CN109828545A - AI intelligent process anomalous identification closed loop control method, host and change system - Google Patents
AI intelligent process anomalous identification closed loop control method, host and change system Download PDFInfo
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- 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]
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
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- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention discloses a kind of AI intelligent process anomalous identification closed loop control method, host and change systems, wherein the AI intelligent process anomalous identification closed loop control method includes: the condition relevant data of real-time reception solar battery sheet；The condition relevant data and normal condition data are compared, judge the state of the solar battery sheet；It when the solar battery sheet is abnormality, is matched by the condition relevant data with abnormal level data library, obtains the first exception level；Corresponding abnormality processing strategy is carried out according to first exception level.The state of cell piece on solar cell module production line described in intelligent real-time monitoring of the present invention, according to the different conditions of cell piece, automatic guidance controls the operating of the solar cell module production line, reduce artificial participation, improve the production efficiency of the solar cell module production line, the yields of product is also improved, there is preferable effect.
Description
Technical field
The present invention relates to solar photovoltaic assembly manufacturing technology fields more particularly to a kind of AI intelligent process anomalous identification to close
Ring control method, host and change system.
Background technique
Photovoltaic manufactures at present and using quick high speed development, China was in photovoltaic solar panel system in recent years
Making equipment field gradually becomes international main supplier, and the leading enterprise of especially high-speed automated welding jig is all at home.
But practical in high-speed welding automatic field, again without method to the abnormal formation of process rapidly and efficiently closed-loop control, all
It is that personnel is needed to intervene, is judged online and identified, label etc..Employee's progress screening area is required in subsequent process of reprocessing
Point.It is conventional to rely on people after especially reaching current 2 times or 3 times of speed with the further promotion of equipment speed of welding
Member's on-line operation, difficulty are just very high.Have to look for new design scheme thus to solve.Employee is relied primarily on as process
Judgement or random sampling, just take hidden danger to processing procedure and product.Operator actually has the individual difference of work incentive,
Also there is the difference of physical efficiency, therefore there are the various phenomenons such as erroneous judgement or error in operation of much failing to judge, integral product is manufactured
Bring the influence of yield.It since personnel need to rest, has a meal, influence is also taken on the continuous production of equipment.Stopping line will lead to
Output reduces, road personnel waiting time and cost after waste.Continuous production random sampling is arranged, missing inspection or appearance may be faced
The probability such as the bad risk of small lot.
In current photovoltaic solar panel manufacturing field, practical automatic welding process face it is bad must reprocess, this
It is to have raw material, technique, equipment, what many factors were formed, therefore reprocessing is also the processing procedure challenge that must all face of each family.When
Before reprocess operator, it is necessary to face a large amount of repetitive work task, the unit label of every exception and distinguish just than
It is more important.Practical is all currently online judgement and operation by automatic welding process operations personnel to complete, but in linear speed
Degree is fastly and operation is also inconvenient, and label is difficult to refine and precisely, needs to reprocess employee and further screens and again identifies that.
Above content is only used to facilitate the understanding of the technical scheme, and is not represented and is recognized that above content is existing skill
Art.
Summary of the invention
Present invention is primarily aimed at provide a kind of AI intelligence (artificial intelligence, Artificial Intelligence) mistake
Journey anomalous identification closed loop control method, host and change system, it is intended to solve in the prior art during manual operation not in time,
Be difficult to quickly identify, Fast Labeling and lead to the problem that yields is low and production efficiency is low.
To achieve the above object, the present invention provides a kind of AI intelligent process anomalous identification closed loop control method, host and dress
Standby system, wherein the AI intelligent process anomalous identification closed loop control method the following steps are included:
The condition relevant data of real-time reception solar battery sheet；
The condition relevant data and normal condition data are compared, judge the state of the solar battery sheet；
When the solar battery sheet is abnormality, by the condition relevant data and abnormal level data library into
Row matching, obtains the first exception level；
Corresponding abnormality processing strategy is carried out according to first exception level.
Optionally, first exception level includes severely subnormal, main abnormal and secondary exception；
In the step for carrying out corresponding abnormality processing strategy according to first exception level:
When first exception level is severely subnormal, the abnormality processing strategy includes generating outage information instruction；
When first exception level is main abnormal, the abnormality processing strategy includes:
Count the first frequency data that the condition relevant data occurs in first time period；
There is serious thing risk probability by the internal unit and/or product that prestore in the abnormal level data library to calculate
The first fault-tolerant frequency in the first time period out；
It is compared by first frequency data and the described first fault-tolerant frequency, obtains the condition relevant data
Second exception level；
The second exception handling information is generated by second exception level, second exception handling information includes generating
Outage information instruction；
When first exception level is secondary abnormal, the abnormality processing strategy includes:
Count the second frequency data that the condition relevant data occurs in second time period；
Pass through the internal unit and/or product duplicating property batch accident probability prestored in the abnormal level data library
Calculate the second fault-tolerant frequency in the second time period；
It is compared by second frequency data and the described second fault-tolerant frequency, obtains the condition relevant data
Third exception level；
Third exception handling information is generated by the third exception level, the third exception handling information includes generating
Warning alert information command.
Optionally, the condition relevant data and normal condition data are compared described, judges the solar energy
After the step of state of cell piece, further includes:
When the solar battery sheet is abnormality, by the condition relevant data and abnormal level data library into
Row matching when matching unsuccessful, counts the frequency data that the condition relevant data occurs；
It is compared by the frequency data with the fault-tolerant frequency, obtains the 4th exception level of condition relevant data；
The 4th exception handling information is generated by the 4th exception level.
Optionally, it is compared by the frequency data with the fault-tolerant frequency, obtains the 4th of the condition relevant data
After the step of exception level, further includes:
When the frequency data are greater than the fault-tolerant frequency, the condition relevant data is stored in the exception level
Database, and update the abnormal level data library；
When the frequency data are less than the fault-tolerant frequency, the condition relevant data is recorded, and regularly update to institute
State abnormal level data library.
The present invention also provides a kind of control host, the control host includes: memory, processor and is stored in described deposit
On reservoir and the AI intelligent process anomalous identification closed loop control process that can run on the processor, the AI intelligent process are different
The other closed loop control process of common sense is arranged for carrying out the step of AI intelligent process anomalous identification closed loop control method, the AI intelligence mistake
Journey anomalous identification closed loop control method includes:
The condition relevant data of real-time reception solar battery sheet；
The condition relevant data and normal condition data are compared, judge the state of the solar battery sheet；
When the solar battery sheet is abnormality, by the condition relevant data and abnormal level data library into
Row matching, obtains the first exception level；
Corresponding abnormality processing strategy is carried out according to first exception level.
The present invention also provides a kind of AI intelligent process anomalous identification closed-loop control change systems, are used for solar cell module
Production line, the AI intelligent process anomalous identification closed-loop control change system include control host and with the control host
The recognizer component and executive module of electric connection, in which:
The recognizer component is for obtaining the solar battery sheet condition relevant data, and by the condition relevant data
It is sent to the control host；
The executive module, for carrying out exception after the first exception handling information for receiving the control host
Reason；
The control host includes: memory, processor and is stored on the memory and can be on the processor
The AI intelligent process anomalous identification closed loop control process of operation, the AI intelligent process anomalous identification closed loop control process are configured to
The step of realizing AI intelligent process anomalous identification closed loop control method, the AI intelligent process anomalous identification closed loop control method packet
It includes:
The condition relevant data of real-time reception solar battery sheet；
The condition relevant data and normal condition data are compared, judge the state of the solar battery sheet；
When the solar battery sheet is abnormality, by the condition relevant data and abnormal level data library into
Row matching, obtains the first exception level；
Corresponding abnormality processing strategy is carried out according to first exception level.
Optionally, the recognizer component includes multiple identification sensors, and multiple identification sensors include image recognition
Sensor, temperature sensor, photoelectric sensor.
Optionally, there are multiple stations, multiple stations include feeding work on the solar cell module production line
Position, transmission station, tile station, series welding station, confluence welder position and typesetting station；
Multiple identification sensor correspondences are distributed at multiple stations, for obtaining the institute at the corresponding station
State the condition relevant data of solar battery sheet.
It optionally, further include the label component being electrically connected with the control host；
The control host is also used to generate spatial abnormal feature data information and mark information by the condition relevant data,
The mark information is sent to the label component, the spatial abnormal feature data information is used to reprocess personnel's operation for guidance；
Place is marked for receiving the mark information, and to the abnormal solar battery sheet in the label component
Reason.
Optionally, the label component includes Laser Jet device, for the abnormal solar battery sheet and/or institute
The glass substrate for stating solar cell module carries out coding processing.
The present invention passes through the condition relevant data of real-time reception solar battery sheet, by the condition relevant data and normally
Status data compares, and judges the state of the solar battery sheet, when the solar battery sheet is abnormality, leads to
It crosses the condition relevant data to be matched with abnormal level data library, obtains the first exception level, it is abnormal according to described first
Grade carries out corresponding abnormality processing strategy, the cell piece on solar cell module production line described in energy intelligent real-time monitoring
State, according to the different conditions of cell piece, automatic guidance controls the operating of the solar cell module production line, reduces people
For participation, improve the production efficiency of the solar cell module production line, also improve the yields of product, have compared with
Good effect.
Detailed description of the invention
Fig. 1 is the server architecture schematic diagram for the hardware running environment that the embodiment of the present invention is related to；
Fig. 2 is the flow diagram of AI intelligent process anomalous identification closed loop control method first embodiment of the present invention；
Fig. 3 is the flow diagram of AI intelligent process anomalous identification closed loop control method second embodiment of the present invention；
Fig. 4 is the flow diagram of AI intelligent process anomalous identification closed loop control method 3rd embodiment of the present invention；
Fig. 5 is the flow diagram of AI intelligent process anomalous identification closed loop control method fourth embodiment of the present invention；
Fig. 6 is the structural schematic diagram of an embodiment of AI intelligent process anomalous identification closed-loop control system of the present invention；
Fig. 7 is in the present invention using the solar battery group for having AI intelligent process anomalous identification closed-loop control system in Fig. 6
The structural schematic diagram of part production line (partial structurtes) embodiment；
Fig. 8 is that the structure of an embodiment of the recognizer component of AI intelligent process anomalous identification closed-loop control system in Fig. 6 is shown
It is intended to；
Fig. 9 is that the structure of an embodiment of the label component of AI intelligent process anomalous identification closed-loop control system in Fig. 6 is shown
It is intended to.
Drawing reference numeral explanation:
| Label | Title | Label | Title |
| 1000 | AI intelligent process anomalous identification closed-loop control change system | 20 | Identification sensor |
| a | Solar cell module production line | 300 | Executive module |
| 100 | Control host | 400 | Mark component |
| 200 | Recognizer component | 40 | Laser Jet device |
The embodiments will be further described with reference to the accompanying drawings for the realization, the function and the advantages of the object of the present invention.
Specific embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
Referring to Fig.1, Fig. 1 is the structural schematic diagram of present invention control host.
As shown in Figure 1, the control host may include: processor 1001, such as CPU, communication bus 1002, user interface
1003, network interface 1004, memory 1005.Wherein, communication bus 1002 is for realizing the connection communication between these components.
User interface 1003 may include display screen (Display), input unit such as keyboard (Keyboard), optional user interface
1003 can also include standard wireline interface and wireless interface.Network interface 1004 optionally may include that the wired of standard connects
Mouth, wireless interface (such as WI-FI interface).Memory 1005 can be high speed RAM memory, be also possible to stable memory
(non-volatile memory), such as magnetic disk storage.Memory 1005 optionally can also be independently of aforementioned processor
1001 storage device.
It will be understood by those skilled in the art that structure shown in Fig. 1 does not constitute the restriction to control host, can wrap
It includes than illustrating more or fewer components, perhaps combines certain components or different component layouts.
As shown in Figure 1, as may include that operating system, network are logical in a kind of memory 1005 of computer storage medium
Believe module, the control program of Subscriber Interface Module SIM and AI intelligent process anomalous identification closed-loop control host 100.
In server shown in Fig. 1, network interface 1004 is mainly used for connecting terminal device, is counted with terminal device
According to communication；User interface 1003 is mainly used for receiving the input instruction of administrator；The server is called by processor 1001
The control program of the AI intelligent process anomalous identification closed-loop control host 100 stored in memory 1005, and execute following operation:
The condition relevant data of real-time reception solar battery sheet；
The condition relevant data and normal condition data are compared, judge the state of the solar battery sheet；
When the solar battery sheet is abnormality, by the condition relevant data and abnormal level data library into
Row matching, obtains the first exception level；
Corresponding abnormality processing strategy is carried out according to first exception level.
Further, processor 1001 can call the AI intelligent process anomalous identification closed loop control stored in memory 1005
The control program of host 100 processed also executes following operation:
First exception level includes severely subnormal, main abnormal and secondary exception；
In the step for carrying out corresponding abnormality processing strategy according to first exception level:
When first exception level is severely subnormal, the abnormality processing strategy includes generating outage information instruction；
When first exception level is main abnormal, the abnormality processing strategy includes:
Count the first frequency data that the condition relevant data occurs in first time period；
There is serious thing risk probability by the internal unit and/or product that prestore in the abnormal level data library to calculate
The first fault-tolerant frequency in the first time period out；
It is compared by first frequency data and the described first fault-tolerant frequency, obtains the condition relevant data
Second exception level；
The second exception handling information is generated by second exception level, second exception handling information includes generating
Outage information instruction；
When first exception level is secondary abnormal, the abnormality processing strategy includes:
Count the second frequency data that the condition relevant data occurs in second time period；
Pass through the internal unit and/or product duplicating property batch accident probability prestored in the abnormal level data library
Calculate the second fault-tolerant frequency in the second time period；
It is compared by second frequency data and the described second fault-tolerant frequency, obtains the condition relevant data
Third exception level；
Third exception handling information is generated by the third exception level, the third exception handling information includes generating
Warning alert information command.
Further, processor 1001 can call the AI intelligent process anomalous identification closed loop control stored in memory 1005
The control program of host 100 processed also executes following operation:
The condition relevant data and normal condition data are compared described, judge the solar battery sheet
After the step of state, further includes:
When the solar battery sheet is abnormality, by the condition relevant data and abnormal level data library into
Row matching when matching unsuccessful, counts the frequency data that the condition relevant data occurs；
It is compared by the frequency data with the fault-tolerant frequency, obtains the 4th exception level of condition relevant data；
The 4th exception handling information is generated by the 4th exception level.
Further, processor 1001 can call the AI intelligent process anomalous identification closed loop control stored in memory 1005
The control program of host 100 processed also executes following operation:
It is compared by the frequency data with the fault-tolerant frequency, obtains the 4th exception level of the condition relevant data
The step of after, further includes:
When the frequency data are greater than the fault-tolerant frequency, the condition relevant data is stored in the exception level
Database, and update the abnormal level data library；
When the frequency data are less than the fault-tolerant frequency, the condition relevant data is recorded, and regularly update to institute
State abnormal level data library.
The present invention passes through the condition relevant data of real-time reception solar battery sheet, by the condition relevant data and normally
Status data compares, and judges the state of the solar battery sheet, when the solar battery sheet is abnormality, leads to
It crosses the condition relevant data to be matched with abnormal level data library, obtains the first exception level, it is abnormal according to described first
Grade carries out corresponding abnormality processing strategy, the cell piece on solar cell module production line described in energy intelligent real-time monitoring
State, according to the different conditions of cell piece, automatic guidance controls the operating of the solar cell module production line, reduces people
For participation, improve the production efficiency of the solar cell module production line, also improve the yields of product, have compared with
Good effect.
Based on above-mentioned hardware configuration, Fig. 2 to Fig. 6 is AI intelligent process anomalous identification closed loop control method provided by the invention
Embodiment.
Referring to figure 2., Fig. 2 is AI intelligent process anomalous identification closed loop control method first embodiment provided by the invention
Flow diagram, in the present embodiment, the AI intelligent process anomalous identification closed loop control method the following steps are included:
Step S10, the condition relevant data of real-time reception solar battery sheet；
It should be noted that in the entire production procedure of solar cell module, the state of the solar battery sheet
Related data includes image data, temperature data, welding fabrication qualitative data, location data etc. related data, above data
It is all related to the quality of the solar battery sheet, in the present embodiment, do not limit more than status data, can also include very much
Status data.
Step S20, the condition relevant data and normal condition data are compared, judges the solar battery sheet
State；
It should be noted that the normal condition data of the solar battery sheet are all stored into the control system,
Image data including normal battery piece, temperature data, welding fabrication qualitative data, location data etc. related data work as reality
When receive solar battery sheet condition relevant data after, the condition relevant data and normal condition data are compared,
The state for judging the solar battery sheet, if it is normal condition, then the detected solar battery sheet is normal
State, the entire production line run well.
Step S30, when the solar battery sheet is abnormality, pass through the condition relevant data and exception level
Database is matched, and the first exception level is obtained；
It should be noted that can establish an abnormal level data library in systems, the abnormal level data library will not
Same abnormal defect classification carries out exception level classification by a default rule, for example, can be to reflect according to different classifications
The form of firing table, if the detected solar battery sheet is abnormal condition, the detected institute of whole system judgement
Stating solar battery sheet is abnormality, searches the abnormal class of the abnormality pairing, and then is obtained corresponding described different
Normal grade.
Step S40, corresponding abnormality processing strategy is carried out according to first exception level；
It should be noted that needing in order to enable the entire solar cell module production line a is able to achieve efficient production
Different abnormality processing strategies is selected according to different exception levels, different exceptions is handled differently, and can be obtained
The control more refined.
The present invention passes through the condition relevant data of real-time reception solar battery sheet, by the condition relevant data and normally
Status data compares, and judges the state of the solar battery sheet, when the solar battery sheet is abnormality, leads to
It crosses the condition relevant data to be matched with abnormal level data library, obtains the first exception level, it is abnormal according to described first
Grade carries out corresponding abnormality processing strategy, the cell piece on solar cell module production line a described in energy intelligent real-time monitoring
State, according to the different conditions of cell piece, automatic guidance controls the operating of the solar cell module production line a, reduces
Artificial participation, improves the production efficiency of the solar cell module production line a, also improves the yields of product,
With preferable effect.
Referring to figure 3., Fig. 3 is AI intelligent process anomalous identification closed loop control method second embodiment provided by the invention
Flow diagram, in the present embodiment, first exception level includes severely subnormal, main abnormal and secondary exception, according to
First exception level carries out in the step of corresponding abnormality processing strategy:
Step S40a, when first exception level is severely subnormal, the abnormality processing strategy includes generating to shut down
Information command；
It should be noted that first exception level is classified as severely subnormal, main abnormal and secondary exception three
Rank certainly controls the production line in order to finer, can also be that first exception level differentiation is multiple
Exception level, so that finer to the monitoring of the production line, for example, there is serious fragmentation, and when area is larger, sternly
Ghost image rings the quality of production of entire product, is determined as severely subnormal, and general exception is determined as main abnormal, light when only making to occur
When micro- scratch, when on the quality of production of entire product without influencing or influencing smaller, it is determined as secondary exception.
Step S40b, when first exception level is main abnormal, the abnormality processing strategy includes first step
The first frequency data occurred for the condition relevant data in statistics first time period；
It should be noted that, although first exception level is main abnormal, but if the exception of the main abnormal
The frequency of appearance is excessively high, often represents that catastrophe failure occurs in equipment or catastrophe failure occurs in material, can also seriously affect whole
The quality of production of a product needs to count at this time the first frequency data that the condition relevant data occurs in first time period,
Further to judge.
Step S50b, there is serious thing wind by the internal unit and/or product that prestore in the abnormal level data library
Dangerous probability calculates the first fault-tolerant frequency in the first time period；
It should be noted that the data that serious thing risk probability occur in internal unit and/or product are stored in the exception
In rating database, the first fault-tolerant frequency occurred extremely in the effective time can be calculated according to the probability, described
The reference that the one fault-tolerant frequency judges as one, within a period of time, the frequency of the secondary abnormal abnormality data is N
It is secondary, as long as being more than this fault-tolerant frequency n times, then an exception level is corresponded to, convenient for comparison judgement；
Step S60b, it is compared by first frequency data and the described first fault-tolerant frequency, obtains the state
Second exception level of related data；
It should be noted that the frequency of the abnormality data of the main abnormal is n times as in a period of time, as long as
Being is more than this fault-tolerant frequency n times, then corresponds to an exception level, within the fault-tolerant frequency n times, is corresponded to another
One exception level, can be handled differently according to different exception levels.
Step S70b, described second exception handling information includes generating outage information instruction；
It should be noted that after judging the second exception level of the condition relevant data, it is abnormal etc. to pass through described second
Grade generates the second exception handling information, such as shuts down or continue production.
Step S40c, when first exception level is secondary abnormal, the abnormality processing strategy includes first step
The second frequency data occurred for the condition relevant data in statistics second time period；
It should be noted that, although first exception level is secondary exception, but if the secondary abnormal exception
The frequency of appearance is excessively high, also will affect the quality of production of entire product, needs to count what the condition relevant data occurred at this time
Frequency data, further to judge.
Step S50c, the internal unit and/or duplicating property of product batch by being prestored in the abnormal level data library
Amount accident probability calculates the second fault-tolerant frequency in the second time period；
It should be noted that the data of internal unit and/or product duplicating property batch accident probability be stored in it is described
In abnormal level data library, the second fault-tolerant frequency occurred extremely in the effective time, institute can be calculated according to the probability
The reference that the second fault-tolerant frequency judges as one is stated, within a period of time, the frequency of the secondary abnormal abnormality data
Secondary is n times, convenient for comparison judgement；
Step S60c, it is compared by second frequency data and the described second fault-tolerant frequency, obtains the state
The third exception level of related data；
It should be noted that the frequency of the secondary abnormal abnormality data is n times as in a period of time, as long as
Being is more than this fault-tolerant frequency n times, then corresponds to an exception level, within the fault-tolerant frequency n times, is corresponded to another
One exception level, can be handled differently according to different exception levels.
Step S70c, third exception handling information, the third abnormality processing letter are generated by the third exception level
Breath includes generating warning alert information command；
It should be noted that after judging the third exception level of the condition relevant data, it is abnormal etc. to pass through the third
Grade generates third exception handling information, and such as warning alert reminds the timely intervention of operating personnel, finds out abnormal details.
In the present invention, it is contemplated that after the first exception level of the condition relevant data, it is entire raw to comprehensively consider influence
The frequency data that the second factor of producing line product quality and efficiency, i.e. condition relevant data occur, by two because Number synthesis is examined
Amount, improves the yields and production efficiency of the product significantly, has preferable effect.
Referring to figure 4., Fig. 4 is AI intelligent process anomalous identification closed loop control method 3rd embodiment provided by the invention
The condition relevant data and normal condition data are compared described in the present embodiment, judge institute by flow diagram
After the step of stating the state of solar battery sheet, further includes:
Step S80, when the solar battery sheet is abnormality, pass through the condition relevant data and exception level
Database is matched, and when matching unsuccessful, counts the frequency data that the condition relevant data occurs；
It should be noted that the abnormal level data library is operating personnel according to the experience in production in the present embodiment
The different quality defects liabilities of typing, the abnormal level data library is the database constantly improve, when occurring
When stating condition relevant data and the unsuccessful situation of abnormal level data storehouse matching, it is also required to come in view of frequency data at this time
Further judge.
Step S90, it is compared by the frequency data with the fault-tolerant frequency, it is different to obtain the condition relevant data second
Normal grade；
It should be noted that needing to set a fault-tolerant principle and the 4th exception level in system, for example, one appearance of setting
The wrong frequency, within a period of time, the frequency of the secondary abnormal abnormality data is n times, as long as being more than that this is fault-tolerant
Frequency n times then correspond to an exception level, within the fault-tolerant frequency n times, correspond to another exception level,
It can be handled differently according to different exception levels.
Step S100, the 4th exception handling information is generated by the 4th exception level.
It should be noted that after judging the 4th exception level of the condition relevant data, it is abnormal etc. to pass through the described 4th
Grade generates the 4th exception handling information, such as shuts down and perhaps continue to produce or directly jump out system dialog box, it is desirable that operation
Personnel's interventional procedure.
In the present invention, when the condition relevant data is not belonging to the Exception Type in the abnormal level data library, examine
Consider the frequency data of condition relevant data appearance, when necessary, operating personnel's interventional procedure improves the product significantly
Yields and production efficiency have preferable effect.
Referring to figure 5., Fig. 5 is AI intelligent process anomalous identification closed loop control method fourth embodiment provided by the invention
Flow diagram is compared with the fault-tolerant frequency by the frequency data in the present embodiment, obtains the state dependency number
According to the second exception level the step of after, further includes:
Step S100a, when the frequency data are greater than the fault-tolerant frequency, the condition relevant data is stored in institute
Abnormal level data library is stated, and updates the abnormal level data library；
It should be noted that illustrating that the condition relevant data compares when the frequency data are greater than the fault-tolerant frequency
Important, prompt person participates in judgement at this time, and the condition relevant data is stored in the abnormal level data library, and is updated
The abnormal level data library, so that database is more perfect.
Step S100b, when the frequency data are less than the fault-tolerant frequency, the condition relevant data is recorded, and fixed
Phase is updated to the abnormal level data library.
It should be noted that illustrating the condition relevant data less when the frequency data are less than the fault-tolerant frequency
Important, system records the condition relevant data, and regularly updates to the abnormal level data library.
In the present invention, the abnormal level data library needs to constantly improve, when the frequency data are greater than the appearance
When the wrong frequency, the condition relevant data is stored in the abnormal level data library, and updates the abnormal level data library,
When the frequency data are less than the fault-tolerant frequency, record the condition relevant data, and regularly update to the exception etc.
Grade database, by the urgent abnormal level data library that timely updates with non-emergent two different situations, so that entire system
System constantly study and self-perfection, are continuously improved the yields and production efficiency of product, have preferable effect.
The present invention provides a kind of AI intelligent process anomalous identification closed-loop control change system, and Fig. 6 is AI provided by the invention
The structural schematic diagram of one embodiment of intelligent process anomalous identification closed-loop control change system.
Fig. 6 to Fig. 9 is please referred to, the AI intelligent process anomalous identification closed-loop control change system 1000 is used for solar energy
Battery component production line a, including control host 100 and with it is described control host 100 be electrically connected recognizer component 200 and
Executive module 300, wherein the recognizer component 200 is used to obtain the solar battery sheet condition relevant data, and will be described
Condition relevant data is sent to the control host 100, the executive module 300, for receiving the control host 100
The first exception handling information after, carry out abnormality processing, wherein the control host 100 include above-mentioned all technical solutions,
Therefore, the AI intelligent process anomalous identification closed-loop control change system 1000 also includes above-mentioned all technical solutions, is also had
There is above-mentioned technical proposal bring technical effect, no longer repeats one by one herein.
It should be noted that the control host 100 is equivalent to the entire AI intelligent process anomalous identification closed-loop control
The control terminal of change system 1000, the recognizer component 200 are the equal of the entire AI intelligent process anomalous identification closed loop control
The input terminal of change system 1000 processed, the executive module 300 are the equal of the entire AI intelligent process anomalous identification closed loop
The output end of change system 1000 is controlled, the input terminal collects the condition relevant data of the relevant solar battery sheet
After be transmitted to the control terminal, after the control terminal carries out relevant processing, outflow to the output end is executed.
The recognizer component 200 is the equal of the entire AI intelligent process anomalous identification closed-loop control change system 1000
Input terminal, mainly acquire the condition relevant data of the solar battery sheet, specifically, in the present embodiment, the identification
Component 200 includes multiple identification sensors 20, and multiple identification sensors 20 include image recognition sensor 20, temperature sensing
Device, photoelectric sensor, acquire the image of the solar battery sheet, temperature, positioning scenarios etc. qualitative data respectively, and by institute
It states condition relevant data and is transmitted to the control host 100.
In addition, having multiple stations on the solar cell module production line a, multiple stations include feeding work
Position, transmission station, tile station, series welding station, confluence welder position and typesetting station, corresponding point of multiple identification sensors 20
It is distributed at multiple stations, it, will for obtaining the condition relevant data of the solar battery sheet at the corresponding station
The recognizer component 200 is respectively set at different stations, can comprehensively understand the solar energy in entire production process
The condition relevant data of cell piece, can with the quality of production of solar cell module described in overall monitor, go wrong in time this
It is handled using different processing strategies, reduces artificial participation, there is preferable effect.
It should be noted that the concrete form of the executive module 300 is not limited in the present embodiment, for example, it may be control
The controller of active motor running on production line processed is also possible to the label component to abnormal solar battery label
400, specifically, the AI intelligent process anomalous identification closed-loop control change system 1000, which is characterized in that further include with it is described
The label component 400 that host 100 is electrically connected is controlled, the control host 100 is also used to raw by the condition relevant data
At spatial abnormal feature data information and mark information, the mark information is sent to the label component 400, the spatial abnormal feature
Data information is used to reprocess personnel's operation for guidance, and the label component 400 is used to receive the mark information, and to exception
Processing is marked in the solar battery sheet, and the mark information convenient for reprocessing personal identification, believe by the spatial abnormal feature data
Breath is used to instruct to reprocess the solar battery sheet of people finder's exception.
In the present embodiment, the label component 400 includes Laser Jet device 40, for the abnormal solar battery
The glass substrate of piece and/or the solar cell module carries out coding processing will be described abnormal by certain coding rule
Solar battery sheet is marked, the position of recording exceptional and abnormal type etc. information, for the big solar energy
Battery component carries out coding processing in the glass substrate of the solar cell module, and guidance reprocesses personnel and carries out operation, hence it is evident that
Ground improves the operating efficiency reprocessed, and has preferable effect.
In addition, it is necessary to explanation, spatial abnormal feature data information and the item on every piece of solar battery module glass substrate
Code is associated, and is stored in MES system, is reprocessing station, the identification to component bar code, and is called automatically by MES system different
Normal distributed data information is shown that reprocessing station operating personnel on the one hand can be by abnormal cell piece by display
Mark information is identified, is on the other hand identified by the spatial abnormal feature data information of display, is carried out to two diagrams
Comparison, it is more intuitive and simple, realize that fool-proof design improves work efficiency convenient for reprocessing for operating personnel.
It should be noted that, in this document, the terms "include", "comprise" or its any other variant are intended to non-row
His property includes, so that the process, method, article or the device that include a series of elements not only include those elements, and
And further include other elements that are not explicitly listed, or further include for this process, method, article or device institute it is intrinsic
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that including being somebody's turn to do
There is also other identical elements in the process, method of element, article or device.
The serial number of the above embodiments of the invention is only for description, does not represent the advantages or disadvantages of the embodiments.
The above is only a preferred embodiment of the present invention, is not intended to limit the scope of the invention, all to utilize this hair
Equivalent structure or equivalent flow shift made by bright specification and accompanying drawing content is applied directly or indirectly in other relevant skills
Art field, is included within the scope of the present invention.
Claims (10)
1. a kind of AI intelligent process anomalous identification closed loop control method is used for solar cell module production line, which is characterized in that
The following steps are included:
The condition relevant data of real-time reception solar battery sheet；
The condition relevant data and normal condition data are compared, judge the state of the solar battery sheet；
When the solar battery sheet is abnormality, pass through the condition relevant data and the progress of abnormal level data library
Match, obtains the first exception level；
Corresponding abnormality processing strategy is carried out according to first exception level.
2. AI intelligent process anomalous identification closed loop control method as described in claim 1, which is characterized in that described first is abnormal
Grade includes severely subnormal, main abnormal and secondary exception；
In the step for carrying out corresponding abnormality processing strategy according to first exception level:
When first exception level is severely subnormal, the abnormality processing strategy includes generating outage information instruction；
When first exception level is main abnormal, the abnormality processing strategy includes:
Count the first frequency data that the condition relevant data occurs in first time period；
There is serious thing risk probability by the internal unit and/or product that prestore in the abnormal level data library and calculates institute
State the first fault-tolerant frequency in first time period；
It is compared by first frequency data and the described first fault-tolerant frequency, obtains the second of the condition relevant data
Exception level；
The second exception handling information is generated by second exception level, second exception handling information includes generating to shut down
Information command；
When first exception level is secondary abnormal, the abnormality processing strategy includes:
Count the second frequency data that the condition relevant data occurs in second time period；
It is calculated by the internal unit and/or product duplicating property batch accident probability that are prestored in the abnormal level data library
The second fault-tolerant frequency in the second time period out；
It is compared by second frequency data and the described second fault-tolerant frequency, obtains the third of the condition relevant data
Exception level；
Third exception handling information is generated by the third exception level, the third exception handling information includes generating warning
Warning message instruction.
3. AI intelligent process anomalous identification closed loop control method as described in claim 1, which is characterized in that it is described will be described
After the step of condition relevant data is compared with normal condition data, judges the state of the solar battery sheet, also wrap
It includes:
When the solar battery sheet is abnormality, pass through the condition relevant data and the progress of abnormal level data library
Match, when matching unsuccessful, counts the frequency data that the condition relevant data occurs；
It is compared by the frequency data with the fault-tolerant frequency, obtains the 4th exception level of condition relevant data；
The 4th exception handling information is generated by the 4th exception level.
4. AI intelligent process anomalous identification closed loop control method as claimed in claim 3, which is characterized in that pass through the frequency
After the step of data are compared with the fault-tolerant frequency, obtain four exception level of the condition relevant data, further includes:
When the frequency data are greater than the fault-tolerant frequency, the condition relevant data is stored in the abnormal level data
Library, and update the abnormal level data library；
When the frequency data are less than the fault-tolerant frequency, the condition relevant data is recorded, and regularly update to described different
Normal rating database.
5. a kind of control host characterized by comprising memory, processor and be stored on the memory and can be in institute
State the AI intelligent process anomalous identification closed loop control process run on processor, the AI intelligent process anomalous identification closed-loop control
Program is arranged for carrying out the AI intelligent process anomalous identification closed loop control method as described in any one of Claims 1-4
Step.
6. a kind of AI intelligent process anomalous identification closed-loop control change system is used for solar cell module production line, feature
It is, including control host and the recognizer component and executive module that are electrically connected with the control host, in which:
The recognizer component sends the condition relevant data for obtaining the solar battery sheet condition relevant data
To the control host；
The control host is configured to control host as described in claim 5；
The executive module, for carrying out abnormality processing after the first exception handling information for receiving the control host.
7. AI intelligent process anomalous identification closed-loop control change system as claimed in claim 6, which is characterized in that the identification
Component includes multiple identification sensors, and multiple identification sensors include image recognition sensor, temperature sensor, photoelectric transfer
Sensor.
8. AI intelligent process anomalous identification closed-loop control change system as claimed in claim 7, which is characterized in that the sun
There can be multiple stations on battery component production line, multiple stations include feeding station, transmission station, tile station, string
Welder position, confluence welder position and typesetting station；
Multiple identification sensors correspondences are distributed at multiple stations, for described in obtaining at the corresponding station too
The condition relevant data of positive energy cell piece.
9. AI intelligent process anomalous identification closed-loop control change system as claimed in claim 6, which is characterized in that further include with
The label component that the control host is electrically connected；
The control host is also used to generate spatial abnormal feature data information and mark information by the condition relevant data, by institute
It states mark information and is sent to the label component, the spatial abnormal feature data information is used to reprocess personnel's operation for guidance；
Processing is marked for receiving the mark information, and to the abnormal solar battery sheet in the label component.
10. AI intelligent process anomalous identification closed-loop control change system as claimed in claim 9, which is characterized in that the mark
Remember that component includes Laser Jet device, for the glass to the abnormal solar battery sheet and/or the solar cell module
Glass substrate carries out coding processing.
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| CN201910153814.1A CN109828545B (en) | 2019-02-28 | 2019-02-28 | AI intelligent process anomaly identification closed-loop control method, host and equipment system |
| PCT/CN2019/078376 WO2020172919A1 (en) | 2019-02-28 | 2019-03-15 | Ai intelligent process abnormality recognition closed-loop control method, host and device system |
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| CN201910153814.1A CN109828545B (en) | 2019-02-28 | 2019-02-28 | AI intelligent process anomaly identification closed-loop control method, host and equipment system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626546A (en) * | 2020-04-07 | 2020-09-04 | 青岛奥利普自动化控制系统有限公司 | Exception management system (MES) -based exception management method and equipment |
| CN113642683A (en) * | 2021-10-14 | 2021-11-12 | 深圳市信润富联数字科技有限公司 | Prompting method and device, electronic equipment and computer readable storage medium |
Citations (48)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997012718A1 (en) * | 1995-10-06 | 1997-04-10 | Brown University Research Foundation | Soldering methods and compositions |
| WO2004108345A1 (en) * | 2003-06-09 | 2004-12-16 | Senju Metal Industry Co., Ltd. | Solder paste |
| CN1769920A (en) * | 2004-10-16 | 2006-05-10 | 曼兹自动化股份公司 | Test system of solar energy battery |
| JP2011034647A (en) * | 2009-08-04 | 2011-02-17 | Toray Ind Inc | Biaxially oriented polyester film |
| JP2011068807A (en) * | 2009-09-28 | 2011-04-07 | Toray Ind Inc | Biaxially oriented polyester film |
| CN102365558A (en) * | 2009-02-07 | 2012-02-29 | 拓科学股份有限公司 | High speed detection of shunt defects in photovoltaic and optoelectronic devices |
| CN202295899U (en) * | 2011-09-28 | 2012-07-04 | 山东力诺太阳能电力股份有限公司 | Zero clearing cell storage box for efficiency detection of solar cells |
| CN102544203A (en) * | 2011-12-26 | 2012-07-04 | 嘉兴优太太阳能有限公司 | Monitoring operation system in solar photovoltaic component production line |
| CN102566475A (en) * | 2010-12-17 | 2012-07-11 | 北京北方微电子基地设备工艺研究中心有限责任公司 | Method and device for processing monitoring alarm and plasma processing device |
| CN102830336A (en) * | 2012-08-23 | 2012-12-19 | 英利能源（中国）有限公司 | Alarming device and alarming method for photovoltaic battery rejected products |
| CN102999015A (en) * | 2011-09-16 | 2013-03-27 | 苏州索力旺光伏设备有限公司 | Photovoltaic module flexible manufacturing control system based on field bus |
| CN103067230A (en) * | 2013-01-23 | 2013-04-24 | 江苏天智互联科技有限公司 | Method for achieving hyper text transport protocol (http) service monitoring through embedding monitoring code |
| CN103071626A (en) * | 2011-10-25 | 2013-05-01 | 英稳达科技股份有限公司 | Error proofing method for sorting of solar cells |
| TW201319213A (en) * | 2011-11-11 | 2013-05-16 | Eternal Chemical Co Ltd | Conductive adhesive composition for use in solar cells and uses thereof |
| CN103185721A (en) * | 2011-12-31 | 2013-07-03 | 致茂电子股份有限公司 | Optical detection system |
| CN203178203U (en) * | 2013-03-06 | 2013-09-04 | 江南大学 | Automatic solar silicon wafer color detection device based on machine vision |
| CN103377094A (en) * | 2012-04-12 | 2013-10-30 | 金蝶软件（中国）有限公司 | Abnormity monitoring method and abnormity monitoring device |
| CN103676790A (en) * | 2012-09-07 | 2014-03-26 | 苏州索力旺光伏设备有限公司 | Photovoltaic pipeline ASI bus control system |
| KR20140111069A (en) * | 2013-03-04 | 2014-09-18 | 중앙대학교 산학협력단 | Solar Cell, Mask for fabricating solar cell electrode and method for fabricating the solar cell |
| CN104753461A (en) * | 2015-04-10 | 2015-07-01 | 福州大学 | Method for diagnosing and classifying faults of photovoltaic power generation arrays on basis of particle swarm optimization support vector machines |
| CN105262441A (en) * | 2015-09-08 | 2016-01-20 | 西安交通大学 | Infrared image-based photovoltaic array fault grading method |
| CN105491144A (en) * | 2015-12-16 | 2016-04-13 | 新奥光伏能源有限公司 | Solar battery production line and remote control method and system thereof |
| CN105759748A (en) * | 2014-12-18 | 2016-07-13 | 中芯国际集成电路制造(上海)有限公司 | Semiconductor production machine hardware performance dynamic monitoring system and monitoring method |
| CN105914257A (en) * | 2016-04-26 | 2016-08-31 | 苏州阿特斯阳光电力科技有限公司 | Data analysis based crystalline silica cell production process monitoring method |
| CN106055932A (en) * | 2016-05-26 | 2016-10-26 | 东莞博力威电池有限公司 | MCU program anti-plagiarizing method and system with Boost loader function |
| KR20160126891A (en) * | 2015-04-24 | 2016-11-02 | 주식회사 엘지화학 | Electrode Terminal Welding Device of Battery Cell Assembly Comprising Battery Cells and Welding Process Using the Same |
| CN106161122A (en) * | 2015-03-27 | 2016-11-23 | 银联商务有限公司 | A kind of automatization's Centralized Monitoring method for early warning and system |
| CN106229381A (en) * | 2016-08-30 | 2016-12-14 | 奥特斯维能源（太仓）有限公司 | Solar-energy photo-voltaic cell sheet series welding device and series welding method |
| CN107153410A (en) * | 2017-07-12 | 2017-09-12 | 上海云统创申智能科技有限公司 | A kind of intelligent sandstone aggregate production line |
| CN107192759A (en) * | 2017-06-09 | 2017-09-22 | 湖南大学 | A kind of photovoltaic cell lossless detection method and system based on sensing optical heat radiation |
| KR20170129535A (en) * | 2016-05-17 | 2017-11-27 | 장관영 | Plating Bumping omitted |
| CN107507885A (en) * | 2017-07-17 | 2017-12-22 | 北京大学 | Manufacture of solar cells process monitoring method based on multichannel sensor data |
| CN107808500A (en) * | 2016-09-08 | 2018-03-16 | 阿自倍尔株式会社 | Monitoring arrangement |
| CN107818312A (en) * | 2017-11-20 | 2018-03-20 | 湖南远钧科技有限公司 | A kind of embedded system based on abnormal behaviour identification |
| CN108038624A (en) * | 2017-12-26 | 2018-05-15 | 北京金风科创风电设备有限公司 | Method and device for analyzing health state of wind turbine generator |
| CN108170801A (en) * | 2017-12-28 | 2018-06-15 | 成都中建材光电材料有限公司 | A kind of manufacture of solar cells equipment management system and its implementation |
| CN108204331A (en) * | 2016-12-19 | 2018-06-26 | 北京金风科创风电设备有限公司 | The fault handling method and device of wind power generating set |
| CN108427297A (en) * | 2018-03-22 | 2018-08-21 | 浙江农林大学暨阳学院 | A kind of intelligent domestic system |
| CN108537347A (en) * | 2018-04-17 | 2018-09-14 | 成都致云科技有限公司 | Information technoloy equipment monitoring system and method |
| CN108596903A (en) * | 2017-09-27 | 2018-09-28 | 广东产品质量监督检验研究院（国家质量技术监督局广州电气安全检验所、广东省试验认证研究院、华安实验室） | A kind of defect inspection method of the black surround and fragment of solar battery sheet |
| US20180287139A1 (en) * | 2017-03-31 | 2018-10-04 | GM Global Technology Operations LLC | Methods for making patterned, thick, silicon-containing electrodes |
| CN108803517A (en) * | 2018-06-22 | 2018-11-13 | 江苏高远智能科技有限公司 | A kind of Intelligentized regulation system and its method of drinks bottle placer production speed |
| CN108878582A (en) * | 2018-06-19 | 2018-11-23 | 成都科微科技有限公司 | Improve the production method of solar panel precision |
| CN109086827A (en) * | 2018-08-10 | 2018-12-25 | 北京百度网讯科技有限公司 | Method and apparatus for detecting monocrystaline silicon solar cell defect |
| CN109084957A (en) * | 2018-08-31 | 2018-12-25 | 华南理工大学 | The defects detection and color sorting process and its system of photovoltaic solar crystal-silicon battery slice |
| CN109103288A (en) * | 2018-10-09 | 2018-12-28 | 黄石金能光伏有限公司 | A kind of photovoltaic module manufacturing method and photovoltaic module |
| CN109175581A (en) * | 2018-10-16 | 2019-01-11 | 苏州辰正太阳能设备有限公司 | Photovoltaic cell efficient welding device and welding procedure |
| CN109244192A (en) * | 2018-10-25 | 2019-01-18 | 宁夏小牛自动化设备有限公司 | It is a kind of to draw weldering integrated equipment with the spare feeding device of half cell piece |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4633099B2 (en) * | 2007-09-28 | 2011-02-16 | シャープ株式会社 | Failure factor estimation method, failure factor estimation device, program, and recording medium |
| JP4568786B2 (en) * | 2009-03-26 | 2010-10-27 | シャープ株式会社 | Factor analysis apparatus and factor analysis method |
| CN105302104A (en) * | 2015-11-24 | 2016-02-03 | 新奥光伏能源有限公司 | Solar cell production line and control system and method thereof |
| CN207586732U (en) * | 2017-08-22 | 2018-07-06 | 正泰集团股份有限公司 | Solar energy electroplax intelligent production system |
| CN109759755A (en) * | 2019-02-28 | 2019-05-17 | 武汉三工智能装备制造有限公司 | AI intelligent process anomalous identification processing system and solar cell chip bonding machine |
-
2019
- 2019-02-28 CN CN201910153814.1A patent/CN109828545B/en active Active
- 2019-03-15 WO PCT/CN2019/078376 patent/WO2020172919A1/en active Application Filing
Patent Citations (51)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1997012718A1 (en) * | 1995-10-06 | 1997-04-10 | Brown University Research Foundation | Soldering methods and compositions |
| WO2004108345A1 (en) * | 2003-06-09 | 2004-12-16 | Senju Metal Industry Co., Ltd. | Solder paste |
| US20070102481A1 (en) * | 2003-06-09 | 2007-05-10 | Rikiya Kato | Solder paste |
| CN1769920A (en) * | 2004-10-16 | 2006-05-10 | 曼兹自动化股份公司 | Test system of solar energy battery |
| US20060103371A1 (en) * | 2004-10-16 | 2006-05-18 | Dieter Manz | Testing system for solar cells |
| CN102365558A (en) * | 2009-02-07 | 2012-02-29 | 拓科学股份有限公司 | High speed detection of shunt defects in photovoltaic and optoelectronic devices |
| JP2011034647A (en) * | 2009-08-04 | 2011-02-17 | Toray Ind Inc | Biaxially oriented polyester film |
| JP2011068807A (en) * | 2009-09-28 | 2011-04-07 | Toray Ind Inc | Biaxially oriented polyester film |
| CN102566475A (en) * | 2010-12-17 | 2012-07-11 | 北京北方微电子基地设备工艺研究中心有限责任公司 | Method and device for processing monitoring alarm and plasma processing device |
| CN102999015A (en) * | 2011-09-16 | 2013-03-27 | 苏州索力旺光伏设备有限公司 | Photovoltaic module flexible manufacturing control system based on field bus |
| CN202295899U (en) * | 2011-09-28 | 2012-07-04 | 山东力诺太阳能电力股份有限公司 | Zero clearing cell storage box for efficiency detection of solar cells |
| CN103071626A (en) * | 2011-10-25 | 2013-05-01 | 英稳达科技股份有限公司 | Error proofing method for sorting of solar cells |
| TW201319213A (en) * | 2011-11-11 | 2013-05-16 | Eternal Chemical Co Ltd | Conductive adhesive composition for use in solar cells and uses thereof |
| CN102544203A (en) * | 2011-12-26 | 2012-07-04 | 嘉兴优太太阳能有限公司 | Monitoring operation system in solar photovoltaic component production line |
| CN103185721A (en) * | 2011-12-31 | 2013-07-03 | 致茂电子股份有限公司 | Optical detection system |
| CN103377094A (en) * | 2012-04-12 | 2013-10-30 | 金蝶软件（中国）有限公司 | Abnormity monitoring method and abnormity monitoring device |
| CN103377094B (en) * | 2012-04-12 | 2016-08-03 | 金蝶软件（中国）有限公司 | Method for monitoring abnormality and device |
| CN102830336A (en) * | 2012-08-23 | 2012-12-19 | 英利能源（中国）有限公司 | Alarming device and alarming method for photovoltaic battery rejected products |
| CN103676790A (en) * | 2012-09-07 | 2014-03-26 | 苏州索力旺光伏设备有限公司 | Photovoltaic pipeline ASI bus control system |
| CN103067230A (en) * | 2013-01-23 | 2013-04-24 | 江苏天智互联科技有限公司 | Method for achieving hyper text transport protocol (http) service monitoring through embedding monitoring code |
| KR20140111069A (en) * | 2013-03-04 | 2014-09-18 | 중앙대학교 산학협력단 | Solar Cell, Mask for fabricating solar cell electrode and method for fabricating the solar cell |
| CN203178203U (en) * | 2013-03-06 | 2013-09-04 | 江南大学 | Automatic solar silicon wafer color detection device based on machine vision |
| CN105759748A (en) * | 2014-12-18 | 2016-07-13 | 中芯国际集成电路制造(上海)有限公司 | Semiconductor production machine hardware performance dynamic monitoring system and monitoring method |
| CN106161122A (en) * | 2015-03-27 | 2016-11-23 | 银联商务有限公司 | A kind of automatization's Centralized Monitoring method for early warning and system |
| CN104753461A (en) * | 2015-04-10 | 2015-07-01 | 福州大学 | Method for diagnosing and classifying faults of photovoltaic power generation arrays on basis of particle swarm optimization support vector machines |
| KR20160126891A (en) * | 2015-04-24 | 2016-11-02 | 주식회사 엘지화학 | Electrode Terminal Welding Device of Battery Cell Assembly Comprising Battery Cells and Welding Process Using the Same |
| CN105262441A (en) * | 2015-09-08 | 2016-01-20 | 西安交通大学 | Infrared image-based photovoltaic array fault grading method |
| CN105491144A (en) * | 2015-12-16 | 2016-04-13 | 新奥光伏能源有限公司 | Solar battery production line and remote control method and system thereof |
| CN105914257A (en) * | 2016-04-26 | 2016-08-31 | 苏州阿特斯阳光电力科技有限公司 | Data analysis based crystalline silica cell production process monitoring method |
| KR20170129535A (en) * | 2016-05-17 | 2017-11-27 | 장관영 | Plating Bumping omitted |
| CN106055932A (en) * | 2016-05-26 | 2016-10-26 | 东莞博力威电池有限公司 | MCU program anti-plagiarizing method and system with Boost loader function |
| CN106229381A (en) * | 2016-08-30 | 2016-12-14 | 奥特斯维能源（太仓）有限公司 | Solar-energy photo-voltaic cell sheet series welding device and series welding method |
| CN107808500A (en) * | 2016-09-08 | 2018-03-16 | 阿自倍尔株式会社 | Monitoring arrangement |
| CN108204331A (en) * | 2016-12-19 | 2018-06-26 | 北京金风科创风电设备有限公司 | The fault handling method and device of wind power generating set |
| US20180287139A1 (en) * | 2017-03-31 | 2018-10-04 | GM Global Technology Operations LLC | Methods for making patterned, thick, silicon-containing electrodes |
| CN107192759A (en) * | 2017-06-09 | 2017-09-22 | 湖南大学 | A kind of photovoltaic cell lossless detection method and system based on sensing optical heat radiation |
| CN107153410A (en) * | 2017-07-12 | 2017-09-12 | 上海云统创申智能科技有限公司 | A kind of intelligent sandstone aggregate production line |
| CN107507885A (en) * | 2017-07-17 | 2017-12-22 | 北京大学 | Manufacture of solar cells process monitoring method based on multichannel sensor data |
| CN108596903A (en) * | 2017-09-27 | 2018-09-28 | 广东产品质量监督检验研究院（国家质量技术监督局广州电气安全检验所、广东省试验认证研究院、华安实验室） | A kind of defect inspection method of the black surround and fragment of solar battery sheet |
| CN107818312A (en) * | 2017-11-20 | 2018-03-20 | 湖南远钧科技有限公司 | A kind of embedded system based on abnormal behaviour identification |
| CN108038624A (en) * | 2017-12-26 | 2018-05-15 | 北京金风科创风电设备有限公司 | Method and device for analyzing health state of wind turbine generator |
| CN108170801A (en) * | 2017-12-28 | 2018-06-15 | 成都中建材光电材料有限公司 | A kind of manufacture of solar cells equipment management system and its implementation |
| CN108427297A (en) * | 2018-03-22 | 2018-08-21 | 浙江农林大学暨阳学院 | A kind of intelligent domestic system |
| CN108537347A (en) * | 2018-04-17 | 2018-09-14 | 成都致云科技有限公司 | Information technoloy equipment monitoring system and method |
| CN108878582A (en) * | 2018-06-19 | 2018-11-23 | 成都科微科技有限公司 | Improve the production method of solar panel precision |
| CN108803517A (en) * | 2018-06-22 | 2018-11-13 | 江苏高远智能科技有限公司 | A kind of Intelligentized regulation system and its method of drinks bottle placer production speed |
| CN109086827A (en) * | 2018-08-10 | 2018-12-25 | 北京百度网讯科技有限公司 | Method and apparatus for detecting monocrystaline silicon solar cell defect |
| CN109084957A (en) * | 2018-08-31 | 2018-12-25 | 华南理工大学 | The defects detection and color sorting process and its system of photovoltaic solar crystal-silicon battery slice |
| CN109103288A (en) * | 2018-10-09 | 2018-12-28 | 黄石金能光伏有限公司 | A kind of photovoltaic module manufacturing method and photovoltaic module |
| CN109175581A (en) * | 2018-10-16 | 2019-01-11 | 苏州辰正太阳能设备有限公司 | Photovoltaic cell efficient welding device and welding procedure |
| CN109244192A (en) * | 2018-10-25 | 2019-01-18 | 宁夏小牛自动化设备有限公司 | It is a kind of to draw weldering integrated equipment with the spare feeding device of half cell piece |
Non-Patent Citations (5)
| Title |
|---|
| SEYEDKAZEM HOSSEINI,: ""Determination of Photovoltaics Characteristics in Real Field Conditions"", 《IEEE JOURNAL OF PHOTOVOLTAICS》 * |
| 华孟迪: ""光伏并网发电系统中孤岛效应检测与容错技术应用研究"", 《中国优秀硕士学位论文全文数据库-工程科技II辑》 * |
| 吴世伟: ""新能源发电功率预测系统数据流容错研究"", 《电气技术》 * |
| 郑军: ""太阳能电池组件测试曲线异常分析"", 《科技与企业》 * |
| 陈军: ""硅太阳能电池制备过程的质量控制策略"", 《机械设计与制造》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111626546A (en) * | 2020-04-07 | 2020-09-04 | 青岛奥利普自动化控制系统有限公司 | Exception management system (MES) -based exception management method and equipment |
| CN113642683A (en) * | 2021-10-14 | 2021-11-12 | 深圳市信润富联数字科技有限公司 | Prompting method and device, electronic equipment and computer readable storage medium |
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| WO2020172919A1 (en) | 2020-09-03 |
| CN109828545B (en) | 2020-09-11 |
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