Control method for production equipment of photovoltaic round wire copper strip

Abstract

The invention discloses a control method of production equipment of a photovoltaic round wire copper strip. The method comprises the following steps: counting the yield of the photovoltaic round wire copper strip in a first time interval, calculating a yield fluctuation value and dividing fluctuation grades; counting the defect types of the defective products in the time interval, distinguishing defects mainly caused by equipment problems, non-equipment problems or other conditions, and calculating the proportion of each defect type; and dynamically adjusting the equipment maintenance period based on the yield fluctuation level and the defect type proportion. By means of the method, the equipment maintenance frequency and the product yield can be effectively balanced, the equipment utilization rate is increased, the product yield is increased, and the stability of photovoltaic round wire copper strip productivity and the product quality are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of production and manufacturing equipment control, and in particular to a control method for production equipment of photovoltaic round wire copper strips. Background Art

[0002] The quality of soldering ribbon production is crucial in photovoltaic module manufacturing. Soldering ribbon is made from round copper strip through multiple processes, including rolling, drawing, annealing, and storage. During the production of photovoltaic round copper ribbon, the equipment maintenance cycle significantly impacts product quality and production efficiency.

[0003] Traditional equipment maintenance methods are usually based on fixed cycles and cannot be dynamically adjusted according to actual production conditions. Fixed-cycle maintenance methods may lead to over-maintenance or under-maintenance. Over-maintenance will waste resources and affect production rhythm and production line utilization; while under-maintenance may cause equipment failure, resulting in insufficient product yield and affecting product quality.

[0004] Therefore, how to maintain a balance between production line efficiency and product yield is an urgent problem to be solved in this field. Summary of the Invention

[0005] The object of the present invention is to provide a control method for production equipment of photovoltaic round wire copper strips, so as to at least partially solve the above-mentioned problems.

[0006] According to one aspect of the present disclosure, a control method for production equipment of photovoltaic round copper strip is provided, comprising:

[0007] Counting the yield of photovoltaic round copper strips in a first time interval T to obtain first statistical data, and calculating the yield fluctuation level in the first time interval based on the first statistical data;

[0008] Counting the defect types of the bad copper strips in the first time interval T to obtain second statistical data, and obtaining the proportion of the defect types of the bad copper strips based on the second statistical data,

[0009] The defect types include a first defect type, a second defect type, and a third defect type, wherein the first defect type refers to a defect mainly caused by a first factor, the second defect type refers to a defect mainly caused by a second factor, and the third defect type refers to other defect types except the first defect type and the second defect type.

[0010] The first factor is a device problem, and the second factor is a non-device problem;

[0011] Based on the yield fluctuation level and the defect type proportion, the equipment maintenance cycle is adjusted or not adjusted.

[0012] Optionally, the method further includes: the first time interval T is less than a regular maintenance cycle of the equipment or less than a previous maintenance cycle of the equipment;

[0013] Among them, for equipment that is put into operation for the first time, the length of the first time interval is less than the regular maintenance cycle of the equipment; for equipment that is not put into operation for the first time, the length of the first time interval is less than the previous maintenance cycle of the equipment.

[0014] Optionally, the method further includes dividing the first time interval into a plurality of sub-intervals t, and calculating the yield y corresponding to each sub-interval t.

[0015] Optionally, the method also includes, based on a pre-set threshold, dividing the calculated yield fluctuation value into several levels to obtain the yield fluctuation level, wherein the yield fluctuation value Y is determined by the mean m and / or fluctuation v of the yield y corresponding to each sub-interval t.

[0016] Optionally, the method also includes correcting the yield fluctuation value Y, including removing burrs in the yield y of the sub-interval t, wherein the burrs are the yield y being less than a first preset threshold or greater than a second preset threshold, and determining the yield fluctuation value Y based on multiple yields after removing the burrs.

[0017] Optionally, the method further includes dynamically adjusting the duration of the first time interval T based on the health of the device, including:

[0018] The duration of the first time interval T is positively correlated with the health of the device, that is, the higher the health of the device, the longer the duration of the first time interval T.

[0019] Optionally, the method further includes adjusting or not adjusting the equipment maintenance cycle based on the yield fluctuation level and the defect type proportion, including:

[0020] If the yield fluctuation level is high and the proportion of the first defect type exceeds the first preset threshold,

[0021] Alternatively, the yield fluctuation level is medium, and the proportion of the first defect type exceeds the second preset threshold,

[0022] Alternatively, the yield fluctuation level is high, and the proportion of the third type exceeds the third preset threshold.

[0023] The equipment maintenance cycle is shortened, wherein the first preset threshold is less than the second preset threshold, and the second preset threshold is less than the third preset threshold,

[0024] Otherwise, the equipment maintenance cycle will not be adjusted.

[0025] Optionally, the method further includes: the yield fluctuation value Y is determined by the mean and / or fluctuation of the yields y of the plurality of subintervals t, including:

[0026] The yield fluctuation value Y is the mean value m of the yield y of the plurality of subintervals t.

[0027] Or, the yield fluctuation value Y is the fluctuation rate v of the yield y of the plurality of subintervals t,

[0028] Alternatively, the yield fluctuation value Y is a weighted calculation of the mean m and the fluctuation rate v.

[0029] The present disclosure further provides a computer-readable storage medium storing a computer program, wherein the computer program executes the steps of the method described in any of the above embodiments when executed by a processor.

[0030] An embodiment of the present application further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor executes the steps of the method described in any of the above embodiments by calling the computer program stored in the memory.

[0031] The present invention discloses a control method for photovoltaic round copper strip production equipment. The method includes calculating the yield rate of the photovoltaic round copper strip during a first time interval, calculating the yield rate fluctuation value, and classifying the fluctuation level; calculating the defect types of defective products within the time interval, distinguishing defects primarily due to equipment problems, non-equipment problems, or other reasons, and calculating the proportion of each defect type; and dynamically adjusting the equipment maintenance cycle based on the yield rate fluctuation level and defect type proportion. This method effectively balances equipment maintenance frequency with product yield, improves equipment utilization, increases product yield, and ensures the stability of photovoltaic round copper strip production capacity and product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of a control method for a photovoltaic round copper strip production device provided in an embodiment of the present application;

[0033] Figure 2 A schematic diagram of a control system for a photovoltaic round copper strip production device provided in an embodiment of the present application;

[0034] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] The following is a further description of specific embodiments of the present invention in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0036] It should be noted that, in this application, the terms "first," "second," and various numerical references are used to distinguish between different categories for ease of description and are not intended to limit the scope of the embodiments of this application. For example, they are used to distinguish between different classification results, rather than to describe a specific order or precedence. It should be understood that the terms described in this manner are interchangeable, where appropriate, to enable description of solutions beyond the embodiments of this application.

[0037] Specifically, Figure 1 The following is a flowchart showing a specific implementation of a control method for a photovoltaic round wire copper strip production device provided by an embodiment of the present application. Figure 1 The control method of the production equipment of photovoltaic round wire copper strip provided by this application has the following specific steps:

[0038] Step S1 : Counting the yield of photovoltaic round wire copper strips in a first time interval T to obtain first statistical data, and calculating the yield fluctuation level in the first time interval based on the first statistical data.

[0039] Step S2: Count the defect types of the bad copper strips in the first time interval T to obtain second statistical data, and obtain the proportion of the defect types of the bad copper strips based on the second statistical data.

[0040] The defect types include a first defect type, a second defect type, and a third defect type, wherein the first defect type refers to a defect mainly caused by a first factor, the second defect type refers to a defect mainly caused by a second factor, and the third defect type refers to other defect types except the first defect type and the second defect type.

[0041] The first factor is a device problem, and the second factor is a non-device problem;

[0042] Step S3: adjusting or not adjusting the equipment maintenance cycle based on the yield fluctuation level and the defect type proportion.

[0043] In some embodiments, the first time interval T can be determined based on the operating characteristics and historical maintenance records of the equipment. For example, for equipment that is being put into operation for the first time, T can be set to 20 days, which is less than the equipment's regular maintenance cycle of 30 days; for equipment that is not being put into operation for the first time, T can be set to 15-20 days, which is less than the equipment's previous maintenance cycle of 25 days. It is understood that the above specific time periods can be adjusted based on actual conditions. Within a time interval less than the regular maintenance cycle or the equipment's previous maintenance cycle, the equipment maintenance cycle can be assessed in advance, and the maintenance cycle can be dynamically adjusted based on the actual situation of the production line, actively controlling the equipment maintenance cycle.

[0044] During a first time interval T, production data of photovoltaic round copper strips is collected, including product yield data and defect type data of defective copper strips. The product yield data can be obtained through a quality inspection system. Specifically, during the production process of photovoltaic round copper strips, during the first time interval T, the photovoltaic round copper strips are inspected after undergoing key processes such as rolling and drawing. In some embodiments, the inspection can be performed using an automated inspection device.

[0045] In some embodiments, the first time interval T can be divided into multiple subintervals t, and each subinterval t corresponds to the yield y generated in the subinterval t. For example, T is 20 days, which is divided into 10 subintervals t, each t is 2 days, and the yields y1 to y10 of each day are calculated. The yield fluctuation value Y is determined by the yield mean m and yield fluctuation v of the yield y in each subinterval t. It is understandable that the yield fluctuation value Y can be characterized in a variety of ways, such as the yield mean and yield fluctuation in this embodiment, or by using the interquartile range, moving average, autocorrelation function, etc., which is not limited in this application.

[0046] In one embodiment, the yield fluctuation value Y=mean value m, where m is calculated as follows:

[0047] m=(y1+y2+...+yn) / n, where n is the number of subintervals.

[0048] In one embodiment, the yield fluctuation value Y=yield fluctuation rate v, where v is calculated as follows:

[0049] v=y max -y min / y min , where y max is the maximum yield rate in the first time interval T, y min is the minimum yield value in the first time interval T.

[0050] In some embodiments, the yield fluctuation value can also be based on the weighted sum of the yield mean m and the yield fluctuation v, specifically Y = αm + βv, where α and β represent weight coefficients, α + β = 1, and the specific values of α and β can be set based on experience, which is not limited in this embodiment.

[0051] It is understandable that factors that lead to a decline in yield may include raw material quality, production process parameters, and equipment stability. The yield changes corresponding to equipment instability are usually less regular, and the yield data reflected within a certain period of time may fluctuate greatly. For example, equipment problems usually include: wear of the wire drawing die, which may cause deviations in the diameter of the round wire (such as local thinning or excessive ovality); mechanical vibration of the equipment may cause scratches or dimensional fluctuations on the surface of the copper strip; residual metal debris or grease in the equipment may contaminate the surface of the copper strip and form defects; deviation of the equipment mechanism may cause abnormal hardness or insufficient ductility of the round wire, which may be easy to break. Within a certain time range, raw materials are usually from the same batch, and the process parameter formula is also fixed. Therefore, this also determines that yield fluctuations caused mainly by raw materials and process parameter formulas are usually relatively stable compared to yield fluctuations caused mainly by equipment problems. In other words, within a certain time range, yield fluctuations caused mainly by equipment problems are usually of a higher level than yield fluctuations caused mainly by raw materials and process parameters.

[0052] For example, yield fluctuations are categorized into three levels: low, medium, and high, based on pre-set thresholds. It is understood that a higher average yield value corresponds to a lower yield fluctuation level, and a lower average yield value corresponds to a higher yield fluctuation level. For example, if m < 70%, the yield fluctuation level is high; if 50% <= m < 80%, the yield fluctuation level is medium; and if 80% <= m <= 100%, the yield fluctuation level is low. Similarly, the smaller the yield fluctuation v, the lower the yield fluctuation level, while the larger the yield fluctuation v, the higher the yield fluctuation level. For example, if the fluctuation v is less than 10%, the yield fluctuation level is low; if the fluctuation v is between 10% and 30%, the yield fluctuation level is medium; and if the fluctuation v is greater than 30%, the yield fluctuation level is high. Furthermore, the yield fluctuation value Y can also be derived through a weighted calculation based on the yield mean m and the yield fluctuation v, which will not be further described in this embodiment. It is understood that the m and v values corresponding to the above levels can be set according to actual circumstances, and this embodiment does not impose any limitations.

[0053] In order to avoid the influence of yield spurious data caused by statistical errors or other system reasons on the calculated yield fluctuation value, in some embodiments, these spurious data are removed and the above formula is used to calculate the yield fluctuation value, wherein the spurious data refers to the yield data with a yield y less than a first threshold, or the yield y greater than a second threshold. The first threshold and the second threshold can be determined by referring to the yield data of multiple sub-intervals combined with historical yield data. The first threshold can be 30% and the second threshold can be 95%, so as to correct the yield fluctuation and make the classification of the yield fluctuation level more accurate.

[0054] In some embodiments, to further improve the accuracy of yield fluctuation classification, this embodiment also includes dynamically adjusting the duration of the first time interval T based on the health of the equipment. For example, if the equipment has been in operation for three years and the number of key component repairs exceeds a preset threshold, indicating relatively low equipment health, the first time interval T is set to a relatively short duration, such as five days. If the equipment has been in operation for less than one year and the number of key component repairs is less than the preset threshold, the first time interval T is set to a relatively long duration, such as 20 days. It is understood that if the equipment has experienced health issues in the past, even after repairs, its operational stability will be compromised to a certain extent. All other things being equal, the greater the number of repairs, the greater the compromise in stability. In this embodiment, the equipment health assessment not only considers the equipment's operating age, but also the number of repairs to key components during operation, further improving the accuracy of yield fluctuation classification. It is understood that key equipment components may include, but are not limited to, drawing dies, annealing and cooling mechanisms, and calendering mechanisms.

[0055] Furthermore, the defect types and proportions of defective products are counted. Specifically, within the same first time interval T, the defect types of the detected defective products are counted to obtain second statistical data. Defect types include but are not limited to the following three categories: (a) The first defect type: defects are mainly caused by equipment problems. For example, they may include but are not limited to wear of the drawing die, local diameter thinning or excessive ovality caused by mechanical vibration of the equipment, residual metal debris or grease pollution in the equipment, etc.; (b) The second defect type: defects are mainly caused by non-equipment problems. For example, they may include but are not limited to fluctuations in raw material quality, abnormal process parameters, etc.; (c) The third defect type: other defect types other than the first defect type and the second defect type, that is, other defect types that are not identified as the first defect type or the second defect type; It can be understood that the situations classified as the third defect type include but are not limited to the following situations: One situation is that, in addition to being mainly caused by equipment problems or Defects that are primarily caused by non-equipment issues may also be caused by both equipment and non-equipment issues, with the probability of the defect being caused by a combination of these issues being difficult to distinguish. For example, defects caused by both equipment and process parameter issues, where it is difficult to distinguish the probability of the defect being caused by either equipment or process issues, are more likely to occur. Specific examples include scratches and voids on the copper strip caused by both drawing dies and impurities in raw materials, or cracks on the copper strip caused by improper coolant formulation and equipment tension component issues. The second situation is that due to limited detection and recognition accuracy, for some defects, the recognition algorithm itself may not be able to accurately identify whether they are the first or second defect types. In this case, they are uniformly classified as the third defect type. Furthermore, the number of occurrences of each defect type is counted, and the proportion of each defect type to the total number of defective products is calculated, i.e., the defect type proportion.

[0056] In some embodiments, the defect types of defective products can be automatically identified through machine vision detection, resistance and conductivity testing, tensile testing and mechanical property analysis. For hidden defects or undetected special defects, manual sampling can be used as a supplementary means of automatic detection. Specifically, for example, product appearance image data can be obtained through an image detection device, resistance and conductivity data can be obtained through an electrical testing device, and product tensile data can be obtained through a tensile testing device. The above-mentioned acquired data are preprocessed and normalized and then input into a trained deep learning model. The model identifies the specific defect type. For example, the model classifies the defect type through the output layer and outputs the specific defect type according to the input data. The deep learning model and its training process can adopt existing models and corresponding existing training methods. This application will not go into details or impose any restrictions.

[0057] Furthermore, the yield fluctuation level and defect type proportion information are comprehensively considered to determine whether to adjust the equipment maintenance cycle. Specifically, when the yield fluctuation level is high, it indicates that there are more significant unstable factors in the production process. If the proportion of the first defect type (mainly defects caused by equipment problems) exceeds the first preset threshold at this time, it can be clearly determined that the equipment problem is the main reason for the decline in yield. In this case, the system will immediately issue a maintenance warning signal and automatically adjust the equipment maintenance plan to shorten the next maintenance time in order to solve the equipment problem as soon as possible and restore production stability. Exemplarily, for example, assuming that the first preset threshold is set to 50%, when the proportion of the first defect type reaches 55% and the yield fluctuation level is high, the system will trigger an immediate maintenance warning.

[0058] If the yield fluctuation level is medium, and the proportion of the first defect type exceeds the second preset threshold (the second preset threshold is greater than the first preset threshold), although equipment problems may still be an important factor leading to a decrease in yield, the yield fluctuation level is not the highest. At this time, the system can issue a maintenance warning signal after a preset time and arrange in advance to shorten the equipment maintenance cycle. For example, if the second preset threshold is set to 60%, when the proportion of the first defect type reaches 65% and the yield fluctuation level is medium, the system will remind you to shorten the maintenance cycle after a certain period of time, so that potential equipment problems can be dealt with in a timely manner without affecting the production rhythm. The "certain time" here can be set according to actual conditions and is not limited in this embodiment.

[0059] For the third defect type, the defect type ratio information is difficult to directly reflect the factors that cause product defects. When the third defect type ratio exceeds the third preset threshold (the third preset threshold is greater than the second preset threshold), the system cannot judge whether there is a problem with the equipment based solely on the defect type ratio. However, the yield fluctuation level is positively correlated with the yield decline caused by equipment factors, that is, the higher the yield fluctuation level, the greater the probability of equipment problems. Therefore, when the yield fluctuation level is high and the third defect type ratio exceeds the third preset threshold, the system can issue a maintenance warning signal after a preset time to shorten the equipment maintenance cycle. For example, if the third preset threshold is set to 70%, when the third defect type ratio reaches 72% and the yield fluctuation level is high, the system will adopt a delayed warning strategy to remind you to shorten the maintenance cycle. Similarly, the "preset time" here can be set according to actual conditions, and this embodiment does not limit it.

[0060] In other cases, such as when the yield fluctuation level is low and the proportion of the first defect type does not exceed the preset threshold, we can preliminarily determine that the probability of equipment problems is low. In this case, to maintain the normal production line rhythm, we can choose not to adjust the equipment maintenance cycle, or even appropriately extend the maintenance cycle to avoid unnecessary equipment downtime and improve production efficiency.

[0061] Furthermore, during the operation of the equipment, yield data and defect information are continuously collected, and the above statistical analysis process is repeated regularly.

[0062] This embodiment determines whether to adjust the equipment maintenance cycle by statistically analyzing the yield fluctuation level in a certain time interval in advance before the fixed maintenance cycle of the equipment arrives during the production process of photovoltaic round wire copper strips, and combining it with the type of product defects detected in the time interval. Furthermore, in the statistical analysis process, the yield fluctuation is accurately quantified by eliminating burr data and dynamically adjusting the time interval, thereby more effectively balancing the equipment maintenance frequency and production continuity, avoiding waste of resources and production downtime caused by excessive equipment maintenance, and avoiding a decrease in the yield of the photovoltaic copper strips due to untimely equipment maintenance, thereby achieving a good balance between the production efficiency of the photovoltaic copper strip production line and the yield of the photovoltaic copper strips.

[0063] Corresponding to the control method of a photovoltaic round wire copper strip production equipment in the above embodiment, Figure 2 A structural block diagram of a control system of a photovoltaic round wire copper strip production device provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0064] See also Figure 2 As shown, the control system 200 of the photovoltaic round wire copper strip production equipment provided in the embodiment of the present application includes:

[0065] a first statistical module, configured to collect statistics on the yield of photovoltaic round copper strips in a first time interval T to obtain first statistical data, and calculate a yield fluctuation level in the first time interval based on the first statistical data;

[0066] The second statistical module is configured to obtain second statistical data by counting the defect types of the defective copper strips in the first time interval T, and obtain a proportion of the defect types of the defective copper strips based on the second statistical data.

[0067] The defect types include a first defect type, a second defect type, and a third defect type, wherein the first defect type refers to a defect mainly caused by a first factor, the second defect type refers to a defect mainly caused by a second factor, and the third defect type refers to other defect types except the first defect type and the second defect type, wherein the first factor is a device problem and the second factor is a non-device problem;

[0068] An adjustment module is used to adjust or not adjust the equipment maintenance cycle based on the yield fluctuation level and the defect type proportion.

[0069] Accordingly, an embodiment of the present application further provides an electronic device, which may be a terminal or a server. Figure 3 As shown, Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0070] The electronic device 300 includes a processor 301 having one or more processing cores, a memory 302 having one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 is electrically connected to the memory 302. Those skilled in the art will appreciate that the electronic device structure shown in the figures does not limit the electronic device and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0071] The processor 301 is the control center of the electronic device 300. It uses various interfaces and lines to connect various parts of the entire electronic device 300. By running or loading software programs (computer programs) and / or units stored in the memory 302, and calling data stored in the memory 302, it executes various functions of the electronic device 300 and processes data, thereby monitoring the electronic device 300 as a whole.

[0072] In the embodiment of the present application, the processor 301 in the electronic device 300 loads instructions corresponding to one or more application processes into the memory 302 according to the following steps, and the processor 301 runs the application stored in the memory 302 to implement various functions:

[0073] Counting the yield of photovoltaic round copper strips in a first time interval T to obtain first statistical data, and calculating the yield fluctuation level in the first time interval based on the first statistical data;

[0074] Counting the defect types of the bad copper strips in the first time interval T to obtain second statistical data, and obtaining the proportion of the defect types of the bad copper strips based on the second statistical data,

[0075] The defect types include a first defect type, a second defect type, and a third defect type, wherein the first defect type refers to a defect caused by a first factor, the second defect type refers to a defect caused by a second factor, and the third defect type refers to other defect types except the first defect type and the second defect type.

[0076] The first factor is a device problem, and the second factor is a non-device problem;

[0077] Based on the yield fluctuation level and the defect type proportion, the equipment maintenance cycle is adjusted or not adjusted.

[0078] The specific implementation of the above operations can be found in the aforementioned embodiments and will not be described again here.

[0079] Optional, such as Figure 3 As shown, the electronic device 300 further includes: a control unit 303, a communication unit 304, an input unit 305 and a power supply 306. The processor 301 is electrically connected to the control unit 303, the communication unit 304, the input unit 305 and the power supply 306 respectively. It can be understood by those skilled in the art that Figure 3 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0080] The control unit 303 can be used to control the production equipment of photovoltaic round wire copper strips.

[0081] The communication unit 304 may be used to communicate with other devices.

[0082] The input unit 305 can be used to receive input numbers, character information or user feature information (such as fingerprint, iris, facial information, etc.), and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0083] Power supply 306 is used to supply power to various components of electronic device 300. Optionally, power supply 306 can be logically connected to processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. Power supply 306 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0084] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0085] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0086] To this end, an embodiment of the present application provides a computer-readable storage medium, which stores multiple computer programs. The computer programs can be loaded by a processor to execute the steps of a control method for photovoltaic round wire copper strip production equipment provided in an embodiment of the present application.

[0087] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0088] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0089] Since the computer program stored in the storage medium can execute the steps in the control method of any photovoltaic round wire copper strip production equipment provided in the embodiments of the present application, the beneficial effects of the control method of any photovoltaic round wire copper strip production equipment provided in the embodiments of the present application can be achieved. Please see the previous embodiments for details and will not be repeated here.

[0090] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the flowchart. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0091] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0093] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It is apparent to those skilled in the art that various changes, modifications, substitutions, and variations to these embodiments may be made without departing from the principles and spirit of the present invention, and these changes and modifications still fall within the scope of protection of the present invention.

Claims

1. A control method for production equipment of photovoltaic round wire copper strip, characterized in that: include: Counting the yield of photovoltaic round copper strips in a first time interval T to obtain first statistical data, and calculating the yield fluctuation level in the first time interval based on the first statistical data; Counting defect types of the defective copper strip in the first time interval T to obtain second statistical data, and obtaining a proportion of defect types of the defective copper strip based on the second statistical data, wherein the defect types include a first defect type, a second defect type, and a third defect type, wherein the first defect type refers to a defect mainly caused by a first factor, the second defect type refers to a defect mainly caused by a second factor, and the third defect type refers to other defect types except the first defect type and the second defect type, wherein the first factor is an equipment problem and the second factor is a non-equipment problem; Based on the yield fluctuation level and the defect type proportion, the equipment maintenance cycle is adjusted or not adjusted.

2. The control method for the production equipment of photovoltaic round wire copper strip according to claim 1, characterized in that: The method further comprises: The first time interval T is shorter than the regular maintenance period of the equipment or shorter than the previous maintenance period of the equipment, Among them, for equipment that is put into operation for the first time, the length of the first time interval is less than the regular maintenance cycle of the equipment; for equipment that is not put into operation for the first time, the length of the first time interval is less than the previous maintenance cycle of the equipment.

3. The control method for the production equipment of photovoltaic round wire copper strip according to claim 1, characterized in that: The method further comprises: The first time interval is divided into a plurality of subintervals t, and the yield y corresponding to each subinterval t is counted.

4. The control method for production equipment of photovoltaic round wire copper strip according to claim 1, characterized in that: The method further comprises: Based on a preset threshold, the calculated yield fluctuation value is divided into several levels to obtain the yield fluctuation level, wherein the yield fluctuation value Y is determined by the mean m and / or fluctuation v of the yield y corresponding to each sub-interval t.

5. The control method for production equipment of photovoltaic round wire copper strip according to claim 4, characterized in that: The method further comprises: Correcting the yield fluctuation value Y includes: removing burrs from the yield y of the subinterval t, wherein the burrs are the yield y being less than a first preset threshold or greater than a second preset threshold, The yield fluctuation value Y is determined based on a plurality of yields after burr removal.

6. The control method for production equipment of photovoltaic round wire copper strip according to claim 4, characterized in that: The method further comprises: Dynamically adjust the duration of the first time interval T based on the health of the device, including: The duration of the first time interval T is positively correlated with the health of the device, that is, the higher the health of the device, the longer the duration of the first time interval T.

7. The control method for production equipment of photovoltaic round wire copper strip according to claim 4, characterized in that: The method further comprises: The adjusting or not adjusting the equipment maintenance cycle based on the yield fluctuation level and the defect type proportion includes: If the yield fluctuation level is high and the proportion of the first defect type exceeds the first preset threshold, Alternatively, the yield fluctuation level is medium, and the proportion of the first defect type exceeds the second preset threshold, Alternatively, the yield fluctuation level is high, and the proportion of the third type exceeds the third preset threshold. The equipment maintenance cycle is shortened, wherein the first preset threshold is less than the second preset threshold, and the second preset threshold is less than the third preset threshold, Otherwise, the equipment maintenance cycle will not be adjusted.

8. The control method for photovoltaic round copper strip production equipment according to claim 4, characterized in that: The method further comprises: The yield fluctuation value Y is determined by the mean and / or fluctuation of the yield y of the plurality of sub-intervals t, including: The yield fluctuation value Y is the mean value m of the yield y of the plurality of subintervals t. Or, the yield fluctuation value Y is the fluctuation rate v of the yield y of the plurality of subintervals t, Alternatively, the yield fluctuation value Y is a weighted calculation of the mean m and the fluctuation rate v.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is executed.

10. An electronic device, characterized in that: The method comprises a memory storing executable program code and a processor coupled to the memory; wherein the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1 to 8.

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