CN114358242A - Heterojunction battery production monitoring system and method - Google Patents
Heterojunction battery production monitoring system and method Download PDFInfo
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- CN114358242A CN114358242A CN202111396000.4A CN202111396000A CN114358242A CN 114358242 A CN114358242 A CN 114358242A CN 202111396000 A CN202111396000 A CN 202111396000A CN 114358242 A CN114358242 A CN 114358242A
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Abstract
The invention discloses a heterojunction battery production monitoring system and a method, which relate to the technical field of solar batteries and comprise a production monitoring module, a production analysis module, a safety analysis module, an environment monitoring module and a cloud platform; the production analysis module is used for acquiring and analyzing monitoring data of a production site, intercepting operation image information of site workers to extract feature points and judging whether the operation of the site workers is standard or not; meanwhile, a behavior recognition algorithm is used for recognizing the limb actions of field workers, when the field workers operate in an irregular or illegal way, an alarm is given in time to remind the field workers to correct in time, so that the production monitoring is automated, and the production efficiency is improved; the safety analysis module is used for receiving the environmental data collected by the environment monitoring module, inputting the environmental data into the safety assessment model to obtain a safety assessment tag, and then carrying out safety reminding on field workers according to the safety assessment tag, so that the production safety is improved.
Description
Technical Field
The invention relates to the technical field of solar cells, in particular to a heterojunction cell production monitoring system and a heterojunction cell production monitoring method.
Background
Heterojunction solar cells are a highly efficient solar cell production technology that combines amorphous silicon with crystalline silicon solar cells for complementary advantages. The heterojunction solar cell uses a-Si to form a PN junction, can complete the whole process at a low temperature of below 200 ℃, and greatly reduces the temperature of the manufacturing process compared with the forming temperature (900 ℃) of the original thermal diffusion type crystal solar cell. Due to the symmetrical structure and the feature of the low temperature process, the deformation and thermal damage of the silicon wafer caused by heat or film formation are reduced, which is very advantageous for realizing the thinning and high efficiency of the wafer, has the leading high conversion efficiency in the industry, the conversion efficiency is little reduced even at high temperature, and the power generation amount can be further improved by using the double-sided unit. Therefore, heterojunction solar cells have become a hot point of research in the field of solar cells in recent years.
However, in the production process of the heterojunction battery, an effective monitoring system is lacked, when the operation of a worker is not standard or wrong, the worker cannot prompt in time, the operation condition of the worker before and after the accident and the accurate data of the influence of the operation condition on the production of the heterojunction battery cannot be known, and a reliable basis is provided for a technician to analyze the accident reason; therefore, a heterojunction cell production monitoring system and method are provided.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. The invention provides a heterojunction battery production monitoring system and a method, wherein the monitoring data collected by a production monitoring module is identified and analyzed through a mature behavior identification algorithm and an image identification technology, and when the operation of field workers is not standard or violates rules, the monitoring system gives an alarm in time to remind the field workers to correct in time; the production monitoring is automated, the cost is greatly saved, and the production efficiency is improved.
In order to achieve the above object, an embodiment according to a first aspect of the present invention provides a heterojunction battery production monitoring system, including a production monitoring module, a production analysis module, a security analysis module, an environment monitoring module, and a cloud platform;
the production monitoring module is used for monitoring the production process of the heterojunction battery and sending collected monitoring data to the cloud platform for storage through the intelligent gateway, so that technicians can look up or analyze the data afterwards;
the production analysis module is connected with the production monitoring module and used for acquiring monitoring data of a production site, intercepting operation image information of site workers in the monitoring data to extract feature points and judging whether the operation of the site workers is standard or not;
the environment monitoring module is used for monitoring the real-time environment in the heterojunction battery production process and sending the acquired environment data to the safety analysis module through the intelligent gateway;
the safety analysis module is used for inputting the environment data into the safety evaluation model to obtain a safety evaluation label, and sending the safety evaluation label to a mobile phone terminal of a field worker through the intelligent gateway to carry out safety reminding on the field worker.
Further, the specific analysis steps of the production analysis module are as follows:
extracting a high-definition image in the monitoring data, and performing image preprocessing on the high-definition image; the image preprocessing comprises image segmentation, image denoising and image identification;
intercepting and comparing operation image information of field workers in the high-definition image with standard image information of each process stored in a database, and determining the currently performed process and corresponding feature points;
extracting the feature points of the intercepted operation image information according to the determined feature points;
and if the matching of the characteristic points is inconsistent, indicating that the field worker has an operation error, generating a reminding signal, and sending the reminding signal and the corresponding characteristic points to a mobile phone terminal of the field worker to remind the field worker to correct the characteristic points in time.
Further, the production analysis module further comprises: processing the collected operation image information into image frames, and identifying the limb actions of the field workers by using a behavior identification algorithm; if the illegal action is identified, generating an illegal signal and sending the illegal signal and the corresponding illegal action to a mobile phone terminal of a field worker; the violations include cell phone playing, crowd, doze, and smoke.
Further, the database stores standard image information and corresponding characteristic points of each procedure in the heterojunction battery production process; wherein the characteristic points are represented as key operations in the respective processes.
Further wherein the environmental data includes temperature information, humidity information, barometric pressure information, and smoke information.
Further, the safety assessment model is constructed through an RBF neural network or a deep convolutional neural network; the concrete construction steps are as follows:
acquiring standard training data; the standard training data comprises historical environment data and corresponding safety evaluation labels; the safety evaluation label in the standard training data is obtained through manual marking;
constructing a deep convolutional neural network model, and dividing standard training data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3: 1;
and after the training set, the test set and the check set are subjected to data normalization, training, testing and checking the deep convolutional neural network model, and marking the trained deep convolutional neural network model as a safety evaluation model.
Furthermore, the production monitoring module is a plurality of high-definition cameras distributed in the production workshop, and the high-definition cameras are provided with position marks.
Further, a heterojunction battery production monitoring method is applied to a monitoring system and comprises the following steps:
the method comprises the following steps: acquiring monitoring data of a heterojunction battery production process, extracting a high-definition image in the monitoring data, and performing image preprocessing on the high-definition image;
step two: intercepting operation image information of field workers in the high-definition image, comparing the operation image information with standard image information of each process stored in a database, and determining the currently performed process and corresponding feature points;
step three: extracting the feature points of the intercepted operation image information according to the determined feature points, and if the feature points are not matched, indicating that field workers have operation errors and generating a reminding signal;
step four: processing the operation image information into an image frame, identifying the limb action of the field worker by using a behavior identification algorithm, and generating an illegal signal if the illegal action is identified;
step five: acquiring real-time environmental data in the production process of the heterojunction battery, and inputting the environmental data into a safety evaluation model to acquire a safety evaluation tag; and carrying out safety reminding on the field workers according to the safety evaluation tag.
Compared with the prior art, the invention has the beneficial effects that:
1. the production analysis module is used for acquiring and analyzing monitoring data of a production field, intercepting operation image information of field workers to extract feature points, and if the extracted feature points are not matched with the feature points of corresponding processes, indicating that the operation of the field workers is not standard, and generating a reminding signal; meanwhile, a behavior recognition algorithm is used for recognizing the limb actions of the field workers, and if illegal actions are recognized, illegal signals are generated, an alarm is given in time, the field workers are reminded to correct in time, so that production monitoring is automated, and the production efficiency is improved;
2. the environment monitoring module is used for monitoring the real-time environment in the heterojunction battery production process, the safety analysis module is used for receiving the environment data collected by the environment monitoring module and inputting the environment data into the safety evaluation model to obtain the safety evaluation label, and then safety reminding is carried out on site workers according to the safety evaluation label, so that the production safety is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a system block diagram of a heterojunction cell production monitoring system of the present invention.
Fig. 2 is a flow chart of a heterojunction cell production monitoring method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 2, a heterojunction battery production monitoring system includes a production monitoring module, a production analysis module, a security analysis module, an environment monitoring module, an intelligent gateway, and a cloud platform;
the intelligent gateway is also called an internetwork connector and a protocol converter, realizes network interconnection above a network layer, is used for two networks with different high-level protocols, and realizes intercommunication between two systems with completely different communication protocols, data formats, languages and system structures; the embodiment mainly transmits the monitoring data to the cloud platform through the intelligent gateway;
the cloud platform can also be understood as a cloud server, receives and stores monitoring data acquired by the production monitoring module, and is used for technicians to look up or analyze the data afterwards;
in the heterojunction battery production monitoring system provided by the application, the cloud platform acquires monitoring data through the intelligent gateway and the production monitoring module, specifically, the production monitoring module monitors the production process of the heterojunction battery and sends the acquired monitoring data to the intelligent gateway, and the intelligent gateway sends the monitoring data to the cloud platform for storage; the production monitoring module comprises a plurality of high-definition cameras distributed in a production workshop, and the high-definition cameras are provided with position marks;
the production analysis module is connected with the production monitoring module and used for acquiring and analyzing monitoring data of a production field and judging whether the operation of field workers is standard or not, and the production analysis module specifically comprises the following steps:
extracting a high-definition image in the monitoring data, and performing image preprocessing on the high-definition image, wherein the image preprocessing comprises image segmentation, image denoising and image identification;
because a complete heterojunction battery production line comprises a plurality of devices and processes, each device is responsible for one of the processes, and the processes are arranged in series to form a production line; the database stores standard image information and corresponding characteristic points of each procedure in the production process; wherein the characteristic points are expressed as key operations in each process;
intercepting and comparing the operation image information of the field workers with the standard image information of each process stored in the database, and determining the currently performed process and the corresponding characteristic points;
extracting the feature points of the intercepted operation image information according to the determined feature points, and if the feature points are matched consistently, indicating the operation specification of field workers and generating normal signals;
if the matching of the characteristic points is inconsistent, indicating that the field worker has an operation error, generating a reminding signal, and sending the reminding signal and the corresponding characteristic points to a mobile phone terminal of the field worker to remind the field worker to correct in time;
processing the collected operation image information into an image frame, identifying the limb action of the field worker by using a behavior identification algorithm, if the illegal action is identified, generating an illegal signal and sending the illegal signal and the corresponding illegal action to a mobile phone terminal of the field worker; the illegal behaviors comprise cell phone playing, crowd, doze and smoking;
according to the invention, through a mature behavior recognition algorithm and an image recognition technology, the monitoring data collected by the production monitoring module is recognized and analyzed, and when the operation of field workers is not standard or violated, the alarm is given in time to remind the field workers to correct in time; the production monitoring is automated, the cost is greatly saved, and the production efficiency is improved;
in the heterojunction battery production monitoring system provided by the application, the safety analysis module acquires environmental data through the intelligent gateway and the environment monitoring module, specifically, the environment monitoring module monitors a real-time environment in the heterojunction battery production process, transmits the acquired environmental data to the intelligent gateway, and then the intelligent gateway transmits the environmental data to the safety analysis module;
the safety analysis module is used for receiving the environmental data collected by the environment monitoring module, inputting the environmental data into the safety evaluation model to obtain a safety evaluation tag, and sending the safety evaluation tag to a mobile phone terminal of a field worker through the intelligent gateway to carry out safety reminding on the field worker; wherein the environmental data comprises temperature information, humidity information, air pressure information and smoke information;
in the embodiment, the safety evaluation model is constructed by an RBF neural network or a deep convolution neural network; the concrete construction steps are as follows:
acquiring standard training data; the standard training data comprise historical environment data and corresponding safety assessment labels, wherein the safety assessment labels in the standard training data are obtained through manual labeling;
constructing a deep convolutional neural network model, and dividing standard training data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3: 1;
after data normalization is carried out on the training set, the test set and the check set, training, testing and checking are carried out on the deep convolutional neural network model, and the trained deep convolutional neural network model is marked as a safety evaluation model; in this embodiment, the value of the safety assessment tag is 0 or 1, and in the safety assessment process, when the safety assessment tag is 1, it indicates that a potential safety hazard exists; when the security evaluation tag is 0, no security risk is indicated.
A monitoring method for heterojunction battery production is applied to a monitoring system and comprises the following steps:
the method comprises the following steps: acquiring monitoring data of a heterojunction battery production process, extracting a high-definition image in the monitoring data, and performing image preprocessing on the high-definition image;
step two: intercepting operation image information of field workers in the high-definition image, comparing the operation image information with standard image information of each process stored in a database, and determining the currently performed process and corresponding feature points;
step three: extracting the feature points of the intercepted operation image information according to the determined feature points, and if the feature points are not matched, indicating that field workers have operation errors and generating a reminding signal;
step four: processing the operation image information into an image frame, identifying the limb action of the field worker by using a behavior identification algorithm, and generating an illegal signal if the illegal action is identified;
step five: the method comprises the steps of obtaining real-time environment data in the production process of the heterojunction battery, inputting the environment data into a safety evaluation model to obtain a safety evaluation tag, sending the safety evaluation tag to a mobile phone terminal of a field worker through an intelligent gateway, and carrying out safety reminding on the field worker.
The working principle of the invention is as follows:
a heterojunction battery production monitoring system and method, while working, the production monitoring module monitors the production process of the heterojunction battery, and send the monitoring data gathered to the cloud terrace through the intelligent gateway, the production analysis module is used for obtaining the monitoring data of the production field and analyzing, intercept the operation image information of the field staff and carry on the characteristic point to extract, if the characteristic point that is extracted is inconsistent with characteristic point matching of the corresponding process, represent the field staff operates the non-normalcy, produce the warning signal; meanwhile, a behavior recognition algorithm is used for recognizing the limb actions of the field workers, and if illegal actions are recognized, illegal signals are generated, an alarm is given in time, the field workers are reminded to correct in time, so that production monitoring is automated, and the production efficiency is improved;
the environment monitoring module is used for monitoring the real-time environment in the heterojunction battery production process, and the safety analysis module is used for receiving the environmental data that the environment monitoring module gathered and obtaining the safety assessment label in the safety assessment model with environmental data input, and when the safety assessment label was 1, the expression had the potential safety hazard, carries out safety warning to the field work personnel, improves the production security.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (8)
1. A heterojunction battery production monitoring system is characterized by comprising a production monitoring module, a production analysis module, a safety analysis module, an environment monitoring module and a cloud platform;
the production monitoring module is used for monitoring the production process of the heterojunction battery and sending collected monitoring data to the cloud platform for storage through the intelligent gateway, so that technicians can look up or analyze the data afterwards;
the production analysis module is connected with the production monitoring module and used for acquiring monitoring data of a production site, intercepting operation image information of site workers in the monitoring data to extract feature points and judging whether the operation of the site workers is standard or not;
the environment monitoring module is used for monitoring the real-time environment in the heterojunction battery production process and sending the acquired environment data to the safety analysis module through the intelligent gateway;
the safety analysis module is used for inputting the environment data into the safety evaluation model to obtain a safety evaluation label, and sending the safety evaluation label to a mobile phone terminal of a field worker through the intelligent gateway to carry out safety reminding on the field worker.
2. A heterojunction cell production monitoring system according to claim 1, wherein the specific analysis steps of the production analysis module are:
extracting a high-definition image in the monitoring data, and performing image preprocessing on the high-definition image; the image preprocessing comprises image segmentation, image denoising and image identification;
intercepting and comparing operation image information of field workers in the high-definition image with standard image information of each process stored in a database, and determining the currently performed process and corresponding feature points;
extracting the feature points of the intercepted operation image information according to the determined feature points;
and if the matching of the characteristic points is inconsistent, indicating that the field worker has an operation error, generating a reminding signal, and sending the reminding signal and the corresponding characteristic points to a mobile phone terminal of the field worker to remind the field worker to correct the characteristic points in time.
3. A heterojunction cell production monitoring system according to claim 2, wherein said production analysis module further comprises: processing the collected operation image information into image frames, and identifying the limb actions of the field workers by using a behavior identification algorithm; if the illegal action is identified, generating an illegal signal and sending the illegal signal and the corresponding illegal action to a mobile phone terminal of a field worker; the violations include cell phone playing, crowd, doze, and smoke.
4. The heterojunction battery production monitoring system of claim 2, wherein the database stores standard image information and corresponding feature points of each process in the heterojunction battery production process; wherein the characteristic points are represented as key operations in the respective processes.
5. A heterojunction battery production monitoring system as claimed in claim 1 wherein the environmental data comprises temperature information, humidity information, atmospheric pressure information and smoke information.
6. A heterojunction battery production monitoring system according to claim 1, wherein the safety assessment model is constructed by an RBF neural network or a deep convolutional neural network; the concrete construction steps are as follows:
acquiring standard training data; the standard training data comprises historical environment data and corresponding safety evaluation labels; the safety evaluation label in the standard training data is obtained through manual marking;
constructing a deep convolutional neural network model, and dividing standard training data into a training set, a test set and a check set according to a set proportion; the set ratio comprises 2:1:1, 3:1:1 and 4:3: 1;
and after the training set, the test set and the check set are subjected to data normalization, training, testing and checking the deep convolutional neural network model, and marking the trained deep convolutional neural network model as a safety evaluation model.
7. The heterojunction battery production monitoring system of claim 1, wherein the production monitoring module is a plurality of high-definition cameras distributed inside a production workshop, and each high-definition camera is provided with a position identifier.
8. A heterojunction battery production monitoring method is applied to a monitoring system and is characterized by comprising the following steps:
the method comprises the following steps: acquiring monitoring data of a heterojunction battery production process, extracting a high-definition image in the monitoring data, and performing image preprocessing on the high-definition image;
step two: intercepting operation image information of field workers in the high-definition image, comparing the operation image information with standard image information of each process stored in a database, and determining the currently performed process and corresponding feature points;
step three: extracting the feature points of the intercepted operation image information according to the determined feature points, and if the feature points are not matched, indicating that field workers have operation errors and generating a reminding signal;
step four: processing the operation image information into an image frame, identifying the limb action of the field worker by using a behavior identification algorithm, and generating an illegal signal if the illegal action is identified;
step five: acquiring real-time environmental data in the production process of the heterojunction battery, and inputting the environmental data into a safety evaluation model to acquire a safety evaluation tag; and carrying out safety reminding on the field workers according to the safety evaluation tag.
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| CN115327897A (en) * | 2022-07-18 | 2022-11-11 | 深圳市粤港科技有限公司 | Intelligent control system based on laboratory |
| CN115660397A (en) * | 2022-11-11 | 2023-01-31 | 广东佳瑞达科技有限公司 | Production preparation process management system and method for intelligent ammeter shell |
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| CN115327897A (en) * | 2022-07-18 | 2022-11-11 | 深圳市粤港科技有限公司 | Intelligent control system based on laboratory |
| CN115660397A (en) * | 2022-11-11 | 2023-01-31 | 广东佳瑞达科技有限公司 | Production preparation process management system and method for intelligent ammeter shell |
| CN116380289A (en) * | 2023-05-24 | 2023-07-04 | 中央储备粮镇江直属库有限公司 | Automatic grain temperature monitoring and analyzing system for granary |
| CN116380289B (en) * | 2023-05-24 | 2023-11-10 | 中央储备粮镇江直属库有限公司 | Automatic grain temperature monitoring and analyzing system for granary |
| CN117392591A (en) * | 2023-09-27 | 2024-01-12 | 鲁班(广东)科技有限公司 | Site security AI detection method and device |
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