CN108549341B - Workshop Production approaches to IM, system and device based on Internet of Things - Google Patents
Workshop Production approaches to IM, system and device based on Internet of Things Download PDFInfo
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- CN108549341B CN108549341B CN201810355341.9A CN201810355341A CN108549341B CN 108549341 B CN108549341 B CN 108549341B CN 201810355341 A CN201810355341 A CN 201810355341A CN 108549341 B CN108549341 B CN 108549341B
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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], computer integrated manufacturing [CIM]
- G05B19/41875—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], computer integrated manufacturing [CIM] characterised by quality surveillance of production
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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
- G05B2219/32—Operator till task planning
- G05B2219/32368—Quality control
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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]
Abstract
The invention discloses Workshop Production approaches to IM, system and device based on Internet of Things, method includes: to be acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；Edge calculations are carried out to collection result；Data correlation is carried out to the result of edge calculations and generates statistical report form；Data analysis is carried out to statistical report form based on deep learning algorithm；According to data analysis as a result, remotely being controlled workshop appliance；System includes acquisition module, edge calculations module, relating module, data analysis module and remote control module；Device includes memory and processor.The present invention directly can carry out remote manual control or remote auto control to workshop appliance, control mode is flexible, without carrying out manual analysis by expert engineer, improves work efficiency, cost of labor and strong real-time are reduced, can be widely applied to internet of things field.
Description
Technical field
The present invention relates to internet of things field, are based especially on Workshop Production approaches to IM, system and the dress of Internet of Things
It sets.
Background technique
In actual production, there are manufacturing schedule delay, finished product fraction defectives to remain high, production material waste is serious for enterprise
The problems such as, these problems seriously constrain the development of enterprise.Production information management manufactures most crucial, most important ring as factory
Section, directly affects quality, quantity, price and the friendship phase of enterprise product, goes back indirect relation to customer satisfaction.
Manufacturing enterprise is faced with increasingly fierce international competition, and enterprise starts increasingly focus on how fast responding market becomes
Change, and is met customer need by steadily improving service.And traditional mode of production field management, to real-time output capacity, online
The production scenes such as yield fluctuation, stable technical process poor information effective monitoring tool, problem can only be real in post
When property is not strong, this has been unable to satisfy Competitive Needs complicated and changeable today.The prior art cooperates sensor to obtain by video camera
Each facility information between pick-up, but there is still a need for expert engineers in backstage progress data analysis, low efficiency and high labor cost.
Summary of the invention
In order to solve the above technical problems, it is an object of the invention to: provide that a kind of high-efficient, cost of labor is low and real-time
Strong, Workshop Production approaches to IM, system and device based on Internet of Things.
First technical solution adopted by the present invention is:
Workshop Production approaches to IM based on Internet of Things, comprising the following steps:
It is acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；
Edge calculations are carried out to collection result, the edge calculations include data aggregate, data denaturation, data filtering, number
According to erasing and abnormality detection；
Data correlation is carried out to the result of edge calculations and generates statistical report form；
Data analysis is carried out to statistical report form based on deep learning algorithm；
According to data analysis as a result, remotely controlled workshop appliance, the long-range control include manually control with
It automatically controls.
Further, described the step for collection result is carried out abnormality detection, comprising the following steps:
Several sample datas in collection result are extracted to construct quaternary tree；
According to the quaternary tree of building, the abnormal score of each data in collection result is calculated；
According to the abnormal score being calculated, the abnormal data in collection result is marked.
Further, described several sample datas extracted in collection result are come the step for constructing quaternary tree, including with
Lower step:
Several sample datas are extracted from collection result；
One is randomly selected from several sample datas as start node；
The first median of several sample datas is chosen as the first separation；
The second median is chosen from the sample data less than the first separation as the second separation；
Third median is chosen from the sample data for being greater than the first separation as third separation；
Based on start node, the first separation, the second separation and third separation, sample data is built into four forks
Tree.
Further, the quaternary tree according to building, calculate collection result in each data abnormal score the step for,
The following steps are included:
Calculate the depth of each data in collection result；
According to the depth of calculating, the abnormal score of each data is obtained.
Further, this is marked to the abnormal data in collection result in the abnormal score that the basis is calculated
Step, specifically:
Judge whether the abnormal score of data is greater than setting numerical value, if so, being abnormal data by the data markers；Instead
It, then be not processed.
Further, the step for result to edge calculations carries out data correlation and generates statistical report form, specifically:
According to edge calculations as a result, joining to the production equipment in workshop, equipment task time, operator, equipment state
Number is associated, and generates statistical report form.
Further, described the step for data analysis is carried out to statistical report form based on deep learning algorithm, including following step
It is rapid:
Priori Workshop Production information is learnt using deep learning algorithm, Workshop Production information is established and control operates
Between mapping model；
According to mapping model, corresponding control operation in statistical report form is obtained.
Further, it is described according to data analysis as a result, the step for remotely being controlled workshop appliance, specifically includes
Following steps:
According to data analysis as a result, the alarm parameters to workshop appliance are configured, the alarm parameters include work
The maximum value of temperature；
According to data analysis as a result, carrying out warning reminding, and adjustment equipment temperature to workshop appliance；
According to data analysis as a result, carrying out switching on and shutting down operation to workshop appliance.
Second technical solution adopted by the present invention is:
Workshop Production information management system based on Internet of Things, comprising:
Acquisition module, for being acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；
Edge calculations module, for carrying out edge calculations to collection result, the edge calculations include data aggregate, data
Denaturation, data filtering, data erasing and abnormality detection；
Relating module carries out data correlation for the result to edge calculations and generates statistical report form；
Data analysis module, for carrying out data analysis to statistical report form based on deep learning algorithm；
Remote control module, for according to data analyze as a result, remotely being controlled workshop appliance, the long-range control
System includes manually controlling and automatically controlling.
Third technical solution adopted by the present invention is:
Workshop Production apparatus for management of information based on Internet of Things, comprising:
Memory, for storing program；
Processor, for loading described program, to execute the workshop life based on Internet of Things as described in the first technical solution
Produce approaches to IM.
The beneficial effects of the present invention are: the present invention is based on equipment state and production information of the technology of Internet of things to workshop are real-time
Acquisition, then by directly obtaining statistical report form, strong real-time after edge calculations and data correlation；In addition, the present invention is based on
After deep learning algorithm carries out data analysis to statistical report form, remote manual control or remote directly can be carried out to workshop appliance
Journey automatically controls, and control mode is flexible, without carrying out manual analysis by expert engineer, improves work efficiency, reduces
Cost of labor.
Detailed description of the invention
Fig. 1 is that the present invention is based on the step flow charts of the Workshop Production approaches to IM of Internet of Things.
Specific embodiment
The present invention is further explained and is illustrated with specific embodiment with reference to the accompanying drawings of the specification.For of the invention real
The step number in example is applied, is arranged only for the purposes of illustrating explanation, any restriction is not done to the sequence between step, is implemented
The execution sequence of each step in example can be adaptively adjusted according to the understanding of those skilled in the art.
Referring to Fig.1, the present invention is based on the Workshop Production approaches to IM of Internet of Things, comprising the following steps:
It is acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；
Edge calculations are carried out to collection result, the edge calculations include data aggregate, data denaturation, data filtering, number
According to erasing and abnormality detection；
Data correlation is carried out to the result of edge calculations and generates statistical report form；
Data analysis is carried out to statistical report form based on deep learning algorithm；
According to data analysis as a result, remotely controlled workshop appliance, the long-range control include manually control with
It automatically controls.
Wherein, the equipment state and production information in workshop include temperature, speed, quantity and the rate of transferring of equipment etc..
It is further used as preferred embodiment, described the step for collection result is carried out abnormality detection, including it is following
Step:
Several sample datas in collection result are extracted to construct quaternary tree；
According to the quaternary tree of building, the abnormal score of each data in collection result is calculated；
According to the abnormal score being calculated, the abnormal data in collection result is marked.
It is further used as preferred embodiment, it is described to extract several sample datas in collection result to construct four forks
The step for tree, comprising the following steps:
Several sample datas are extracted from collection result；
One is randomly selected from several sample datas as start node；
The first median of several sample datas is chosen as the first separation；
The second median is chosen from the sample data less than the first separation as the second separation；
Third median is chosen from the sample data for being greater than the first separation as third separation；
Based on start node, the first separation, the second separation and third separation, sample data is built into four forks
Tree.
It is further used as preferred embodiment, the quaternary tree according to building calculates each data in collection result
Abnormal score the step for, comprising the following steps:
Calculate the depth of each data in collection result；
According to the depth of calculating, the abnormal score of each data is obtained.
It is further used as preferred embodiment, the abnormal score that the basis is calculated, to different in collection result
The step for regular data is marked, specifically:
Judge whether the abnormal score of data is greater than setting numerical value, if so, being abnormal data by the data markers；Instead
It, then be not processed.
It is further used as preferred embodiment, the result to edge calculations carries out data correlation and generates statistics report
The step for table, specifically:
According to edge calculations as a result, joining to the production equipment in workshop, equipment task time, operator, equipment state
Number is associated, and generates statistical report form.
Wherein, the content of statistical report form includes Current Temperatures, present speed and the rate of transferring of equipment etc., further include temperature and
The corresponding alarm police circles' value of speed.
It is further used as preferred embodiment, it is described that this is analyzed to statistical report form progress data based on deep learning algorithm
One step, comprising the following steps:
Priori Workshop Production information is learnt using deep learning algorithm, Workshop Production information is established and control operates
Between mapping model；
According to mapping model, corresponding control operation in statistical report form is obtained.
Be further used as preferred embodiment, it is described according to data analysis as a result, remotely being controlled to workshop appliance
The step for processed, specifically includes the following steps:
According to data analysis as a result, the alarm parameters to workshop appliance are configured, the alarm parameters include work
The maximum value of temperature；
According to data analysis as a result, carrying out warning reminding, and adjustment equipment temperature to workshop appliance；
According to data analysis as a result, carrying out switching on and shutting down operation to workshop appliance.
For example: if production technology needs just work normally at a temperature of 80 to 100 degree, can in wechat small routine or
Maximum temperature values and minimum temperature value are set in person's APP software, and when reaching 101 degree such as temperature or be down to 79 degree, server-side all can
Lower photos and sending messages carry out alarm and reminding to user, with the operating temperature of timely adjustment equipment；
Corresponding with the method for Fig. 1, the present invention is based on the Workshop Production information management systems of Internet of Things, comprising:
Acquisition module, for being acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；
Edge calculations module, for carrying out edge calculations to collection result, the edge calculations include data aggregate, data
Denaturation, data filtering, data erasing and abnormality detection；
Relating module carries out data correlation for the result to edge calculations and generates statistical report form；
Data analysis module, for carrying out data analysis to statistical report form based on deep learning algorithm；
Remote control module, for according to data analyze as a result, remotely being controlled workshop appliance, the long-range control
System includes manually controlling and automatically controlling.
Corresponding with the method for Fig. 1, the present invention is based on the Workshop Production apparatus for management of information of Internet of Things, comprising:
Memory, for storing program；
Processor, for loading described program, to execute, the present invention is based on the Workshop Production approaches to IM of Internet of Things.
Workshop Production approaches to IM of the invention can be carried out by wechat small routine, APP software or background terminal
Concrete application, below by taking the application scenarios of APP software as an example, the present invention will be described in detail the Workshop Production message tube based on Internet of Things
The specific steps process of reason method:
S1, it is acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；
Wherein, step S1 specifically: by the Internet of Things monitoring device such as camera, sensor and communication module, to workshop
Equipment state and production information acquire in real time.
S2, to collection result carry out edge calculations, the edge calculations include data aggregate, data denaturation, data filtering,
Data erasing and abnormality detection.The purpose of step S2 is to carry out primary filtration to the collection result of Internet of Things monitoring device, is reduced
Abnormal data reduces the bandwidth of data transmission, the speed of improve data transfer, and then improves real-time.
Wherein, this processing step is carried out abnormality detection to collection result specifically includes the following steps:
S21, several sample datas are extracted from collection result；
S22, one is randomly selected from several sample datas as start node；
S23, the first median of several sample datas is chosen as the first separation；
S24, the second median is chosen from the sample data less than the first separation as the second separation；
S25, third median is chosen from the sample data for being greater than the first separation as third separation；
S26, it is based on start node, the first separation, the second separation and third separation, sample data is built into four
Fork tree；
S27, the depth for calculating each data in collection result；
S28, the depth according to calculating, obtain the abnormal score of each data；
S29, judge whether the abnormal score of data is greater than setting numerical value, if so, being abnormal data by the data markers；
Conversely, being then not processed.
S3, data correlation is carried out to the result of edge calculations and generates statistical report form；
Wherein, the step S3 specifically: according to edge calculations as a result, when production equipment, equipment to workshop work
Between, operator, equipment status parameter be associated, generate statistical report form.
S4, data analysis is carried out to statistical report form based on deep learning algorithm；
Wherein, the step S4 specifically includes the following steps:
S41, priori Workshop Production information is learnt using deep learning algorithm, establishes Workshop Production information and control
Mapping model between operation；
Wherein, Workshop Production information and control operation between mapping model such as: when the operating temperature of production equipment is
At 100 °, corresponding control operation is to shut down to production equipment.The present invention establishes mapping mould by deep learning algorithm
Type can provide a control program with priori referring to value for subsequent artificial remotely controlling, or directly according to control
Scheme processed carries out automatically controlling, and without human intervention, greatly improves work efficiency and reduces cost of labor.
S42, according to mapping model, obtain corresponding control in statistical report form and operate.
S5, according to data analysis as a result, remotely controlled workshop appliance, the long-range control is including manually controlling
And automatic control；
S6, user account and password are inputted by APP software, obtains facility information belonging to corresponding account, and to phase
The equipment answered carries out remote control operation etc..
Workshop Production approaches to IM of the invention can be fully applicable in PCB circuit board production procedure, wherein
PCB circuit board critical process includes: reinforcement bonding process, fits over film and cuts membrane process etc.；Critical process equipment includes:
The multiple types equipment such as the full-automatic reinforcing equipment of FPC, automatic cutting film machine, automatic film applicator；By means of the present invention can
It enough greatly improves PCB circuit board automation equipment factory and PCB manufactures the working efficiency of factory.
In conclusion the present invention is based on Workshop Production approaches to IM, system and the devices of Internet of Things to have following function
Can:
1), equipment is filed management: including device type, device name, affiliated company, device parameter, the bases such as accessory data
Plinth data maintenance.
2), obtain device data: obtaining includes production capacity, temperature, speed, and the equipment such as pressure specify parameter, ensures related work
Skill operates normally under specified parameter.
3), alarm parameters are set: alarm police circles' value of specified parameter is set, to have the function that prompting and alarm.
4), transfer rate analysis: the rate report form statistics of transferring including operation hours and production capacity is analyzed.
5) rights management, is arranged: for the login account of user, that distributes different equipment checks permission.
6) it, may be used on the mobile terminals such as wechat small routine and APP software, it is convenient and practical.
7), long-range control: the control that can be carried out according to the operating condition of equipment manually or automatically operates.
It is to be illustrated to preferable implementation of the invention, but the present invention is not limited to the embodiment above, it is ripe
Various equivalent deformation or replacement can also be made on the premise of without prejudice to spirit of the invention by knowing those skilled in the art, this
Equivalent deformation or replacement are all included in the scope defined by the claims of the present application a bit.
Claims (6)
1. the Workshop Production approaches to IM based on Internet of Things, it is characterised in that: the following steps are included:
It is acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；
Edge calculations are carried out to collection result, the edge calculations include data aggregate, data denaturation, data filtering, data wiping
It removes and abnormality detection；
Data correlation is carried out to the result of edge calculations and generates statistical report form；
Data analysis is carried out to statistical report form based on deep learning algorithm；
According to data analysis as a result, remotely being controlled workshop appliance, the long-range control is including manually controlling and automatically
Control；
Described the step for collection result is carried out abnormality detection, comprising the following steps:
Several sample datas in collection result are extracted to construct quaternary tree；
According to the quaternary tree of building, the abnormal score of each data in collection result is calculated；
According to the abnormal score being calculated, the abnormal data in collection result is marked；
Several sample datas in the extraction collection result are come the step for constructing quaternary tree, comprising the following steps:
Several sample datas are extracted from collection result；
One is randomly selected from several sample datas as start node；
The first median of several sample datas is chosen as the first separation；
The second median is chosen from the sample data less than the first separation as the second separation；
Third median is chosen from the sample data for being greater than the first separation as third separation；
Based on start node, the first separation, the second separation and third separation, sample data is built into quaternary tree；
The quaternary tree according to building, the step for calculating the abnormal score of each data in collection result, including following step
It is rapid:
Calculate the depth of each data in collection result；
According to the depth of calculating, the abnormal score of each data is obtained；
The abnormal score that the basis is calculated, the step for the abnormal data in collection result is marked, specifically:
Judge whether the abnormal score of data is greater than setting numerical value, if so, being abnormal data by the data markers；Conversely, then
It is not processed.
2. the Workshop Production approaches to IM according to claim 1 based on Internet of Things, it is characterised in that: the opposite side
The step for result that edge calculates carries out data correlation and generates statistical report form, specifically:
According to edge calculations as a result, to the production equipment in workshop, equipment task time, operator, equipment status parameter into
Row association, generates statistical report form.
3. the Workshop Production approaches to IM according to claim 1 based on Internet of Things, it is characterised in that: described to be based on
The step for deep learning algorithm carries out data analysis to statistical report form, comprising the following steps:
Priori Workshop Production information is learnt using deep learning algorithm, is established between Workshop Production information and control operation
Mapping model；
According to mapping model, corresponding control operation in statistical report form is obtained.
4. the Workshop Production approaches to IM according to claim 1 based on Internet of Things, it is characterised in that: the basis
Data analysis as a result, the step for remotely being controlled workshop appliance, specifically includes the following steps:
According to data analysis as a result, the alarm parameters to workshop appliance are configured, the alarm parameters include operating temperature
Maximum value；
According to data analysis as a result, carrying out warning reminding, and adjustment equipment temperature to workshop appliance；
According to data analysis as a result, carrying out switching on and shutting down operation to workshop appliance.
5. the Workshop Production information management system based on Internet of Things, it is characterised in that: include:
Acquisition module, for being acquired in real time based on equipment state and production information of the technology of Internet of things to workshop；
Edge calculations module, for carrying out edge calculations to collection result, the edge calculations include data aggregate, data change
Property, data filtering, data erasing and abnormality detection；
Relating module carries out data correlation for the result to edge calculations and generates statistical report form；
Data analysis module, for carrying out data analysis to statistical report form based on deep learning algorithm；
Remote control module, for according to data analyze as a result, remotely being controlled workshop appliance, the long-range control packet
It includes and manually controls and automatically control；
Described the step for collection result is carried out abnormality detection, comprising the following steps:
Several sample datas in collection result are extracted to construct quaternary tree；
According to the quaternary tree of building, the abnormal score of each data in collection result is calculated；
According to the abnormal score being calculated, the abnormal data in collection result is marked；
Several sample datas in the extraction collection result are come the step for constructing quaternary tree, comprising the following steps:
Several sample datas are extracted from collection result；
One is randomly selected from several sample datas as start node；
The first median of several sample datas is chosen as the first separation；
The second median is chosen from the sample data less than the first separation as the second separation；
Third median is chosen from the sample data for being greater than the first separation as third separation；
Based on start node, the first separation, the second separation and third separation, sample data is built into quaternary tree；
The quaternary tree according to building, the step for calculating the abnormal score of each data in collection result, including following step
It is rapid:
Calculate the depth of each data in collection result；
According to the depth of calculating, the abnormal score of each data is obtained；
The abnormal score that the basis is calculated, the step for the abnormal data in collection result is marked, specifically:
Judge whether the abnormal score of data is greater than setting numerical value, if so, being abnormal data by the data markers；Conversely, then
It is not processed.
6. the Workshop Production apparatus for management of information based on Internet of Things, it is characterised in that: include:
Memory, for storing program；
Processor, it is raw to execute the workshop according to any one of claims 1-4 based on Internet of Things for loading described program
Produce approaches to IM.
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| CN111078786A (en) * | 2019-10-21 | 2020-04-28 | 张国基 | Edge operation system |
| CN110764431A (en) * | 2019-11-28 | 2020-02-07 | 安徽实友电力金具有限公司 | Circuit switch control system of smart home |
| CN111680045A (en) * | 2020-06-15 | 2020-09-18 | 华晨宝马汽车有限公司 | Method and device for generating data value stream about vehicle and storage medium |
| CN112241154A (en) * | 2020-10-15 | 2021-01-19 | 杭州澳亚生物技术有限公司 | Intelligent monitoring management system for GMP workshop |
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