CN109828545B - AI intelligent process anomaly identification closed-loop control method, host and equipment system - Google Patents
AI intelligent process anomaly identification closed-loop control method, host and equipment system Download PDFInfo
- Publication number
- CN109828545B CN109828545B CN201910153814.1A CN201910153814A CN109828545B CN 109828545 B CN109828545 B CN 109828545B CN 201910153814 A CN201910153814 A CN 201910153814A CN 109828545 B CN109828545 B CN 109828545B
- Authority
- CN
- China
- Prior art keywords
- state
- related data
- abnormal
- data
- solar cell
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 106
- 230000002159 abnormal effect Effects 0.000 claims abstract description 139
- 238000004519 manufacturing process Methods 0.000 claims abstract description 53
- 238000012545 processing Methods 0.000 claims description 19
- 230000008439 repair process Effects 0.000 claims description 14
- 238000003466 welding Methods 0.000 claims description 13
- 238000009826 distribution Methods 0.000 claims description 11
- 239000011521 glass Substances 0.000 claims description 5
- 239000000758 substrate Substances 0.000 claims description 5
- 238000005507 spraying Methods 0.000 claims description 4
- 230000002547 anomalous effect Effects 0.000 claims 1
- 238000005304 joining Methods 0.000 claims 1
- 230000000694 effects Effects 0.000 abstract description 11
- 238000013473 artificial intelligence Methods 0.000 description 44
- 230000005856 abnormality Effects 0.000 description 28
- 238000010586 diagram Methods 0.000 description 8
- 238000004891 communication Methods 0.000 description 5
- 238000010924 continuous production Methods 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000003860 storage Methods 0.000 description 2
- 239000012634 fragment Substances 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 238000003892 spreading Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Factory Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Photovoltaic Devices (AREA)
Abstract
The invention discloses an AI intelligent process anomaly identification closed-loop control method, a host and an equipment system, wherein the AI intelligent process anomaly identification closed-loop control method comprises the following steps: receiving state related data of the solar cell in real time; comparing the state related data with normal state data, and judging the state of the solar cell; when the solar cell is in an abnormal state, matching the state related data with an abnormal grade database to obtain a first abnormal grade; and performing a corresponding exception handling strategy according to the first exception level. The invention intelligently monitors the state of the cell on the solar cell assembly production line in real time, automatically guides and controls the operation of the solar cell assembly production line according to different states of the cell, reduces artificial participation, improves the production efficiency of the solar cell assembly production line, improves the yield of products and has better effect.
Description
Technical Field
The invention relates to the technical field of solar photovoltaic module manufacturing, in particular to an AI intelligent process anomaly identification closed-loop control method, a host and an equipment system.
Background
At present, solar photovoltaic manufacturing and application are rapidly and rapidly developed, China gradually becomes an international main supplier in the field of photovoltaic solar panel manufacturing equipment in recent years, and particularly, enterprises with leading high-speed automatic welding equipment are in China. However, in the field of high-speed welding automation, no method is available for forming fast and efficient closed-loop control on process abnormity, and personnel intervention is required for on-line judgment, identification, marking and the like. Staff is required to carry out screening and distinguishing in the subsequent repair process. With the further increase of the welding speed of the equipment, particularly after the current 2 times or 3 times of welding speed is reached, the difficulty is very high by the conventional personnel on-line operation. New designs must be found to address this. As a process, the hidden danger is brought to the manufacturing process and the product mainly by staff judgment or random sampling. Operators actually have individual differences of working enthusiasm and physical differences, so that various phenomena of missing judgment, erroneous judgment, operation errors and the like exist, and the production and manufacturing of the whole product are influenced by the yield. The continuous production of the equipment is also affected by the need of people to rest, eat and the like. The line stop will result in reduced output, wasting later personnel waiting time and cost. Arranging continuous production random sampling may face the possibility of missed inspection or the risk of small-lot bad inspection.
In the current field of photovoltaic solar panel manufacturing, the defects of the actual automatic welding process are required to be repaired, which is formed by various factors such as raw materials, processes, equipment and so on, so that the repair is also a process challenge which is required to be faced by each family. At present, a repair operator needs to face a large number of repetitive work tasks, and the marking and distinguishing of each abnormal unit are important. The actual current online judgment and operation of operators of automatic welding procedures are completed, but the online speed is high, the operation is inconvenient, the marks are difficult to refine and be accurate, and further screening and re-identification of repair workers are needed.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide an AI (Artificial Intelligence) process anomaly identification closed-loop control method, a host and an equipment system, and aims to solve the problems of low yield and low production efficiency caused by untimely, difficult and rapid identification and rapid marking in the manual operation process in the prior art.
In order to achieve the above object, the present invention provides an AI intelligent process anomaly identification closed-loop control method, a host and an equipment system, wherein the AI intelligent process anomaly identification closed-loop control method comprises the following steps:
receiving state related data of the solar cell in real time;
comparing the state related data with normal state data, and judging the state of the solar cell;
when the solar cell is in an abnormal state, matching the state related data with an abnormal grade database to obtain a first abnormal grade;
and performing a corresponding exception handling strategy according to the first exception level.
Optionally, the first anomaly level comprises a severe anomaly, a major anomaly, and a minor anomaly;
according to the first exception level, the corresponding exception handling strategy is carried out:
when the first exception level is a serious exception, the exception handling strategy comprises a shutdown information generating instruction;
when the first exception level is a primary exception, the exception handling policy comprises:
counting first frequency data of the state related data in a first time period;
calculating a first fault-tolerant frequency in the first time period according to the risk probability of serious accidents occurring to internal equipment and/or products prestored in the abnormal grade database;
comparing the first frequency data with the first fault-tolerant frequency to obtain a second abnormal level of the state-related data;
generating second exception handling information through the second exception level, wherein the second exception handling information comprises a shutdown information generating instruction;
when the first exception level is a minor exception, the exception handling policy comprises:
counting second frequency data of the state related data in a second time period;
calculating a second fault-tolerant frequency in the second time period according to the repeated batch accident probability of the internal equipment and/or products prestored in the abnormal grade database;
comparing the second frequency data with the second fault-tolerant frequency to obtain a third anomaly level of the state-related data;
and generating third anomaly processing information through the third anomaly level, wherein the third anomaly processing information comprises a warning alarm information generating instruction.
Optionally, after the step of comparing the state-related data with the normal state data and determining the state of the solar cell, the method further includes:
when the solar cell is in an abnormal state, matching the state related data with an abnormal level database, and when the matching is unsuccessful, counting frequency data of the state related data;
comparing the frequency data with fault-tolerant frequency to obtain a fourth abnormal grade of the state-related data;
and generating fourth exception handling information through the fourth exception level.
Optionally, after the step of obtaining a fourth abnormal level of the state-related data by comparing the frequency data with the fault tolerance frequency, the method further includes:
when the frequency data is greater than the fault-tolerant frequency, storing the state related data into the abnormal level database, and updating the abnormal level database;
and when the frequency data is less than the fault-tolerant frequency, recording the state related data, and periodically updating the state related data to the abnormal grade database.
The present invention also provides a control host, including: the closed-loop control system comprises a memory, a processor and an AI intelligent process abnormality recognition closed-loop control program stored on the memory and operable on the processor, wherein the AI intelligent process abnormality recognition closed-loop control program is configured to implement the steps of an AI intelligent process abnormality recognition closed-loop control method, and the AI intelligent process abnormality recognition closed-loop control method comprises the following steps:
receiving state related data of the solar cell in real time;
comparing the state related data with normal state data, and judging the state of the solar cell;
when the solar cell is in an abnormal state, matching the state related data with an abnormal grade database to obtain a first abnormal grade;
and performing a corresponding exception handling strategy according to the first exception level.
The invention also provides an AI intelligent process anomaly identification closed-loop control equipment system which is used for a solar cell assembly production line and comprises a control host, an identification assembly and an execution assembly, wherein the identification assembly and the execution assembly are electrically connected with the control host, and the AI intelligent process anomaly identification closed-loop control equipment system comprises:
the identification component is used for acquiring the sheet state related data of the solar cell and sending the state related data to the control host;
the execution component is used for performing exception handling after receiving first exception handling information of the control host;
the control host includes: the closed-loop control system comprises a memory, a processor and an AI intelligent process abnormality recognition closed-loop control program stored on the memory and operable on the processor, wherein the AI intelligent process abnormality recognition closed-loop control program is configured to implement the steps of an AI intelligent process abnormality recognition closed-loop control method, and the AI intelligent process abnormality recognition closed-loop control method comprises the following steps:
receiving state related data of the solar cell in real time;
comparing the state related data with normal state data, and judging the state of the solar cell;
when the solar cell is in an abnormal state, matching the state related data with an abnormal grade database to obtain a first abnormal grade;
and performing a corresponding exception handling strategy according to the first exception level.
Optionally, the identification component comprises a plurality of identification sensors, and the plurality of identification sensors comprise an image identification sensor, a temperature sensor and a photoelectric sensor.
Optionally, the solar cell module production line is provided with a plurality of stations, and the plurality of stations comprise a material taking station, a transmission station, a sheet laying station, a series welding station, a confluence welding station and a typesetting station;
the plurality of identification sensors are correspondingly distributed at the plurality of stations and used for acquiring state related data of the solar cells corresponding to the stations.
Optionally, the system further comprises a marking component electrically connected with the control host;
the control host is also used for generating abnormal distribution data information and mark information through the state related data and sending the mark information to the mark component, wherein the abnormal distribution data information is used for guiding the operation of repair personnel;
the marking assembly is used for receiving the marking information and marking the abnormal solar cell.
Optionally, the marking assembly includes a laser coder for performing code spraying processing on the abnormal solar cell and/or the glass substrate of the solar cell assembly.
According to the invention, the state related data of the solar cell is received in real time, the state related data is compared with the normal state data, the state of the solar cell is judged, when the solar cell is in an abnormal state, the state related data is matched with the abnormal level database to obtain a first abnormal level, a corresponding abnormal processing strategy is carried out according to the first abnormal level, the state of the solar cell on the solar cell assembly production line can be intelligently monitored in real time, the operation of the solar cell assembly production line is automatically guided and controlled according to different states of the solar cell, the artificial participation is reduced, the production efficiency of the solar cell assembly production line is improved, the yield of products is also improved, and the solar cell assembly production line has a better effect.
Drawings
FIG. 1 is a schematic diagram of a server architecture of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a first embodiment of the AI intelligent process anomaly identification closed-loop control method according to the present invention;
FIG. 3 is a flowchart illustrating a closed-loop control method for AI intelligent process anomaly identification according to a second embodiment of the present invention;
FIG. 4 is a flowchart illustrating a third embodiment of the AI intelligent process anomaly identification closed-loop control method according to the present invention;
FIG. 5 is a schematic flow chart illustrating a fourth embodiment of the AI intelligent process anomaly identification closed-loop control method according to the present invention;
FIG. 6 is a schematic structural diagram of an AI intelligence process anomaly identification closed-loop control system in accordance with an embodiment of the present invention;
FIG. 7 is a schematic structural diagram of an embodiment of a solar module production line (partial structure) to which the AI intelligent process anomaly identification closed-loop control system of FIG. 6 is applied according to the present invention;
FIG. 8 is a block diagram illustrating an embodiment of an identification component of the AI intelligence process anomaly identification closed-loop control system of FIG. 6;
FIG. 9 is a block diagram illustrating an embodiment of a tagging component of the AI intelligence process anomaly identification closed-loop control system of FIG. 6.
The reference numbers illustrate:
| reference numerals | Name (R) | Reference numerals | Name (R) |
| 1000 | AI intelligent process anomaly identification closed-loop |
20 | Identification sensor |
| a | Solar cell |
300 | |
| 100 | |
400 | |
| 200 | |
40 | Laser code printer |
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a control host according to the present invention.
As shown in fig. 1, the control host may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation on the control master, and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, the memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a control program of the AI intelligent process abnormality recognition closed-loop control host 100.
In the server shown in fig. 1, the network interface 1004 is mainly used for connecting a terminal device and performing data communication with the terminal device; the user interface 1003 is mainly used for receiving input instructions of an administrator; the server calls the control program of the AI intelligence process anomaly recognition closed-loop control host 100 stored in the memory 1005 through the processor 1001, and performs the following operations:
receiving state related data of the solar cell in real time;
comparing the state related data with normal state data, and judging the state of the solar cell;
when the solar cell is in an abnormal state, matching the state related data with an abnormal grade database to obtain a first abnormal grade;
and performing a corresponding exception handling strategy according to the first exception level.
Further, the processor 1001 may call the control program of the AI intelligence process anomaly recognition closed-loop control host 100 stored in the memory 1005, and also perform the following operations:
the first anomaly level comprises a severe anomaly, a primary anomaly, and a secondary anomaly;
according to the first exception level, the corresponding exception handling strategy is carried out:
when the first exception level is a serious exception, the exception handling strategy comprises a shutdown information generating instruction;
when the first exception level is a primary exception, the exception handling policy comprises:
counting first frequency data of the state related data in a first time period;
calculating a first fault-tolerant frequency in the first time period according to the risk probability of serious accidents occurring to internal equipment and/or products prestored in the abnormal grade database;
comparing the first frequency data with the first fault-tolerant frequency to obtain a second abnormal level of the state-related data;
generating second exception handling information through the second exception level, wherein the second exception handling information comprises a shutdown information generating instruction;
when the first exception level is a minor exception, the exception handling policy comprises:
counting second frequency data of the state related data in a second time period;
calculating a second fault-tolerant frequency in the second time period according to the repeated batch accident probability of the internal equipment and/or products prestored in the abnormal grade database;
comparing the second frequency data with the second fault-tolerant frequency to obtain a third anomaly level of the state-related data;
and generating third anomaly processing information through the third anomaly level, wherein the third anomaly processing information comprises a warning alarm information generating instruction.
Further, the processor 1001 may call the control program of the AI intelligence process anomaly recognition closed-loop control host 100 stored in the memory 1005, and also perform the following operations:
after the step of comparing the state-related data with the normal state data and determining the state of the solar cell, the method further comprises the following steps:
when the solar cell is in an abnormal state, matching the state related data with an abnormal level database, and when the matching is unsuccessful, counting frequency data of the state related data;
comparing the frequency data with fault-tolerant frequency to obtain a fourth abnormal grade of the state-related data;
and generating fourth exception handling information through the fourth exception level.
Further, the processor 1001 may call the control program of the AI intelligence process anomaly recognition closed-loop control host 100 stored in the memory 1005, and also perform the following operations:
after the step of obtaining a fourth abnormal level of the state-related data by comparing the frequency data with the fault-tolerant frequency, the method further comprises:
when the frequency data is greater than the fault-tolerant frequency, storing the state related data into the abnormal level database, and updating the abnormal level database;
and when the frequency data is less than the fault-tolerant frequency, recording the state related data, and periodically updating the state related data to the abnormal grade database.
According to the invention, the state related data of the solar cell is received in real time, the state related data is compared with the normal state data, the state of the solar cell is judged, when the solar cell is in an abnormal state, the state related data is matched with the abnormal level database to obtain a first abnormal level, a corresponding abnormal processing strategy is carried out according to the first abnormal level, the state of the solar cell on the solar cell assembly production line can be intelligently monitored in real time, the operation of the solar cell assembly production line is automatically guided and controlled according to different states of the solar cell, the artificial participation is reduced, the production efficiency of the solar cell assembly production line is improved, the yield of products is also improved, and the solar cell assembly production line has a better effect.
Based on the above hardware structure, fig. 2 to 6 are embodiments of the AI intelligent process anomaly identification closed-loop control method provided by the present invention.
Referring to fig. 2, fig. 2 is a schematic flowchart of a first embodiment of an AI intelligent process abnormality identification closed-loop control method according to the present invention, in this embodiment, the AI intelligent process abnormality identification closed-loop control method includes the following steps:
step S10, receiving the state related data of the solar cell in real time;
it should be noted that, in the whole production flow of the solar cell module, the state-related data of the solar cell includes image data, temperature data, welding forming quality data, positioning data, and other related data, which are all related to the quality of the solar cell.
Step S20, comparing the state-related data with normal state data, and judging the state of the solar cell;
it should be noted that the normal state data of the solar cell is stored in the control system, and includes image data, temperature data, welding forming quality data, positioning data and other related data of the normal cell, after the state related data of the solar cell is received in real time, the state related data is compared with the normal state data, the state of the solar cell is determined, and if the state is the normal state, the detected solar cell is in the normal state, and the whole production line runs normally.
Step S30, when the solar cell is in an abnormal state, matching the state related data with an abnormal level database to obtain a first abnormal level;
it should be noted that an abnormal level database is established in the system, the abnormal level database classifies different abnormal defects, and performs abnormal level classification according to different categories and a preset rule, for example, in the form of a mapping table, if the detected solar cell is in an abnormal state, the whole system determines that the detected solar cell is in an abnormal state, searches for an abnormal category matched with the abnormal state, and further obtains the corresponding abnormal level.
Step S40, according to the first abnormal level, corresponding abnormal processing strategy is carried out;
it should be noted that, in order to enable the whole solar cell module production line a to realize efficient production, different exception handling strategies need to be selected according to different exception grades, different exceptions need to be handled differently, and finer control can be obtained.
According to the invention, the state related data of the solar cell is received in real time, the state related data is compared with the normal state data, the state of the solar cell is judged, when the solar cell is in an abnormal state, the state related data is matched with the abnormal level database to obtain a first abnormal level, a corresponding abnormal processing strategy is carried out according to the first abnormal level, the state of the solar cell on the solar cell assembly production line a can be intelligently monitored in real time, the operation of the solar cell assembly production line a is automatically guided and controlled according to different states of the solar cell, artificial participation is reduced, the production efficiency of the solar cell assembly production line a is improved, the yield of products is also improved, and a better effect is achieved.
Referring to fig. 3, fig. 3 is a flowchart illustrating a second embodiment of the AI intelligent process anomaly identification closed-loop control method according to the present invention, in this embodiment, the first anomaly level includes a severe anomaly, a primary anomaly, and a secondary anomaly, and in the step of performing a corresponding anomaly handling policy according to the first anomaly level:
step S40a, when the first exception level is a serious exception, the exception handling strategy comprises a shutdown information generating instruction;
it should be noted that the first anomaly level is classified into three levels of a serious anomaly, a major anomaly and a minor anomaly, and of course, for more finely controlling the production line, the first anomaly level may be further classified into a plurality of anomaly levels, so that the monitoring of the production line is more finely performed, for example, when a serious fragment occurs and the area is large, the production quality of the whole product is seriously affected, and the major anomaly is determined as the serious anomaly, and when only a slight scratch occurs, the production quality of the whole product is not affected or the effect is small, and the minor anomaly is determined.
Step S40b, when the first exception level is a main exception, the exception handling strategy comprises a first step of counting first frequency data appearing in the state-related data in a first time period;
it should be noted that, although the first anomaly level is a main anomaly, if the frequency of occurrence of the anomaly of the main anomaly is too high, which often represents that a serious failure occurs in equipment or a serious failure occurs in a material, the production quality of the whole product is also seriously affected, and at this time, first frequency data of occurrence of the state-related data in the first time period needs to be counted to further judge.
Step S50b, calculating a first fault-tolerant frequency in the first time period according to the risk probability of serious accidents occurring to internal equipment and/or products prestored in the abnormal grade database;
it should be noted that, data of the risk probability of serious accident occurrence of internal devices and/or products is stored in the abnormal grade database, and a first fault-tolerant frequency of abnormal occurrence in effective working time can be calculated according to the probability, and the first fault-tolerant frequency is used as a reference for judgment, for example, within a period of time, the frequency of the abnormal state data of the secondary abnormality is N, and if the frequency exceeds the fault-tolerant frequency by N times, the abnormal state data is corresponding to an abnormal grade, which is convenient for comparison and judgment;
step S60b, comparing the first frequency data with the first fault-tolerant frequency to obtain a second abnormal level of the state-related data;
it should be noted that, for example, within a period of time, the frequency of the abnormal state data of the main abnormality is N times, and if the frequency exceeds this fault-tolerant frequency by N times, the abnormal state data corresponds to one abnormality level, and when the frequency is within N times of the fault-tolerant frequency, the abnormal state data corresponds to another abnormality level, and the abnormal state data can be handled differently according to different abnormality levels.
Step S70b, the second exception handling information comprises a stop information generating instruction;
it should be noted that after the second exception level of the state-related data is determined, second exception handling information is generated through the second exception level, such as shutdown or continuous production.
Step S40c, when the first exception level is a minor exception, the exception handling policy includes a first step of counting second frequency data occurring in the state-related data within a second time period;
it should be noted that, although the first anomaly level is a secondary anomaly, if the frequency of the occurrence of the secondary anomaly is too high, the production quality of the whole product is also affected, and at this time, the frequency data of the occurrence of the state-related data needs to be counted for further judgment.
Step S50c, calculating a second fault-tolerant frequency in the second time period according to the repeated batch accident probability of the internal equipment and/or products pre-stored in the abnormal level database;
it should be noted that, data of the probability of occurrence of repeated batch accidents of internal devices and/or products is stored in the abnormal level database, and a second fault-tolerant frequency of abnormal occurrence in effective working time can be calculated according to the probability, and the second fault-tolerant frequency is used as a reference for judgment, for example, in a period of time, the frequency of the abnormal state data of secondary abnormality is N, which is convenient for comparison and judgment;
step S60c, comparing the second frequency data with the second fault-tolerant frequency to obtain a third anomaly level of the state-related data;
it should be noted that, for example, within a period of time, the frequency of the abnormal state data of the secondary abnormality is N times, and if the frequency exceeds this fault-tolerant frequency by N times, the abnormal state data corresponds to one abnormality level, and when the frequency is within N times of the fault-tolerant frequency, the abnormal state data corresponds to another abnormality level, and the abnormal state data can be handled differently according to different abnormality levels.
Step S70c, generating third anomaly processing information through the third anomaly level, wherein the third anomaly processing information comprises a warning alarm information generating instruction;
after the third anomaly level of the state-related data is determined, third anomaly handling information, such as a warning alarm, is generated through the third anomaly level, so that an operator is reminded of timely intervention, and details of anomalies are found out.
In the invention, after the first abnormal grade of the state-related data is considered, the second factor which influences the product quality and efficiency of the whole production line, namely the frequency data of the state-related data is considered comprehensively, the two factors are considered comprehensively, the yield and the production efficiency of the product are obviously improved, and the effect is better.
Referring to fig. 4, fig. 4 is a schematic flow chart of a third embodiment of the AI intelligence process anomaly identification closed-loop control method according to the present invention, in this embodiment, after the step of comparing the state-related data with the normal state data and determining the state of the solar cell, the method further includes:
step S80, when the solar cell is in an abnormal state, matching the state related data with an abnormal level database, and when the matching is unsuccessful, counting the frequency data of the state related data;
it should be noted that, in this embodiment, the abnormal level database is different quality defect data that is recorded by an operator according to experience in production, the abnormal level database is a continuously improved database, and when a situation that matching between the state-related data and the abnormal level database is unsuccessful occurs, frequency data also needs to be considered at this time to further determine.
Step S90, comparing the frequency data with fault-tolerant frequency to obtain a second abnormal grade of the state-related data;
it should be noted that, a fault-tolerant principle and a fourth exception level need to be set in the system, for example, a fault-tolerant frequency is set, for example, the frequency of the abnormal state data of the secondary exception is N times within a period of time, if the frequency exceeds the fault-tolerant frequency by N times, the abnormal state data corresponds to an exception level, and when the frequency is within N times of the fault-tolerant frequency, the abnormal state data corresponds to another exception level, the abnormal state data can be handled differently according to different exception levels.
And step S100, generating fourth exception handling information through the fourth exception level.
It should be noted that after the fourth exception level of the state-related data is determined, fourth exception handling information is generated through the fourth exception level, such as shutdown or continuous production, or a system dialog box is directly skipped, and an operator is required to perform an intervention operation.
In the invention, when the state related data does not belong to the abnormal type in the abnormal grade database, the frequency data of the state related data is considered, and if necessary, an operator intervenes in operation, so that the yield and the production efficiency of the product are obviously improved, and a better effect is achieved.
Referring to fig. 5, fig. 5 is a schematic flow chart of a fourth embodiment of the AI intelligence process anomaly identification closed-loop control method according to the present invention, in this embodiment, after the step of obtaining a second anomaly level of the state-related data by comparing the frequency data with the fault-tolerant frequency, the method further includes:
step S100a, when the frequency data is greater than the fault-tolerant frequency, storing the state related data into the abnormal level database, and updating the abnormal level database;
it should be noted that when the frequency data is greater than the fault-tolerant frequency, it indicates that the state-related data is important, and at this time, a person is prompted to participate in the judgment, and the state-related data is stored in the abnormal level database, and the abnormal level database is updated, so that the database is more complete.
And step S100b, when the frequency data is less than the fault-tolerant frequency, recording the state related data, and periodically updating the state related data to the abnormal level database.
It should be noted that when the frequency data is less than the fault-tolerant frequency, it indicates that the state-related data is not important, and the system records the state-related data and periodically updates the state-related data to the abnormal level database.
In the invention, the abnormal grade database is required to be continuously improved, when the frequency data is greater than the fault-tolerant frequency, the state related data is stored in the abnormal grade database and is updated, when the frequency data is less than the fault-tolerant frequency, the state related data is recorded and is periodically updated to the abnormal grade database, and the abnormal grade database is timely updated under two different conditions of emergency and non-emergency, so that the whole system is continuously learned and self-improved, the yield and the production efficiency of products are continuously improved, and the method has a better effect.
Fig. 6 is a schematic structural diagram of an embodiment of the AI intelligent process abnormality identification closed-loop control equipment system provided by the present invention.
Referring to fig. 6 to 9, the AI intelligent process anomaly identification closed-loop control equipment system 1000 is used for a solar cell module production line a, and includes a control host 100, and an identification module 200 and an execution module 300 that are electrically connected to the control host 100, where the identification module 200 is configured to obtain data related to a sheet state of a solar cell and send the data related to the state to the control host 100, and the execution module 300 is configured to perform anomaly processing after receiving first anomaly processing information of the control host 100, where the control host 100 includes all the technical solutions described above, and therefore, the AI intelligent process anomaly identification closed-loop control equipment system 1000 also includes all the technical solutions described above, and also has technical effects brought by the technical solutions described above, and details are not repeated here.
It should be noted that the control host 100 is equivalent to a control end of the whole AI intelligent process abnormality identification closed-loop control equipment system 1000, the identification component 200 is equivalent to an input end of the whole AI intelligent process abnormality identification closed-loop control equipment system 1000, the execution component 300 is equivalent to an output end of the whole AI intelligent process abnormality identification closed-loop control equipment system 1000, the input end collects relevant state-related data of the relevant solar cell and transmits the data to the control end, and the control end performs relevant processing and transmits the data to the output end for execution.
The identification component 200 is equivalent to an input end of the whole AI intelligent process anomaly identification closed-loop control equipment system 1000, and mainly collects state-related data of the solar cell, specifically, in this embodiment, the identification component 200 includes a plurality of identification sensors 20, and the plurality of identification sensors 20 includes an image identification sensor 20, a temperature sensor, and a photoelectric sensor, and respectively collects quality data of an image, a temperature, a positioning condition, and the like of the solar cell, and transmits the state-related data to the control host 100.
In addition, a plurality of stations have on the solar module production line a, it is a plurality of the station is including getting material station, transmission station, piece-spreading station, stringer station, converging welding station and composing station, it is a plurality of identification sensor 20 corresponds and distributes in a plurality of station department is used for acquireing and corresponds station department the relevant data of state of solar wafer sets up respectively station department of difference identification component 200 can understand comprehensively in the whole production process the relevant data of state of solar wafer can monitor comprehensively solar module's production quality, the problem of appearing in time this adopts different processing strategies to handle, has reduced artificial participation, has better effect.
It should be noted that, in this embodiment, the specific form of the executing component 300 is not limited, and for example, the executing component may be a controller that controls an operation of an active motor on a production line, and may also be a marking component 400 that marks an abnormal solar cell, specifically, the AI intelligent process abnormality identification closed-loop control equipment system 1000, and is characterized by further including a marking component 400 that is electrically connected to the control host 100, the control host 100 is further configured to generate, according to the state-related data, abnormality distribution data information and marking information, and send the marking information to the marking component 400, where the abnormality distribution data information is used to guide a repair worker to work, the marking component 400 is configured to receive the marking information and mark the abnormal solar cell, and the marking information is convenient for the repair worker to identify, and the abnormal distribution data information is used for guiding a repair worker to search the abnormal solar cell.
In this embodiment, the marking assembly 400 includes a laser code printer 40, and is configured to perform code spraying processing on the abnormal solar cell and/or the glass substrate of the solar cell assembly, mark the abnormal solar cell according to a certain coding rule, record information such as an abnormal position and an abnormal type, and perform code spraying processing on the glass substrate of the solar cell assembly for a large solar cell assembly, so as to guide a repair worker to perform work, thereby significantly improving the repair work efficiency, and achieving a better effect.
In addition, it should be noted that the abnormal distribution data information is associated with the bar code on each solar cell module glass substrate and is stored in the MES system, the module bar code is identified at the repair station, the abnormal distribution data information is automatically called through the MES system and is displayed through the display, the repair station operator can identify the abnormal distribution data information on the abnormal cell piece on one hand, and identify the abnormal distribution data information through the display on the other hand, and the two diagrams are compared, so that the method is more visual and simple, the fool-proof design is realized, the repair of the operator is facilitated, and the working efficiency is improved.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (9)
1. An AI intelligent process anomaly identification closed-loop control method is used for a solar cell module production line and is characterized by comprising the following steps:
receiving state related data of the solar cell in real time;
comparing the state related data with normal state data, and judging the state of the solar cell;
when the solar cell is in an abnormal state, matching the state related data with an abnormal grade database to obtain a first abnormal grade;
performing a corresponding exception handling strategy according to the first exception level;
the first anomaly level comprises a severe anomaly, a primary anomaly, and a secondary anomaly;
according to the first exception level, the corresponding exception handling strategy is carried out:
when the first exception level is a serious exception, the exception handling strategy comprises a shutdown information generating instruction;
when the first exception level is a primary exception, the exception handling policy comprises:
counting first frequency data of the state related data in a first time period;
calculating a first fault-tolerant frequency in the first time period according to the risk probability of serious accidents occurring to internal equipment and/or products prestored in the abnormal grade database;
comparing the first frequency data with the first fault-tolerant frequency to obtain a second abnormal level of the state-related data;
generating second exception handling information through the second exception level, wherein the second exception handling information comprises a shutdown information generating instruction;
when the first exception level is a minor exception, the exception handling policy comprises:
counting second frequency data of the state related data in a second time period;
calculating a second fault-tolerant frequency in the second time period according to the repeated batch accident probability of the internal equipment and/or products prestored in the abnormal grade database;
comparing the second frequency data with the second fault-tolerant frequency to obtain a third anomaly level of the state-related data;
and generating third anomaly processing information through the third anomaly level, wherein the third anomaly processing information comprises a warning alarm information generating instruction.
2. The AI intelligence process anomaly identification closed-loop control method of claim 1, wherein after the step of comparing the state-related data with normal state data to determine the state of the solar cell, further comprising:
when the solar cell is in an abnormal state, matching the state related data with an abnormal level database, and when the matching is unsuccessful, counting frequency data of the state related data;
comparing the frequency data with fault-tolerant frequency to obtain a fourth abnormal grade of the state-related data;
and generating fourth exception handling information through the fourth exception level.
3. The AI intelligence process anomaly identification closed-loop control method of claim 2, wherein after the step of obtaining a fourth anomaly level for the status-related data by comparing the frequency data to a fault tolerance frequency, further comprising:
when the frequency data is greater than the fault-tolerant frequency, storing the state related data into the abnormal level database, and updating the abnormal level database;
and when the frequency data is less than the fault-tolerant frequency, recording the state related data, and periodically updating the state related data to the abnormal grade database.
4. A control host, characterized by comprising a memory, a processor and an AI intelligent process anomaly identification closed-loop control program stored on the memory and operable on the processor, the AI intelligent process anomaly identification closed-loop control program being configured to implement the steps of the AI intelligent process anomaly identification closed-loop control method according to any one of claims 1 to 3.
5. The utility model provides an AI intelligence process anomaly identification closed-loop control equipment system for solar module production line, its characterized in that, including the main control system, and with main control system electric connection's discernment subassembly and executive module, wherein:
the identification component is used for acquiring the sheet state related data of the solar cell and sending the state related data to the control host;
the control host is configured as a control host as claimed in claim 4;
and the execution component is used for performing exception handling after receiving the first exception handling information of the control host.
6. The AI intelligence process anomaly identification closed-loop control equipment system of claim 5, wherein the identification component includes a plurality of identification sensors including an image identification sensor, a temperature sensor, a photosensor.
7. The AI intelligence process anomaly identification closed-loop control equipment system of claim 6, wherein a plurality of stations are provided on the solar module production line, the plurality of stations including a take-off station, a transport station, a sheeting station, a series welding station, a flow-joining welding station, and a typesetting station;
the plurality of identification sensors are correspondingly distributed at the plurality of stations and used for acquiring state related data of the solar cells corresponding to the stations.
8. The AI intelligence process anomaly identification closed-loop control equipment system of claim 5, further comprising a tagging component electrically connected to the control host;
the control host is also used for generating abnormal distribution data information and mark information through the state related data and sending the mark information to the mark component, wherein the abnormal distribution data information is used for guiding the operation of repair personnel;
the marking assembly is used for receiving the marking information and marking the abnormal solar cell.
9. The AI intelligence process anomaly identification closed-loop control equipment system of claim 8, wherein the marking component includes a laser code printer for code-spraying the solar cell sheet that is anomalous and/or the glass substrate of the solar cell module.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| 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 |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910153814.1A CN109828545B (en) | 2019-02-28 | 2019-02-28 | AI intelligent process anomaly identification closed-loop control method, host and equipment system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109828545A CN109828545A (en) | 2019-05-31 |
| CN109828545B true CN109828545B (en) | 2020-09-11 |
Family
ID=66864969
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910153814.1A Active CN109828545B (en) | 2019-02-28 | 2019-02-28 | AI intelligent process anomaly identification closed-loop control method, host and equipment system |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN109828545B (en) |
| WO (1) | WO2020172919A1 (en) |
Families Citing this family (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 (43)
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
Family Cites Families (10)
| 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 |
| CN104753461B (en) * | 2015-04-10 | 2017-04-12 | 福州大学 | Method for diagnosing and classifying faults of photovoltaic power generation arrays on basis of particle swarm optimization support vector machines |
| 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 |
| KR20170129535A (en) * | 2016-05-17 | 2017-11-27 | 장관영 | Plating Bumping omitted |
| US10622621B2 (en) * | 2017-03-31 | 2020-04-14 | GM Global Technology Operations LLC | Methods for making patterned, thick, silicon-containing electrodes |
| CN207586732U (en) * | 2017-08-22 | 2018-07-06 | 正泰集团股份有限公司 | Solar energy electroplax intelligent production system |
| CN109084957B (en) * | 2018-08-31 | 2024-03-19 | 华南理工大学 | Defect detection and color sorting method and system for photovoltaic solar crystalline silicon cell |
| CN109244192B (en) * | 2018-10-25 | 2024-03-01 | 宁夏小牛自动化设备股份有限公司 | Scribing and welding integrated device with half-piece battery piece standby feeding device |
| 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 (44)
| 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 |
| 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 |
| CN103377094B (en) * | 2012-04-12 | 2016-08-03 | 金蝶软件（中国）有限公司 | Method for monitoring abnormality and device |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
Non-Patent Citations (5)
| Title |
|---|
| "Determination of Photovoltaics Characteristics in Real Field Conditions";Seyedkazem Hosseini,;《IEEE Journal of Photovoltaics》;20180228;第572-580页 * |
| "光伏并网发电系统中孤岛效应检测与容错技术应用研究";华孟迪;《中国优秀硕士学位论文全文数据库-工程科技II辑》;20170215;第C042-1183页 * |
| "太阳能电池组件测试曲线异常分析";郑军;《科技与企业》;20141231;第189页 * |
| "新能源发电功率预测系统数据流容错研究";吴世伟;《电气技术》;20181231;第107-111页 * |
| "硅太阳能电池制备过程的质量控制策略";陈军;《机械设计与制造》;20111231;第252-254页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2020172919A1 (en) | 2020-09-03 |
| CN109828545A (en) | 2019-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2017084186A1 (en) | System and method for automatic monitoring and intelligent analysis of flexible circuit board manufacturing process | |
| CN109828545B (en) | AI intelligent process anomaly identification closed-loop control method, host and equipment system | |
| CN115293463B (en) | Glass lens processing supervision method and system based on cutting quality prediction | |
| CN107069960B (en) | Online defect diagnosis method for secondary operation and maintenance management system | |
| CN111340250A (en) | Equipment maintenance device, method and computer readable storage medium | |
| CN113567446B (en) | Method and system for grading component defect detection quality | |
| CN111598491B (en) | Data monitoring method applied to AOI detection and electronic equipment | |
| CN110597196A (en) | Data acquisition system and data acquisition method | |
| CN109117526B (en) | Data recording and analyzing system applicable to maintenance guide of mechanical system equipment | |
| CN114137302B (en) | Electric energy metering instrument verification whole process monitoring system | |
| CN117236934B (en) | Industrial Internet remote monitoring operation and maintenance management system | |
| CN112290546A (en) | Transformer substation primary equipment fault discrimination method based on man-machine co-fusion | |
| CN107247198A (en) | A kind of distribution equipment malfunction Forecasting Methodology and device | |
| CN114750510A (en) | Defect marking and automatic removing method, system, equipment and medium | |
| US11374534B2 (en) | Optoelectronic solar cell test system for an in-line solar cell production plant, and method for optimizing the in-line production of solar cells using an optoelectronic solar cell test system of this type | |
| CN116665401A (en) | Accident prevention alarm system for chemical production | |
| CN109523030B (en) | Telemetering parameter abnormity monitoring system based on machine learning | |
| CN114895634A (en) | Product production line automatic control system based on machine vision | |
| CN110763979A (en) | LED wafer point measurement automatic system based on MES system | |
| CN112700095B (en) | Battery Pack Production Management System | |
| JP2002297669A (en) | Inspection support system | |
| CN111210092A (en) | Stacking machine predictive maintenance method and system based on deep learning | |
| CN114693164A (en) | Big data-based package printing setting management system | |
| CN111768150A (en) | Logistics equipment health management platform based on 5G network | |
| CN114240211A (en) | Intelligent production management system and management method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |