CN112859796A - Production process monitoring system - Google Patents
Production process monitoring system Download PDFInfo
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- CN112859796A CN112859796A CN202110277756.0A CN202110277756A CN112859796A CN 112859796 A CN112859796 A CN 112859796A CN 202110277756 A CN202110277756 A CN 202110277756A CN 112859796 A CN112859796 A CN 112859796A
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- 238000004519 manufacturing process Methods 0.000 title claims abstract description 77
- 238000012544 monitoring process Methods 0.000 title claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 40
- 230000002159 abnormal effect Effects 0.000 claims abstract description 28
- 230000007613 environmental effect Effects 0.000 claims abstract description 16
- 239000002994 raw material Substances 0.000 claims abstract description 4
- 238000000034 method Methods 0.000 claims description 6
- 238000007418 data mining Methods 0.000 claims description 4
- 238000007637 random forest analysis Methods 0.000 claims description 3
- WYTGDNHDOZPMIW-RCBQFDQVSA-N alstonine Natural products C1=CC2=C3C=CC=CC3=NC2=C2N1C[C@H]1[C@H](C)OC=C(C(=O)OC)[C@H]1C2 WYTGDNHDOZPMIW-RCBQFDQVSA-N 0.000 claims 1
- 238000004886 process control Methods 0.000 claims 1
- 238000013528 artificial neural network Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005059 dormancy Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/4185—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by the network communication
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31088—Network communication between supervisor and cell, machine group
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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
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
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- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Factory Administration (AREA)
Abstract
The invention relates to the field of production monitoring, in particular to a production process monitoring system, which comprises: the target data acquisition module is used for acquiring working condition parameters of production and processing machinery, working condition parameters of workers and environmental parameters in a production workshop; the abnormal state identification module is used for identifying the working condition parameters of the production and processing machinery, the working condition parameters of workers and the abnormal parameters carried in the environmental parameters in the production workshop and making corresponding early warning according to the identification result; and the production flow registration module is used for realizing automatic registration of the raw material source and the processing flow of the product and generating a processing flow two-dimensional code of each product. The invention can find abnormal or dangerous factors existing in the production process in time and provide guarantee for the safety of the production process.
Description
Technical Field
The invention relates to the field of production monitoring, in particular to a production process monitoring system.
Background
At present, monitoring of an existing production and manufacturing workshop is generally realized simply by depending on a camera, monitoring of a production process needs to be carried out manually, the problems of low management efficiency, complex management process, time and labor waste exist, and abnormal or dangerous factors existing in the production process cannot be found timely.
Disclosure of Invention
In order to solve the technical problems, the invention provides a production process monitoring system which can find abnormal or dangerous factors existing in the production process in time and provide guarantee for the safety of the production process.
In order to achieve the purpose, the invention adopts the technical scheme that:
a production process monitoring system comprising: the target data acquisition module is used for acquiring working condition parameters of production and processing machinery, working condition parameters of workers and environmental parameters in a production workshop;
the abnormal state identification module is used for identifying the working condition parameters of the production and processing machinery, the working condition parameters of workers and the abnormal parameters carried in the environmental parameters in the production workshop and making corresponding early warning according to the identification result;
and the production flow registration module is used for realizing automatic registration of the raw material source and the processing flow of the product and generating a processing flow two-dimensional code of each product.
Further, the working condition parameters of the production processing machine comprise a control command currently executed by each actuating part, a motion track of each actuating part and a working image of the production processing machine.
Furthermore, the working condition parameters of the workers are realized based on a binocular vision sensor and are arranged at each monitoring point through a snake-shaped mechanical arm frame.
Furthermore, environmental parameters in the production workshop are collected based on sensor groups which are pasted and arranged on walls in the production workshop and on the work clothes, a wake-up module is carried in the sensor group on the work clothes, wake-up of the sensor group is realized based on positioning information at the current position of the sensor, when the positioning information fed back by the sensor is positioned in the production workshop, the sensor is woken up, when the positioning information fed back by the sensor leaves the production workshop, the sensor is dormant, and an RFID chip carrying worker information is configured in each sensor.
Furthermore, the abnormal state identification module identifies the abnormal working condition of the working personnel based on the Dssd _ Xconcentration coco model, identifies the control command currently executed by each actuating component, the motion track of each actuating component and the abnormal parameters loaded in the environmental parameters in the production workshop based on the Bi-LSTM + Attention model, and identifies the abnormal working image of the production and processing machinery based on the Dssd _ Xconcentration model.
Further, the abnormal state identification module realizes the selection of an early warning mode according to an identification result based on the random forest.
Further, the production flow registration module realizes automatic registration of each product processing flow based on the data mining module, and generates a processing flow table.
Furthermore, the processing flow comprises a staff number, a production and processing machinery number and a processing control command flow corresponding to the staff number, and each product is pasted with a processing flow two-dimensional code.
The invention has the following beneficial effects:
1) the comprehensive automatic monitoring management of the production process is realized, the management process is simplified, and the management efficiency is improved.
2) The abnormal or dangerous factors existing in the production process can be found in time, and the safety of the production process is guaranteed.
3) Through the design of the sensor structure, the collection of the working track of the working personnel can be realized while the collection of the environmental parameters in the workshop is realized.
4) The automatic registration of the production flow is realized, and the tracing of each product processing flow is realized.
Drawings
Fig. 1 is a system block diagram of a production process monitoring system according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will be more clearly understood, the invention is further described in detail below with reference to examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, an embodiment of the present invention provides a production process monitoring system, which includes a main controller, and a target data acquisition module and a remote server that communicate with the main controller, where the target data acquisition module is used to acquire working condition parameters of a production and processing machine, working condition parameters of a worker, and environmental parameters in a production workshop; the main controller carries:
the abnormal state identification module is used for identifying the working condition parameters of the production and processing machinery, the working condition parameters of workers and the abnormal parameters carried in the environmental parameters in the production workshop and making corresponding early warning according to the identification result;
and the production flow registration module is used for realizing automatic registration of the raw material source and the processing flow of the product and generating a processing flow two-dimensional code of each product.
In this embodiment, the working condition parameters of the production processing machine include a control command currently executed by each actuating member, a motion trajectory of each actuating member, and a working image of the production processing machine. In this embodiment, mining of the control command currently executed by each actuating member is realized based on the data mining module, acquisition of the movement locus of each actuating member is realized based on the three-dimensional attitude sensor mounted on the actuating member, and acquisition of the working image of the production and processing machine is realized based on the binocular vision sensor.
In this embodiment, the working condition parameters of the staff are realized based on a binocular vision sensor and are arranged at each monitoring point through a snake-shaped mechanical arm support. The snake-shaped mechanical arm generates a corresponding moving route according to different processing flows, and automatically transfers to a corresponding monitoring point according to the progress of the processing flows, so that the target image is acquired.
In this embodiment, environmental parameter realizes gathering on pasting the sensor group that sets up on the wall in the workshop and on the work clothes in the workshop based on pasting, carries one in the sensor group on the work clothes and awakens up the module, realizes awaking of sensor group based on the locating information at sensor place now, when the locating information that the sensor fed back is located the workshop, the sensor awakens up, when the locating information that the sensor fed back leaves the workshop in, the sensor dormancy, and all dispose one in every sensor and carry the RFID chip of staff's information. Based on the design of the sensor structure, the collection of the working track of the working personnel can be realized while the collection of the environmental parameters in the workshop is realized.
In this embodiment, the abnormal state identification module identifies the abnormal working condition of the worker based on the Dssd _ Xception coco model, identifies the control command currently executed by each actuating element, the movement track of each actuating element and the abnormal parameter loaded in the environmental parameter in the production workshop based on the Bi-LSTM + Attention model, and identifies the abnormal working image of the production and processing machine based on the Dssd _ Xception model. The Dssd-Xception coco model adopts a Dssd target detection algorithm, a coco data set is used for pre-training an Xception neural network, then a previously prepared data set is used for training the model, various parameters in the deep neural network are finely adjusted, and finally a proper model for realizing the recognition of the abnormal working condition of the working personnel is obtained.
In this embodiment, the abnormal state identification module selects an early warning mode according to an identification result based on a random forest.
In this embodiment, the production process registration module realizes automatic registration of each product processing process based on the data mining module, and generates a processing process table.
In this embodiment, the processing flow includes a staff serial number, a production and processing machinery serial number and a processing control command flow corresponding thereto, and each product is configured with a processing flow two-dimensional code by pasting
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (8)
1. A production process monitoring system, comprising:
the target data acquisition module is used for acquiring working condition parameters of production and processing machinery, working condition parameters of workers and environmental parameters in a production workshop;
the abnormal state identification module is used for identifying the working condition parameters of the production and processing machinery, the working condition parameters of workers and the abnormal parameters carried in the environmental parameters in the production workshop and making corresponding early warning according to the identification result;
and the production flow registration module is used for realizing automatic registration of the raw material source and the processing flow of the product and generating a processing flow two-dimensional code of each product.
2. The production process monitoring system of claim 1, wherein the production process machine operating parameters include a control command currently executed by each actuator, a motion trajectory of each actuator, and a work image of the production process machine.
3. The production process monitoring system of claim 1, wherein the worker operating condition parameters are based on binocular vision sensors and are provided at each monitoring point by a serpentine robotic arm.
4. The system for monitoring the production process of claim 1, wherein the environmental parameters in the production workshop are collected based on sensor groups attached to walls in the production workshop and on the work clothes, a wake-up module is carried in the sensor group on the work clothes, the sensor group is woken up based on the current positioning information of the sensor, when the positioning information fed back by the sensor is located in the production workshop, the sensor wakes up, when the positioning information fed back by the sensor leaves the production workshop, the sensor is dormant, and each sensor is provided with an RFID chip carrying the information of the staff.
5. The system as claimed in claim 1, wherein the abnormal state recognition module recognizes abnormal working conditions of the operator based on Dssd _ Xception coco model, recognizes the control command currently executed by each actuator, the motion trajectory of each actuator, and the abnormal parameters loaded in the environmental parameters in the production plant based on Bi-LSTM + Attention model, and recognizes the abnormal working image of the production processing machine based on Dssd _ Xception model.
6. A production process monitoring system as claimed in claim 1 wherein the abnormal state identification module implements selection of an early warning mode based on random forests based on the identification results.
7. The system for monitoring a manufacturing process of claim 1, wherein said manufacturing process registration module creates the manufacturing process table based on a data mining module that performs an automatic registration of each product manufacturing process.
8. The system as claimed in claim 1, wherein the process flow includes a staff number, a production and processing machine number and a corresponding process control command flow, and each product is configured with a process flow two-dimensional code.
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| CN202110277756.0A CN112859796A (en) | 2021-03-15 | 2021-03-15 | Production process monitoring system |
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| CN202110277756.0A CN112859796A (en) | 2021-03-15 | 2021-03-15 | Production process monitoring system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117273470A (en) * | 2023-11-21 | 2023-12-22 | 张家港欣迈克五金工具有限公司 | Intelligent monitoring and early warning system for production safety of vehicle cargo fixer processing factory |
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| CN110399951A (en) * | 2019-07-26 | 2019-11-01 | 马鞍山市科泰电气科技有限公司 | A kind of monitoring management system and method for plant area staff motion track |
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| CN112437165A (en) * | 2021-01-26 | 2021-03-02 | 江苏博恩环保科技有限公司 | Remote monitoring type real-time monitoring system and method for detection workflow |
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2021
- 2021-03-15 CN CN202110277756.0A patent/CN112859796A/en active Pending
Patent Citations (8)
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| JP2009037306A (en) * | 2007-07-31 | 2009-02-19 | Toshiba Corp | Monitoring method, monitoring program and monitoring system for production process |
| CN106162094A (en) * | 2016-08-05 | 2016-11-23 | 北京丰华联合科技有限公司 | A kind of safety in production intelligent video monitoring system for firework factory |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117273470A (en) * | 2023-11-21 | 2023-12-22 | 张家港欣迈克五金工具有限公司 | Intelligent monitoring and early warning system for production safety of vehicle cargo fixer processing factory |
| CN117273470B (en) * | 2023-11-21 | 2024-01-30 | 张家港欣迈克五金工具有限公司 | Intelligent monitoring and early warning system for production safety of vehicle cargo fixer processing factory |
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Application publication date: 20210528 |