A method and system for managing and monitoring the preparation of photoelectric thin film materials

Abstract

The present invention discloses a method and system for managing and monitoring the preparation of photoelectric thin film materials, which relates to the technical field of photoelectric thin film material preparation, and includes the following steps: monitoring the temperature of a substrate in real time by installing multiple temperature sensors on the surface of the substrate, and connecting all temperature sensors to a central data acquisition system, through which temperature data is collected and recorded in real time. The present invention can timely discover the problem of non-uniform heating, which enables the production team to intervene in time, avoiding quality defects of photoelectric thin film materials caused by uneven heating. When this problem is found, the heating element with abnormal hidden dangers is quickly located, and the temperature of the heating element is adjusted in real time using a fuzzy logic algorithm to ensure that its temperature is always within the optimal working range. Such intelligent adjustment not only ensures the continuity and efficiency of the production process, but also optimizes energy consumption and production cycle, ensuring that each batch of materials meets high quality standards.

Description

Technical Field

[0001] The present invention relates to the technical field of photoelectric thin film material preparation, and in particular to a method and system for managing and monitoring the preparation of photoelectric thin film materials. Background Art

[0002] Photovoltaic thin film material preparation management involves the process of manufacturing and managing thin film materials used in optoelectronic devices. Photovoltaic thin films are a special class of materials with specific optical and electrical properties, and are widely used in solar cells, displays, light sensors and other fields. These films are usually composed of semiconductors, metals or dielectric materials, and are deposited on substrates by various physical or chemical methods. Preparation management not only includes the selection of suitable materials and deposition techniques, but also requires precise control of parameters in the deposition process, such as temperature, gas pressure, deposition rate, etc., to ensure that the quality and performance of the film meet the design requirements.

[0003] The preparation management of optoelectronic thin film materials also includes aspects such as monitoring and optimization of the preparation process, cost control, and environmental safety management. Monitoring during the preparation process ensures the uniformity and repeatability of the film, while optimization aims to improve material performance and reduce production costs. At the same time, since some deposition materials or processes may pose risks to the environment and the health of operators, reasonable environmental safety measures are also an important part of management. As the market's requirements for the performance and reliability of optoelectronic products increase, an effective quality control system is also an indispensable part of the preparation management of optoelectronic thin film materials.

[0004] In the preparation and management of optoelectronic thin film materials, the deposition process refers to the technical steps of uniformly covering the material on the substrate in the form of a thin film. This process is the core part of the entire preparation and directly affects the performance of the film and the quality of the final product. Deposition technology is mainly divided into two categories: physical vapor deposition (PVD) and chemical vapor deposition (CVD).

[0005] Physical vapor deposition (PVD) includes techniques such as sputtering and evaporation. In the sputtering process, the material is knocked out from the target surface after being bombarded by ions, and then deposited on the substrate to form a thin film. Evaporation is to heat the material to a high enough vapor pressure so that it evaporates into a gaseous state, and then condenses on a cooled substrate under low pressure to form a thin film. The choice of these methods depends on the material, thickness, purity and other physical properties of the required film.

[0006] Chemical vapor deposition (CVD) generates solid materials on the surface of a substrate under high temperature conditions through chemical reactions. The raw gas decomposes on the surface of the substrate or reacts with other chemicals to generate the desired film. CVD technology can produce films with excellent structural and electrical properties, suitable for the production of silicon optoelectronic devices, high-performance ceramics, etc. In addition, there are some special deposition technologies such as atomic layer deposition (ALD), which can control the thickness and composition of the film with atomic-level precision, and are very suitable for the production of ultra-thin and high-precision optoelectronic films. The management of the entire deposition process requires strict control of parameters such as temperature, pressure, and gas flow to ensure the quality and consistency of the film.

[0007] The prior art has the following deficiencies:

[0008] During the preparation of photovoltaic thin film materials through physical vapor deposition, if the heating element of the substrate malfunctions and causes non-uniform heating of the substrate, the existing technology usually shuts down the machine for maintenance in a timely manner. The maintenance will not only greatly reduce production efficiency, but also may cause serious quality problems in the photovoltaic thin film materials due to production interruptions, such as uneven film thickness, unstable physical properties, etc.

[0009] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0010] The purpose of the present invention is to provide a method and system for managing and monitoring the preparation of photoelectric thin film materials. The present invention can promptly detect the problem of non-uniform heating, which enables the production team to intervene in time and avoid quality defects of photoelectric thin film materials caused by uneven heating. When this problem is discovered, the heating element with abnormal hidden dangers is quickly located, and the temperature of the heating element is adjusted in real time using a fuzzy logic algorithm to ensure that its temperature is always within the optimal working range. Such intelligent adjustment not only ensures the continuity and efficiency of the production process, but also optimizes energy consumption and production cycle, ensuring that each batch of materials meets high quality standards to solve the problems in the above-mentioned background technology.

[0011] In order to achieve the above object, the present invention provides the following technical solution: a method for managing and monitoring the preparation of photoelectric thin film materials, comprising the following steps:

[0012] The temperature of the substrate is monitored in real time by a plurality of temperature sensors installed on the surface of the substrate, and all the temperature sensors are connected to a central data acquisition system, through which the temperature data is collected and recorded in real time;

[0013] The optimal operating temperature range is set based on the quality requirements and process tolerance of the photovoltaic film, and the substrate temperature monitored in real time is comprehensively analyzed to identify the abnormal hidden dangers of non-uniform heating of the substrate;

[0014] When it is detected that there is an abnormal hidden danger of non-uniform heating of the substrate, the actual temperature of the substrate is compared and analyzed with the optimal working temperature range, and the heating element with the abnormal hidden danger is screened out;

[0015] Analyze the heating element with abnormal hidden dangers and calculate the temperature deviation between the expected heating temperature and the actual temperature of the heating element under the current heating power;

[0016] The heating elements with abnormal hidden dangers are further divided according to the temperature deviation, and the abnormal hidden dangers are divided into abnormal hidden dangers of expected heating temperature being greater than the actual temperature and abnormal hidden dangers of expected heating temperature being less than the actual temperature;

[0017] For abnormal hidden dangers where the expected heating temperature is lower than the actual temperature, the fuzzy logic algorithm is used to adjust the current heating power of the heating element so that the actual temperature of the heating element is within the optimal operating temperature range;

[0018] For abnormal hidden dangers where the expected heating temperature is greater than the actual temperature, a fuzzy logic algorithm is used to adjust the heating time of the heating element so that the actual temperature of the heating element is within the optimal operating temperature range.

[0019] Preferably, the specific steps for identifying the abnormal hidden danger of non-uniform heating of the substrate are as follows:

[0020] Based on the central data acquisition system, the actual temperatures of different temperature sensors at the same time are obtained, a data set is established for the actual temperatures obtained at the same time, and the temperature mean and temperature standard deviation of the actual temperatures in the data set are calculated;

[0021] The temperature mean and temperature standard deviation are compared and analyzed with the preset optimal operating temperature range and standard deviation reference threshold, and the results of the comparison and analysis are as follows:

[0022] If the temperature mean is within the optimal operating temperature range and the temperature standard deviation is less than the standard deviation reference threshold, a normal heating signal is generated;

[0023] If the temperature mean is not within the optimal operating temperature range and the temperature standard deviation is less than the standard deviation reference threshold, a non-uniform heating hazard signal is generated.

[0024] Preferably, when a non-uniform heating hazard signal is generated during the operation of the substrate, all actual temperatures in the analysis set at the corresponding moment are compared and analyzed with the optimal operating temperature range, and the actual temperatures that are not within the optimal operating temperature range are screened out. After the actual temperature is matched with the corresponding heating element, the heating element is marked as a heating element with abnormal hazards and then screened out.

[0025] Preferably, the deviation between the expected heating temperature and the actual temperature of the heating element with potential abnormality at the current heating power is calculated by the following steps:

[0026] Get the heating power of the heating element from the controller of the heating system P ;

[0027] Calculate the expected heating temperature of the heating element with abnormal hidden dangers under the current heating power. The calculation expression is: , where Indicates the expected heating temperature of the heating element, is the radiative emissivity, is the Stefan–Boltzmann constant, A is the surface area of ​​the heating element, ℎ is the convective heat transfer coefficient, is the ambient temperature;

[0028] Calculate temperature deviation , the calculation expression is: ,in, Indicates the actual heating temperature of the heating element.

[0029] Preferably, if the temperature deviation is greater than 0, that is , the heating element with abnormal hidden danger is classified as abnormal hidden danger, which is classified as abnormal hidden danger of expected heating temperature being greater than actual temperature;

[0030] If the temperature deviation is less than 0, that is , the heating element with abnormal hidden dangers is classified as an abnormal hidden danger of expected heating temperature being lower than the actual temperature.

[0031] Preferably, the steps of adjusting the power of the heating element using the fuzzy logic algorithm are as follows:

[0032] Define the input variables and output variables for power adjustment. The input variables include temperature deviation and current heating power, and the output variables include power adjustment ;

[0033] Define fuzzy sets for each variable in power adjustment;

[0034] Use trapezoidal or triangular membership functions to represent each fuzzy set;

[0035] Formulate fuzzy rules for power adjustment;

[0036] Fuzzy reasoning for power regulation using Mamdani or Sugeno models;

[0037] Use the centroid method to perform defuzzification and calculate the precise power adjustment value. The specific calculation formula is: ,in, It is i The membership output value of the rule, is the power adjustment corresponding to the rule, n Indicates the total number of rules;

[0038] According to the calculated Adjust the current heating power. The specific adjustment formula is: , where is the adjusted power, Indicates the current heating power.

[0039] Preferably, the steps of adjusting the heating time of the heating element by using a fuzzy logic algorithm are as follows:

[0040] Define the input variables and output variables for heating time adjustment. The input variables include temperature deviation and current heating time, and the output variables include heating time adjustment. ;

[0041] Define fuzzy sets for each variable when adjusting the heating duration;

[0042] Formulate fuzzy rules for adjusting heating time;

[0043] Fuzzy reasoning for thermal duration adjustment using Mamdani or Sugeno models;

[0044] Use the centroid method to perform defuzzification and calculate the precise heating time adjustment value. The calculation formula is: ,in, It is k The membership output value of the rule, is the heating time adjustment corresponding to the rule;

[0045] According to the calculated Adjust the current heating time. The specific adjustment formula is: , where is the adjusted heating time, Indicates the current heating time. m Indicates the total number of rules.

[0046] A management and monitoring system for the preparation of photoelectric thin film materials, comprising a temperature monitoring module, an abnormal hidden danger analysis module, an abnormal screening module, a temperature difference calculation module, an abnormal classification module, a power adjustment module and a heating time adjustment module;

[0047] A temperature monitoring module monitors the temperature of the substrate in real time through multiple temperature sensors installed on the surface of the substrate, and connects all temperature sensors to a central data acquisition system, through which temperature data is collected and recorded in real time;

[0048] The abnormal hidden danger analysis module sets the optimal operating temperature range based on the quality requirements and process tolerance of the photovoltaic film, and conducts a comprehensive analysis of the real-time monitored substrate temperature to identify the abnormal hidden danger of non-uniform heating of the substrate;

[0049] The abnormal screening module compares and analyzes the actual temperature of the substrate with the optimal operating temperature range when it detects that the substrate has an abnormal hidden danger of non-uniform heating, and screens out the heating elements with abnormal hidden dangers;

[0050] The temperature difference calculation module analyzes the heating element with abnormal hidden dangers and calculates the temperature deviation between the expected heating temperature and the actual temperature of the heating element under the current heating power;

[0051] The abnormal classification module further divides the heating elements with abnormal hidden dangers according to the temperature deviation, and divides the abnormal hidden dangers into abnormal hidden dangers of expected heating temperature greater than the actual temperature and abnormal hidden dangers of expected heating temperature less than the actual temperature;

[0052] The power adjustment module uses a fuzzy logic algorithm to adjust the current heating power of the heating element for the abnormal hidden danger that the expected heating temperature is lower than the actual temperature, so that the actual temperature of the heating element is within the optimal operating temperature range;

[0053] The heating time adjustment module uses a fuzzy logic algorithm to adjust the heating time of the heating element for abnormal hidden dangers such as the expected heating temperature being greater than the actual temperature, so that the actual temperature of the heating element is within the optimal operating temperature range.

[0054] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0055] The present invention can quickly detect any signs of non-uniform heating by real-time monitoring and recording the temperature of the substrate. The instant abnormality detection mechanism can intervene before the problem develops to a serious level, thereby avoiding quality problems of photovoltaic thin film materials caused by uneven heating, such as inconsistent film thickness or unstable physical properties. In addition, this method can also help the maintenance team locate problems more accurately and perform targeted repairs or adjustments, thereby reducing unnecessary equipment downtime.

[0056] The present invention uses a fuzzy logic algorithm to make real-time adjustments to abnormal heating elements, ensuring that the actual temperature of the heating elements can quickly return to the optimal operating temperature range. This intelligent adjustment mechanism allows the production process to continue without stopping, significantly improving the operating efficiency and output of the production line. For heating elements whose actual temperature is lower than expected, the system will increase their heating power; conversely, if the actual temperature is higher than expected, the heating time will be reduced or the power will be lowered. Such dynamic adjustment not only reduces energy consumption, but also optimizes the production cycle, ensuring that each batch of materials can meet high quality standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0058] Figure 1 The present invention is a method flow chart of a method for managing and monitoring the preparation of photoelectric thin film materials.

[0059] Figure 2 The present invention is a module schematic diagram of a system for managing and monitoring the preparation of photoelectric thin film materials. DETAILED DESCRIPTION

[0060] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0061] The present invention provides Figure 1 A method for managing and monitoring the preparation of photoelectric thin film materials is shown, comprising the following steps:

[0062] The temperature of the substrate is monitored in real time by a plurality of temperature sensors installed on the surface of the substrate, and all the temperature sensors are connected to a central data acquisition system, through which the temperature data is collected and recorded in real time;

[0063] Temperature sensors should be evenly distributed across the surface of the substrate to ensure that the temperature distribution across the substrate can be captured. This helps identify hot and cold spots, thereby avoiding areas that are locally overheated or do not reach the desired temperature.

[0064] Connecting all temperature sensors to a central data acquisition system that collects and records temperature data in real time has the following key benefits:

[0065] The central data acquisition system can provide real-time temperature monitoring, which is essential for adjusting heating parameters, ensuring heating uniformity, and preventing product defects caused by abnormal temperature. The system can also record temperature data, which provides basic data for quality control, fault analysis, and optimization of production processes.

[0066] The collected temperature data can be used for further data analysis, such as trend analysis, anomaly detection, and process control. These analyses can help engineers optimize the production process, reduce downtime, and improve product quality. For example, by analyzing the data, the performance degradation trend of the heating element can be discovered, and maintenance or replacement can be performed in time to avoid production interruptions and increased defect rates.

[0067] The optimal operating temperature range is set based on the quality requirements and process tolerance of the photovoltaic film, and the substrate temperature monitored in real time is comprehensively analyzed to identify the abnormal hidden dangers of non-uniform heating of the substrate;

[0068] The quality requirements for photovoltaic thin films refer to the specific performance standards that the thin film materials must meet to ensure their effectiveness and reliability in the final application. These requirements typically include film uniformity, thickness accuracy, optical transparency, electrical properties (such as conductivity and dielectricity), and stability to environmental factors (such as temperature and humidity). During the manufacturing process, these quality parameters must be strictly controlled to ensure that each batch of thin film products can provide consistent performance in its application, such as use in solar cells, displays or sensors. Quality control measures include, but are not limited to, the use of high-precision preparation techniques, precise measurement tools and strict quality inspection processes.

[0069] Process tolerance refers to the range of variation allowed for material and process parameters during the preparation of photovoltaic thin films without affecting the performance of the final product. This includes the control range for factors such as temperature, humidity, deposition rate, substrate cleanliness, etc. The setting of tolerance is based on understanding the extent to which changes in these parameters affect the performance of the film to ensure the stability of the production process and the reliability of the product. For example, process tolerance specifies that the substrate temperature fluctuates within a specific range to prevent unacceptable changes in material properties due to temperature fluctuations. Appropriate process tolerance settings are critical to optimizing production efficiency, reducing scrap, and improving economic benefits.

[0070] The optimal operating temperature range is set based on the quality requirements and process tolerance of the photovoltaic film. Therefore, the optimal operating temperature range is not specifically limited here and can be determined according to actual needs.

[0071] The specific steps to identify the abnormal hidden danger of non-uniform substrate heating are as follows:

[0072] Based on the central data acquisition system, the actual temperatures of different temperature sensors at the same time are obtained, a data set is established for the actual temperatures obtained at the same time, and the temperature mean and temperature standard deviation of the actual temperatures in the data set are calculated;

[0073] The temperature mean and temperature standard deviation are compared and analyzed with the preset optimal operating temperature range and standard deviation reference threshold, and the results of the comparison and analysis are as follows:

[0074] If the temperature mean is within the optimal operating temperature range and the temperature standard deviation is less than the standard deviation reference threshold, a normal heating signal is generated, indicating that the substrate can be heated uniformly and efficiently at this time;

[0075] If the temperature mean is not within the optimal operating temperature range and the temperature standard deviation is less than the standard deviation reference threshold, a non-uniform heating hidden danger signal is generated, indicating that at this time there is an abnormal hidden danger of non-uniform heating of the substrate.

[0076] When it is detected that there is an abnormal hidden danger of non-uniform heating of the substrate, the actual temperature of the substrate is compared and analyzed with the optimal working temperature range, and the heating element with the abnormal hidden danger is screened out;

[0077] When a non-uniform heating hazard signal is generated during the operation of the substrate, all actual temperatures in the analysis set at the corresponding moment are compared and analyzed with the optimal working temperature range, and the actual temperatures that are not within the optimal working temperature range are screened out. After matching this actual temperature with the corresponding heating element, the heating element is marked as a heating element with abnormal hazards and then screened out.

[0078] Analyze the heating element with abnormal hidden dangers and calculate the temperature deviation between the expected heating temperature and the actual temperature of the heating element under the current heating power;

[0079] The deviation between the expected heating temperature and the actual temperature of the heating element with a potential abnormality at the current heating power is calculated by the following steps:

[0080] Get the heating power of the heating element from the controller of the heating system P ;

[0081] Calculate the expected heating temperature of the heating element with abnormal hidden dangers under the current heating power. The calculation expression is: , where Indicates the expected heating temperature of the heating element, is the radiative emissivity, is the Stefan–Boltzmann constant, Ais the surface area of ​​the heating element (in square meters), ℎ is the convective heat transfer coefficient (in Watts per square meter Kelvin), is the ambient temperature (in Kelvin);

[0082] Calculate temperature deviation , the calculation expression is: ,in, Indicates the actual heating temperature of the heating element.

[0083] The heating elements with abnormal hidden dangers are further divided according to the temperature deviation, and the abnormal hidden dangers are divided into abnormal hidden dangers of expected heating temperature being greater than the actual temperature and abnormal hidden dangers of expected heating temperature being less than the actual temperature;

[0084] If the temperature deviation is greater than 0, , the heating element with abnormal hidden danger is classified as abnormal hidden danger, which is classified as abnormal hidden danger of expected heating temperature being greater than actual temperature;

[0085] If the temperature deviation is less than 0, that is , the heating element with abnormal hidden dangers is classified as an abnormal hidden danger of expected heating temperature being lower than the actual temperature.

[0086] For abnormal hidden dangers where the expected heating temperature is lower than the actual temperature, the fuzzy logic algorithm is used to adjust the current heating power of the heating element so that the actual temperature of the heating element is within the optimal operating temperature range;

[0087] The steps to adjust the power of the heating element using the fuzzy logic algorithm are as follows:

[0088] Define the input variables and output variables for power adjustment. The input variables include temperature deviation and current heating power, and the output variables include power adjustment ;

[0089] Define fuzzy sets for each variable in power adjustment;

[0090] For example:

[0091] Temperature deviation can be classified as "very low", "low", "moderate", "high", "very high".

[0092] The current heating power can be divided into "Low", "Medium" and "High".

[0093] The power adjustment can be divided into "substantially decrease", "slightly decrease", "remain unchanged", "slightly increase", and "substantially increase".

[0094] Use trapezoidal or triangular membership functions to represent each fuzzy set;

[0095] For example, for temperature deviation, you could set a "very high" membership function that is 1 (perfect compliance) above 30°C and rises linearly between 25°C and 30°C.

[0096] Formulate fuzzy rules for power adjustment;

[0097] For example: If the temperature deviation is "high" and the current heating power is "medium", the power is adjusted to "increase slightly".

[0098] If the temperature deviation is "very high", the power is adjusted to "greatly increased" regardless of the current heating power.

[0099] Fuzzy reasoning for power regulation using Mamdani or Sugeno models;

[0100] For example, when using the Mamdani model, the impact of each rule can be evaluated through the maximum-minimum synthesis method and the fuzzy set of the overall output can be synthesized.

[0101] Use the centroid method (center average method) to perform defuzzification and calculate the precise power adjustment value. The specific calculation formula is: ,in, It is i The membership output value of the rule, is the power adjustment corresponding to the rule, n Indicates the total number of rules;

[0102] For example, if the output membership for "small increase" is 0.8, the corresponding power adjustment might be +15 watts.

[0103] According to the calculated Adjust the current heating power. The specific adjustment formula is: , where is the adjusted power, Indicates the current heating power.

[0104] For abnormal hidden dangers where the expected heating temperature is greater than the actual temperature, the fuzzy logic algorithm is used to adjust the heating time of the heating element so that the actual temperature of the heating element is within the optimal working temperature range;

[0105] The steps of adjusting the heating time of the heating element using the fuzzy logic algorithm are as follows:

[0106] Define the input variables and output variables for heating time adjustment. The input variables include temperature deviation and current heating time, and the output variables include heating time adjustment. ;

[0107] The current heating time of the heating element can also be obtained via the central data acquisition system.

[0108] Define fuzzy sets for each variable when adjusting the heating duration;

[0109] For example:

[0110] Temperature deviation can be classified as Very Low, Low, Moderate, High, or Very High.

[0111] The current heating time can be divided into "short", "medium" and "long".

[0112] Heating time adjustment can be divided into "substantially reduce", "slightly reduce", and "remain unchanged".

[0113] Formulate fuzzy rules for adjusting heating time;

[0114] Example of fuzzy rule: If the temperature deviation is "high" and the current power is "medium", then the power is adjusted to "small increase".

[0115] If the temperature deviation is "very high", the power is adjusted to "greatly increased" regardless of the current power.

[0116] Fuzzy reasoning for thermal duration adjustment using Mamdani or Sugeno models;

[0117] Here we take the Mamdani method as an example, which evaluates the impact of each rule through the maximum-minimum synthesis method and synthesizes the fuzzy set of the overall output.

[0118] Use the centroid method (center average method) for defuzzification and calculate the precise heating time adjustment value. The calculation formula is: ,in, It is k The membership output value of the rule, is the heating time adjustment corresponding to the rule;

[0119] According to the calculated Adjust the current heating time. The specific adjustment formula is: , where is the adjusted heating time, Indicates the current heating time. m Indicates the total number of rules.

[0120] The present invention can quickly detect any signs of non-uniform heating by real-time monitoring and recording the temperature of the substrate. The instant abnormality detection mechanism can intervene before the problem develops to a serious level, thereby avoiding quality problems of photovoltaic thin film materials caused by uneven heating, such as inconsistent film thickness or unstable physical properties. In addition, this method can also help the maintenance team locate problems more accurately and perform targeted repairs or adjustments, thereby reducing unnecessary equipment downtime.

[0121] The present invention uses a fuzzy logic algorithm to make real-time adjustments to abnormal heating elements, ensuring that the actual temperature of the heating elements can quickly return to the optimal operating temperature range. This intelligent adjustment mechanism allows the production process to continue without stopping (maintenance and management of the heating elements will continue after a single process or batch process is completed), which significantly improves the operating efficiency and output of the production line. For heating elements whose actual temperature is lower than expected, the system will increase their heating power; conversely, if the actual temperature is higher than expected, the heating time will be reduced or the power will be lowered. Such dynamic adjustment not only reduces energy consumption, but also optimizes the production cycle, ensuring that each batch of materials can meet high quality standards.

[0122] The present invention provides Figure 2 A photoelectric thin film material preparation management and monitoring system shown includes a temperature monitoring module, an abnormal hidden danger analysis module, an abnormal screening module, a temperature difference calculation module, an abnormal classification module, a power adjustment module and a heating time adjustment module;

[0123] A temperature monitoring module monitors the temperature of the substrate in real time through multiple temperature sensors installed on the surface of the substrate, and connects all temperature sensors to a central data acquisition system, through which temperature data is collected and recorded in real time;

[0124] The abnormal hidden danger analysis module sets the optimal operating temperature range based on the quality requirements and process tolerance of the photovoltaic film, and conducts a comprehensive analysis of the real-time monitored substrate temperature to identify the abnormal hidden danger of non-uniform heating of the substrate;

[0125] The abnormal screening module compares and analyzes the actual temperature of the substrate with the optimal operating temperature range when it detects that the substrate has an abnormal hidden danger of non-uniform heating, and screens out the heating elements with abnormal hidden dangers;

[0126] The temperature difference calculation module analyzes the heating element with abnormal hidden dangers and calculates the temperature deviation between the expected heating temperature and the actual temperature of the heating element under the current heating power;

[0127] The abnormal classification module further divides the heating elements with abnormal hidden dangers according to the temperature deviation, and divides the abnormal hidden dangers into abnormal hidden dangers of expected heating temperature greater than the actual temperature and abnormal hidden dangers of expected heating temperature less than the actual temperature;

[0128] The power adjustment module uses a fuzzy logic algorithm to adjust the current heating power of the heating element for the abnormal hidden danger that the expected heating temperature is lower than the actual temperature, so that the actual temperature of the heating element is within the optimal operating temperature range;

[0129] The heating time adjustment module uses a fuzzy logic algorithm to adjust the heating time of the heating element for abnormal hazards such as the expected heating temperature being greater than the actual temperature, so that the actual temperature of the heating element is within the optimal operating temperature range;

[0130] A method for managing and monitoring the preparation of photoelectric thin film materials provided in an embodiment of the present invention is implemented by the above-mentioned system for managing and monitoring the preparation of photoelectric thin film materials. The specific method and process of the system for managing and monitoring the preparation of photoelectric thin film materials are detailed in the embodiment of the above-mentioned method for managing and monitoring the preparation of photoelectric thin film materials, and will not be repeated here.

[0131] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0132] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0133] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0137] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0138] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0139] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for managing and monitoring the preparation of photoelectric thin film materials, characterized in that: The following steps are involved: The temperature of the substrate is monitored in real time by a plurality of temperature sensors installed on the surface of the substrate, and all the temperature sensors are connected to a central data acquisition system, through which the temperature data is collected and recorded in real time; The optimal operating temperature range is set based on the quality requirements and process tolerance of the photovoltaic film, and the substrate temperature monitored in real time is comprehensively analyzed to identify the abnormal hidden dangers of non-uniform heating of the substrate; When it is detected that there is an abnormal hidden danger of non-uniform heating of the substrate, the actual temperature of the substrate is compared and analyzed with the optimal working temperature range, and the heating element with the abnormal hidden danger is screened out; Analyze the heating element with abnormal hidden dangers and calculate the temperature deviation between the expected heating temperature and the actual temperature of the heating element under the current heating power; The heating elements with abnormal hidden dangers are further divided according to the temperature deviation, and the abnormal hidden dangers are divided into abnormal hidden dangers of expected heating temperature being greater than the actual temperature and abnormal hidden dangers of expected heating temperature being less than the actual temperature; For abnormal hidden dangers where the expected heating temperature is lower than the actual temperature, the fuzzy logic algorithm is used to adjust the current heating power of the heating element so that the actual temperature of the heating element is within the optimal operating temperature range; For abnormal hidden dangers where the expected heating temperature is greater than the actual temperature, the fuzzy logic algorithm is used to adjust the heating time of the heating element so that the actual temperature of the heating element is within the optimal working temperature range; The deviation between the expected heating temperature and the actual temperature of the heating element with a potential abnormality at the current heating power is calculated by the following steps: Get the heating power of the heating element from the controller of the heating system P ; Calculate the expected heating temperature of the heating element with abnormal hidden dangers under the current heating power. The calculation expression is: , where Indicates the expected heating temperature of the heating element, is the radiative emissivity, is the Stefan–Boltzmann constant, A is the surface area of ​​the heating element, ℎ is the convective heat transfer coefficient, is the ambient temperature; Calculate temperature deviation , the calculation expression is: ,in, Indicates the actual heating temperature of the heating element; If the temperature deviation is greater than 0, , the heating element with abnormal hidden danger is classified as abnormal hidden danger, which is classified as abnormal hidden danger of expected heating temperature being greater than actual temperature; If the temperature deviation is less than 0, , the heating element with abnormal hidden danger is classified as abnormal hidden danger, which is classified as abnormal hidden danger of expected heating temperature being lower than actual temperature; The steps to adjust the power of the heating element using the fuzzy logic algorithm are as follows: Define the input variables and output variables for power adjustment. The input variables include temperature deviation and current heating power, and the output variables include power adjustment ; Define fuzzy sets for each variable in power adjustment; Use trapezoidal or triangular membership functions to represent each fuzzy set; Formulate fuzzy rules for power adjustment; Fuzzy reasoning for power regulation using Mamdani or Sugeno models; Use the centroid method to perform defuzzification and calculate the precise power adjustment value. The specific calculation formula is: ,in, It is i The membership output value of the rule, is the power adjustment corresponding to the rule, n Indicates the total number of rules; According to the calculated Adjust the current heating power. The specific adjustment formula is: , where is the adjusted power, Indicates the current heating power; The steps of adjusting the heating time of the heating element using the fuzzy logic algorithm are as follows: Define the input variables and output variables for heating time adjustment. The input variables include temperature deviation and current heating time, and the output variables include heating time adjustment. ; Define fuzzy sets for each variable when adjusting the heating duration; Formulate fuzzy rules for adjusting heating time; Fuzzy reasoning for thermal duration adjustment using Mamdani or Sugeno models; Use the centroid method to perform defuzzification and calculate the precise heating time adjustment value. The calculation formula is: ,in, It is k The membership output value of the rule, is the heating time adjustment corresponding to the rule; According to the calculated Adjust the current heating time. The specific adjustment formula is: , where is the adjusted heating time, Indicates the current heating time. m Indicates the total number of rules.

2. A method for managing and monitoring the preparation of photoelectric thin film materials according to claim 1, characterized in that: The specific steps to identify the abnormal hidden danger of non-uniform substrate heating are as follows: Based on the central data acquisition system, the actual temperatures of different temperature sensors at the same time are obtained, a data set is established for the actual temperatures obtained at the same time, and the temperature mean and temperature standard deviation of the actual temperatures in the data set are calculated; The temperature mean and temperature standard deviation are compared and analyzed with the preset optimal operating temperature range and standard deviation reference threshold, and the results of the comparison and analysis are as follows: If the temperature mean is within the optimal operating temperature range and the temperature standard deviation is less than the standard deviation reference threshold, a normal heating signal is generated; If the temperature mean is not within the optimal operating temperature range and the temperature standard deviation is less than the standard deviation reference threshold, a non-uniform heating hazard signal is generated.

3. A method for managing and monitoring the preparation of photoelectric thin film materials according to claim 2, characterized in that: When a non-uniform heating hazard signal is generated during the operation of the substrate, all actual temperatures in the analysis set at the corresponding moment are compared and analyzed with the optimal working temperature range, and the actual temperatures that are not within the optimal working temperature range are screened out. After matching this actual temperature with the corresponding heating element, the heating element is marked as a heating element with abnormal hazards and then screened out.

4. A system for managing and monitoring the preparation of photoelectric thin film materials, used to implement a method for managing and monitoring the preparation of photoelectric thin film materials as described in any one of claims 1 to 3, characterized in that: It includes temperature monitoring module, abnormal hidden danger analysis module, abnormal screening module, temperature difference calculation module, abnormal classification module, power adjustment module and heating time adjustment module; A temperature monitoring module monitors the temperature of the substrate in real time through multiple temperature sensors installed on the surface of the substrate, and connects all temperature sensors to a central data acquisition system, through which temperature data is collected and recorded in real time; The abnormal hidden danger analysis module sets the optimal operating temperature range based on the quality requirements and process tolerance of the photovoltaic film, and conducts a comprehensive analysis of the real-time monitored substrate temperature to identify the abnormal hidden danger of non-uniform heating of the substrate; The abnormal screening module compares and analyzes the actual temperature of the substrate with the optimal operating temperature range when it detects that the substrate has an abnormal hidden danger of non-uniform heating, and screens out the heating elements with abnormal hidden dangers; The temperature difference calculation module analyzes the heating element with abnormal hidden dangers and calculates the temperature deviation between the expected heating temperature and the actual temperature of the heating element under the current heating power; The abnormal classification module further divides the heating elements with abnormal hidden dangers according to the temperature deviation, and divides the abnormal hidden dangers into abnormal hidden dangers of expected heating temperature greater than the actual temperature and abnormal hidden dangers of expected heating temperature less than the actual temperature; The power adjustment module uses a fuzzy logic algorithm to adjust the current heating power of the heating element for the abnormal hidden danger that the expected heating temperature is lower than the actual temperature, so that the actual temperature of the heating element is within the optimal operating temperature range; The heating time adjustment module uses a fuzzy logic algorithm to adjust the heating time of the heating element for abnormal hidden dangers such as the expected heating temperature being greater than the actual temperature, so that the actual temperature of the heating element is within the optimal operating temperature range.

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