CN111222762A - Solar cell panel coating process state monitoring and quality control system - Google Patents
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Abstract
The invention provides a solar cell panel coating process state monitoring and quality control system which comprises a central processing unit, a sensing data online monitoring module, a product quality real-time monitoring module, an equipment running state online monitoring module, a dynamic decision optimization module and a basic information management module. According to the invention, various data in the solar cell panel coating process are uploaded to the cloud end for processing, storage and analysis, the production process is monitored in real time, key quality data are monitored, an alarm is given in time and an adjustment suggestion is given when an abnormality occurs, the product quality is ensured, the product qualification rate is improved, the development and daily maintenance processes become centralized, shorter, more economical and efficient, and the problem that the product quality and the product qualification rate are greatly reduced because data errors often occur in the production process and quality data due to the fact that the existing solar cell panel coating process state monitoring and quality control system cannot well monitor the production process and quality data in the use process is solved.
Description
Technical Field
The invention relates to the field of solar cell panel coating processes, in particular to a solar cell panel coating process state monitoring and quality control system.
Background
The importance of intelligent manufacturing is emphasized no matter German industry 4.0 or Chinese manufacturing 2025, for manufacturing enterprises, intelligent manufacturing needs to be realized by constructing an intelligent factory, the intelligent factory is based on a digital factory, production information management is completed through an equipment monitoring technology and an Internet of things technology, and equipment data is converted into real-time information by utilizing a data processing and analyzing platform, and the intelligent factory is a humanized factory which is comfortable in environment, efficient and energy-saving.
The solar cell panel coating process is complex, the types of data are more and the data volume is large in the whole production process, and the data errors can often occur in the existing solar cell panel coating process state monitoring and quality control system in the use process due to the fact that the production process and quality data cannot be well improved, and the product quality and the product percent of pass are greatly reduced.
Therefore, there is a need to provide a system for monitoring the state of the solar cell panel coating process and controlling the quality of the solar cell panel coating process.
Disclosure of Invention
The invention provides a solar cell panel coating process state monitoring and quality control system, which solves the problem that the product quality and the product qualification rate are greatly reduced because data errors often occur in the production process and quality data in the use process of the conventional solar cell panel coating process state monitoring and quality control system.
In order to solve the technical problems, the solar cell panel coating process state monitoring and quality control system provided by the invention comprises a central processing unit, a sensing data online monitoring module, a product quality real-time monitoring module, an equipment running state online monitoring module, a dynamic decision optimization module and a basic information management module, wherein the electrical output end of the central processing unit is respectively and interactively connected with the electrical input ends of the sensing data online monitoring module, the product quality real-time monitoring module, the equipment running state online monitoring module, the dynamic decision optimization module and the basic information management module, the electrical output end of the product quality real-time monitoring module is respectively and interactively connected with a univariate and a multivariate, the electrical output end of the sensing data online monitoring module is interactively connected with a BP neural network module, the electrical output end of the BP neural network module is interactively connected with the product quality real-time monitoring module, the electric output end of the equipment running state online monitoring module is respectively and interactively connected with an input module and an alarm module, and the electric output end of the equipment running state online monitoring module is electrically connected with a display module.
Preferably, the electrical output end of the BP neural network module is interactively connected with a genetic algorithm module, and the genetic algorithm module comprises three parts of determining a BP neural network structure, optimizing a threshold value and a weight value of a genetic algorithm and training and predicting the BP neural network.
Preferably, in the implementation process of the genetic algorithm optimization threshold, the related operations include chromosome coding, generation of an initial population, construction of a fitness function, selection operation, crossover operation and mutation operation.
Preferably, the BP neural network structure is composed of an input layer, a hidden layer and an output layer, and is a multi-layer feedforward neural network with unidirectional propagation, and the training and prediction of the BP neural network includes initializing the network, calculating hidden layer output, calculating output layer output, calculating errors, updating weights, updating thresholds, and judging whether iteration of an algorithm is finished.
Preferably, the sensing data online monitoring module comprises production process data such as temperature, gas flow (silane, nitrogen, laughing gas and ammonia), current and power, and final coating thickness and statistical indexes.
Preferably, the statistical indicator includes one or more of a mean, range, standard deviation, median, single value, moving range, reject count, reject rate, defect count, and unit defect count of the production characteristic data.
Preferably, the display module is set as a computer display screen and a mobile phone display screen, and the input module is set as a keyboard, a mouse and mobile communication equipment.
Preferably, the alarm module is provided with a plurality of buzzers.
Compared with the related art, the solar cell panel coating process state monitoring and quality control system provided by the invention has the following beneficial effects:
the invention provides a solar cell panel coating process state monitoring and quality control system,
1. the system integrally adopts a B/S framework, various data in the solar cell panel coating process are uploaded to the cloud end for processing, storing and analyzing, the production process is monitored in real time, key quality data are monitored, an alarm is given in time and an adjustment suggestion is given when abnormality occurs, the product quality is ensured, the product qualification rate is improved, the development and daily maintenance processes become centralized, shorter, more economical and efficient, and the problem that the product quality and the product qualification rate are greatly reduced because data errors often occur due to the fact that the existing solar cell panel coating process state monitoring and quality control system cannot well monitor the production process and quality data in the using process is solved;
2. the invention realizes the monitoring of the whole production process by arranging the interactive connection between the BP neural network module and the sensing data online monitoring module and the product quality real-time monitoring module to perform online monitoring on the acquired data and monitoring a plurality of quality characteristic data through a multivariable control chart, when abnormal conditions occur, the system gives an alarm in time, the influence of data such as temperature, gas flow, current, power and the like on the film thickness of a coating film is found out by establishing a regression model between market process data and product quality data based on the BP neural network module, the threshold value and the weight value of the BP neural network module are improved through the genetic algorithm module, the genetic algorithm module has advantages in the aspect of global search, the performance of the BP neural network module is improved, and the accuracy of the regression model is improved.
Drawings
FIG. 1 is a system schematic diagram of a solar panel coating process state monitoring and quality control system provided by the invention;
FIG. 2 is a system schematic diagram of the sensor data online monitoring module shown in FIG. 1;
FIG. 3 is a system diagram of the statistical indicator of FIG. 1;
FIG. 4 is a flowchart of the operation of the BP neural network module shown in FIG. 1;
FIG. 5 is a flowchart illustrating the operation of the neural network algorithm shown in FIG. 4;
FIG. 6 is a flowchart of the operation of the genetic algorithm module shown in FIG. 2.
Detailed Description
The invention is further described with reference to the following figures and embodiments.
Referring to fig. 1, fig. 2, fig. 3, fig. 4, fig. 5 and fig. 6 in combination, wherein fig. 1 is a system schematic diagram of a solar panel coating process state monitoring and quality control system according to the present invention, fig. 2 is a system schematic diagram of a sensor data online monitoring module shown in fig. 1, fig. 3 is a system schematic diagram of statistical indexes shown in fig. 1, fig. 4 is a node work flow diagram of a BP neural network module shown in fig. 1, fig. 5 is a work flow diagram of a neural network algorithm shown in fig. 4, and fig. 6 is a work flow diagram of a genetic algorithm module shown in fig. 2. The solar cell panel coating process state monitoring and quality control system comprises a central processing unit, a sensing data online monitoring module, a product quality real-time monitoring module, an equipment running state online monitoring module, a dynamic decision optimization module and a basic information management module, wherein the electrical output end of the central processing unit is respectively and interactively connected with the electrical input ends of the sensing data online monitoring module, the product quality real-time monitoring module, the equipment running state online monitoring module, the dynamic decision optimization module and the basic information management module, the electrical output end of the product quality real-time monitoring module is respectively and interactively connected with a univariate and a multivariate, the electrical output end of the sensing data online monitoring module is interactively connected with a BP neural network module, the electrical output end of the BP neural network module is interactively connected with the product quality real-time monitoring module, the electric output end of the equipment running state online monitoring module is respectively and interactively connected with an input module and an alarm module, and the electric output end of the equipment running state online monitoring module is electrically connected with a display module.
The electric output end of the BP neural network module is interactively connected with a genetic algorithm module, the genetic algorithm module comprises three parts of determining a BP neural network structure, optimizing a threshold value and a weight value of a genetic algorithm and training and predicting the BP neural network, the threshold value and the weight value of the BP neural network module are improved through the genetic algorithm module, the genetic algorithm module has advantages in the aspect of global search, the performance of the BP neural network module is improved, and therefore the accuracy of a regression model is improved.
In the implementation process of the genetic algorithm optimization threshold, the related operations comprise chromosome coding, initial population generation, fitness function construction, selection operation, crossover operation and mutation operation.
The BP neural network structure consists of an input layer, a hidden layer and an output layer, and is a multi-layer feedforward neural network with one-way propagation, and the training and prediction of the BP neural network comprise initializing the network, calculating the output of the hidden layer, calculating the output of the output layer, calculating errors, updating weights, updating thresholds and judging whether the iteration of the algorithm is finished.
The online monitoring module for the sensing data comprises production process data such as temperature, gas flow (silane, nitrogen, laughing gas and ammonia), current and power, final coating thickness and statistical indexes, and the influence of the data such as the temperature, the gas flow, the current and the power on the coating thickness is found out by establishing a regression model between market process data and product quality data based on the BP neural network module.
The statistical indexes comprise one or more of the mean value, range, standard deviation, median, single value, moving range, unqualified product number, unqualified product rate, defect number and unit defect number of the production characteristic data, a plurality of quality characteristic data are monitored through a multivariable control chart, the monitoring of the whole production process is realized, and when abnormal conditions occur, the system gives an alarm in time.
The display module is set as a computer display screen and a mobile phone display screen, the input module is set as a keyboard, a mouse and mobile communication equipment, and by setting the computer display screen, the keyboard and the mouse, a user can control the system through various equipment without being limited to a certain operation method, and the practicability of the system in the using process is improved.
The alarm module sets up to bee calling organ, bee calling organ's quantity sets up to a plurality of, through setting up a plurality of bee calling organ, can let the user can be more clear, accurate, rapid discovery problem to in time make the adjustment to it, thereby improved the qualification rate of product.
The working principle of the solar cell panel coating process state monitoring and quality control system provided by the invention is as follows:
the BP neural network training process comprises the following steps:
(1) the network is initialized.
Forming a sequence X by input and output of the system2Y; determining the number of nodes of an input layer as n, the number of nodes of a hidden layer as l, and the number of nodes of an output layer as m; initializing a connection weight wij between the input layer and the hidden layer and a connection weight wjk between the hidden layer and the output layer; initializing a threshold a of an implicit layer and a threshold b of an output layer; giving a learning rate and a neuron excitation function;
(2) the hidden layer output is computed.
And calculating the output of the hidden layer through an input variable x, a connection weight wij between the input layer and the hidden layer and a threshold a of the hidden layer. The output H of the hidden layer is:
in the formula, l represents the number of nodes of the hidden layer, f represents the excitation function of the hidden layer, a Sigmoid function is selected as the excitation function, and the excitation function is as follows:
(3) and calculating output layer output.
And calculating the predicted output of the output layer through the output H of the hidden layer, the connection weight wjk between the hidden layer and the output layer threshold b. The predicted output O of the output layer is:
(4) and calculating the error.
The prediction error of the network is calculated from the predicted output O of the output layer and the desired output Y of the output layer. The network prediction error e is expressed as:
ek＝Yk-Okk＝1,2,…,m (4)
(5) and updating the weight value.
And updating the connection weight wij between the input layer and the hidden layer and the connection weight wjk between the hidden layer and the output layer according to the prediction error e of the network.
wjk＝wjk+ηHjekj＝1,2,…,l；k＝1,2,…,m
(6)
Where η represents the learning rate.
(6) The threshold is updated.
And updating the threshold value a of the hidden layer and the threshold value b of the output layer according to the prediction error e of the network.
bk＝bk+ekk＝1,2,…,m (8)
(7) And (4) judging whether the iteration of the algorithm is finished, and if not, returning to the step (2).
In the implementation process of the genetic algorithm, the involved operations comprise chromosome coding, initial population generation, fitness function construction, selection operation, crossover operation, mutation operation and the like. The flow of the genetic algorithm is as follows:
(1) the chromosomes are encoded.
Coding is the first step in designing genetic algorithms, and the selection of coding methods affects the selection, crossover and mutation processes and the efficiency of genetic evolution. The common coding methods include binary coding and floating point coding. Binary coding operation and realization are simple, cross, variation and other operations are convenient to realize, but local search capability is poor, and numerical value optimization precision is not high; the floating point coding has high precision, is suitable for searching a large space, and can process complex constraint conditions.
(2) The population is initialized.
The individuals in the population correspond to solutions to the problem, the population corresponds to a set of solutions to the problem, and the number of individuals in the population is the size of the population. The parameter value of the reasonable group scale is usually set to be 20-160. Too large population scale results in increased computational complexity and longer time consumption; too small a population size can result in insufficient information and premature convergence of the algorithm.
(3) And designing a fitness function.
The fitness is used for judging the excellent degree of the genetic algorithm individuals, the individuals with high fitness have high probability to be transmitted to the next generation group, and the value of the fitness cannot be smaller than zero. The fitness function is determined by the objective function and is the basis for the algorithm to complete the selection operation. Therefore, designing the fitness function is an important step of the genetic algorithm.
(4) And (4) genetic manipulation. Including selection, crossover and mutation operations.
Selecting: and selecting individuals with strong adaptability, screening the individuals with weak adaptability, and using the individuals as parents to be inherited to the next generation, so that the fitness of the group gradually approaches to an optimal value.
And (3) crossing: simulating gene recombination, selecting two individuals in a parent population, completing exchange combination of certain positions, and generating new individuals superior to the parent. The convergence of the genetic algorithm depends on the convergence of the crossover operator.
Mutation: certain bits in the coding strings of individual chromosomes are changed to generate new individuals, so that an optimal solution can be obtained in a local range, and the diversity of population individuals is ensured.
Compared with the related art, the solar cell panel coating process state monitoring and quality control system provided by the invention has the following beneficial effects:
the system integrally adopts a B/S framework, various data in the solar cell panel coating process are uploaded to the cloud end for processing, storing and analyzing, the production process is monitored in real time, key quality data are monitored, an alarm is given in time and an adjustment suggestion is given when an abnormality occurs, the product quality is ensured, the product qualification rate is improved, the development and daily maintenance processes become centralized, shorter, more economical and efficient, and the problem that the product quality and the product qualification rate are greatly reduced because data errors often occur due to the fact that the existing solar cell panel coating process state monitoring and quality control system cannot well monitor the production process and the quality data in the using process is solved.
The above description is only an 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 (8)
1. The solar cell panel coating process state monitoring and quality control system comprises a central processing unit, a sensing data online monitoring module, a product quality real-time monitoring module, an equipment running state online monitoring module, a dynamic decision optimization module and a basic information management module, and is characterized in that the electrical output end of the central processing unit is respectively and interactively connected with the electrical input ends of the sensing data online monitoring module, the product quality real-time monitoring module, the equipment running state online monitoring module, the dynamic decision optimization module and the basic information management module, the electrical output end of the product quality real-time monitoring module is respectively and interactively connected with a univariate and a multivariate, the electrical output end of the sensing data online monitoring module is interactively connected with a BP neural network module, the electrical output end of the BP neural network module is interactively connected with the product quality real-time monitoring module, the electric output end of the equipment running state online monitoring module is respectively and interactively connected with an input module and an alarm module, and the electric output end of the equipment running state online monitoring module is electrically connected with a display module.
2. The system for monitoring the state and controlling the quality of the solar cell panel coating process according to claim 1, wherein the electrical output end of the BP neural network module is interactively connected with a genetic algorithm module, and the genetic algorithm module comprises three parts of determining the BP neural network structure, optimizing a threshold value and a weight value of a genetic algorithm and training and predicting the BP neural network.
3. The system for monitoring the state and controlling the quality of the solar cell panel coating process according to claim 2, wherein operations involved in the implementation of the genetic algorithm optimization threshold include chromosome coding, initial population generation, fitness function construction, selection, crossover, and mutation operations.
4. The system for monitoring the state and controlling the quality of the solar cell panel coating process according to claim 2, wherein the BP neural network structure comprises an input layer, a hidden layer and an output layer, and is a multi-layer feed-forward neural network with unidirectional propagation, and the BP neural network training and prediction comprises initializing the network, calculating the hidden layer output, calculating the output layer output, calculating errors, updating weights, updating thresholds and judging whether the iteration of the algorithm is finished.
5. The system for monitoring the state and controlling the quality of the solar cell panel coating process according to claim 1, wherein the sensing data online monitoring module comprises production process data such as temperature, gas flow (silane, nitrogen, laughing gas and ammonia), current and power, and final coating thickness and statistical indexes.
6. The system according to claim 5, wherein the statistical indicators comprise one or more of mean, range, standard deviation, median, single value, moving range, reject count, reject rate, defect count, and unit defect count of the production characteristic data.
7. The system for monitoring the state of the solar cell panel coating process and controlling the quality of the solar cell panel coating process according to claim 1, wherein the display module is configured to be a computer display screen and a mobile phone display screen, and the input module is configured to be a keyboard, a mouse and a mobile communication device.
8. The system for monitoring the state of the solar panel coating process and controlling the quality of the solar panel coating process according to claim 1, wherein the alarm module is provided with a buzzer, and the number of the buzzers is multiple.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114936746A (en) * | 2022-04-13 | 2022-08-23 | 舒氏集团有限公司 | Quality analysis method for wear-resistant, illumination-resistant, tensile and shrinkage-free warning tape corona composite film |
| CN115406489A (en) * | 2022-11-01 | 2022-11-29 | 山东申华光学科技有限公司 | Monitoring and early warning method and system for film coating of film coating machine |
| CN117554407A (en) * | 2024-01-10 | 2024-02-13 | 南通纳科达聚氨酯科技有限公司 | On-line detection method and system for photonic crystal coating defects |
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| CN114936746A (en) * | 2022-04-13 | 2022-08-23 | 舒氏集团有限公司 | Quality analysis method for wear-resistant, illumination-resistant, tensile and shrinkage-free warning tape corona composite film |
| CN115406489A (en) * | 2022-11-01 | 2022-11-29 | 山东申华光学科技有限公司 | Monitoring and early warning method and system for film coating of film coating machine |
| CN115406489B (en) * | 2022-11-01 | 2023-01-24 | 山东申华光学科技有限公司 | Monitoring and early warning method and system for film coating of film coating machine |
| CN117554407A (en) * | 2024-01-10 | 2024-02-13 | 南通纳科达聚氨酯科技有限公司 | On-line detection method and system for photonic crystal coating defects |
| CN117554407B (en) * | 2024-01-10 | 2024-05-24 | 南通纳科达聚氨酯科技有限公司 | On-line detection method and system for photonic crystal coating defects |
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