公司 | State monitoring method and system of inductance production and manufacturing control system |
| CN117422585B (en) * | 2023-12-15 | 2024-05-17 | 中山市三乐电子有限公司 | State monitoring method and system of inductance production and manufacturing control system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111695880B (en) | 2024-01-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111695880B (en) | Production flow monitoring method and system | |
| CN113779409A (en) | Intention mining method and system based on big data | |
| CN113222554A (en) | Intelligent office cooperation method and system based on big data | |
| CN111948967B (en) | Power data acquisition method and system based on big data | |
| CN113282312A (en) | Software package updating method and system | |
| CN111899060A (en) | Advertisement management method and system based on big data | |
| CN111339160A (en) | Scientific and technological achievement data mining method and system | |
| CN113282482A (en) | Compatibility test method and system for software package | |
| CN111767437B (en) | Enterprise science and technology project management method and system | |
| CN113901100A (en) | Member information management method and device | |
| CN111338275B (en) | Method and system for monitoring running state of electrical equipment | |
| CN112035327A (en) | Palm application management method and device | |
| CN113253261B (en) | Information early warning method and system based on radar camera | |
| CN111308986A (en) | Electrical equipment switch management method and system | |
| CN113901304A (en) | Virtual client information processing method and system | |
| CN112948598A (en) | Pattern learning information embedding method and system | |
| CN114938336A (en) | Method and system for classifying network operation information | |
| CN113282739A (en) | Information extraction and pushing method and system based on artificial intelligence | |
| CN114385448A (en) | Sampler operation test method and system | |
| CN113206818A (en) | Cloud server safety protection method and system | |
| CN113282790A (en) | Video feature extraction method and system based on artificial intelligence | |
| CN113901487A (en) | Digital information encryption method and system based on cloud computing service | |
| CN113888149A (en) | Intelligent payment information uploading method and system based on block chain service | |
| CN113449762A (en) | Smart city Internet of things fire-fighting remote monitoring method and system | |
| CN113099585A (en) | Light dimming control method and system |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20231031 Address after: 215500 No.6-1, research institute road, Changshu Economic and Technological Development Zone, Suzhou City, Jiangsu Province Applicant after: Suzhou Zhiyun Chuangyu Information Technology Co.,Ltd. Address before: 215513 Xu Wu highway, Wu City, Bixi New District, Changshu City, Suzhou City, Jiangsu Province Applicant before: Changshu Hantai Chemical Fiber Weaving Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |