A control method of a production equipment of a photovoltaic round wire copper strip

Abstract

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

Description

Technical Field

[0001] This invention relates to the field of manufacturing equipment control technology, and specifically to a control method for a photovoltaic round wire copper strip production equipment. Background Technology

[0002] In photovoltaic module manufacturing, the production quality of solder strips is crucial. Solder strips are made from round copper wire through multiple processes such as rolling, drawing, annealing, and wire storage. During the production of photovoltaic round copper wire, the equipment maintenance cycle has a significant impact on 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 may lead to over-maintenance or under-maintenance. Over-maintenance will waste resources and affect production cycle and production line utilization; while under-maintenance may lead to equipment failure, resulting in insufficient product yield and affecting product quality.

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

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

[0006] According to one aspect of this disclosure, a control method for a production equipment for photovoltaic round wire copper strip is proposed, comprising:

[0007] The yield of photovoltaic round wire copper strip in the first time interval T is statistically analyzed to obtain the first statistical data. Based on the first statistical data, the yield fluctuation level of the first time interval is calculated.

[0008] The second statistical data is obtained by statistically analyzing the types of defective copper strips in the first time interval T, and the percentage of each defective copper strip type is obtained based on the second statistical data.

[0009] The defect types include a first defect type, a second defect type, and a third defect type. The first defect type refers to a defect primarily caused by a first factor; the second defect type refers to a defect primarily caused by a second factor; and the third defect type refers to any other defect type besides the first and second defect types.

[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 proportion of the defect type, the equipment maintenance cycle may or may not be adjusted.

[0012] Optionally, the method further includes ensuring that the first time interval T is less than the equipment's regular maintenance cycle or less than the equipment's previous maintenance cycle.

[0013] For equipment put into operation for the first time, the first time interval is shorter than the equipment's regular maintenance cycle; for equipment not put into operation for the first time, the first time interval is shorter than the equipment's previous maintenance cycle.

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

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

[0016] Optionally, the method further includes correcting the yield fluctuation value Y, including removing glitch in the yield y of the sub-interval t, wherein the glitch is 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 glitch.

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

[0018] The duration of the first time interval T is positively correlated with the health status of the device; that is, the higher the relative health status 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 ratio, 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] This shortens the equipment maintenance cycle, where 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 determining the yield fluctuation value Y by the mean and / or volatility of the yield y over a plurality of sub-intervals t, including,

[0026] The yield fluctuation value Y is the mean m of the yield y of multiple sub-intervals t.

[0027] Alternatively, the yield fluctuation value Y is the fluctuation rate v of the yield y over multiple sub-intervals t.

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

[0029] This disclosure also provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to perform the steps of the method described in any of the above embodiments.

[0030] This application also provides an electronic device, which includes a memory and a processor. 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] This invention discloses a control method for photovoltaic round wire copper strip production equipment. The method includes: statistically analyzing the yield of photovoltaic round wire copper strip within a first time interval, calculating the yield fluctuation value and classifying the fluctuation level; statistically analyzing the defect types of defective products within this time interval, distinguishing between defects mainly caused by equipment problems, non-equipment problems, or other situations, and calculating the proportion of each defect type; and dynamically adjusting the equipment maintenance cycle based on the yield fluctuation level and the defect type proportion. This method can effectively balance equipment maintenance frequency and product yield, improve equipment utilization, increase product yield, and ensure the stability of photovoltaic round wire copper strip production capacity and product quality. Attached Figure Description

[0032] Figure 1 A schematic diagram of a control method for a photovoltaic round wire copper strip production equipment provided in this application embodiment;

[0033] Figure 2 A schematic diagram of a control system for a photovoltaic round wire copper strip production equipment provided in this application embodiment;

[0034] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings. It should be noted that these descriptions are for the purpose of aiding understanding the present invention, but do not constitute a limitation thereof. Furthermore, the technical features involved in the various embodiments of the present invention described below can 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 designations are used for ease of description and are not intended to limit the scope of the embodiments of this application. For example, they may distinguish different classification results, rather than describing a specific order or sequence. It should be understood that such described objects can be interchanged where appropriate to describe solutions other than those in the embodiments of this application.

[0037] Specifically, Figure 1 This document illustrates a flowchart detailing the control method for a photovoltaic round wire copper strip production equipment according to an embodiment of this application. Please refer to [link / reference]. Figure 1 The control method for the photovoltaic round wire copper strip production equipment provided in this application has the following specific steps:

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

[0039] Step S2: Statistically analyze the types of defective copper strips in the first time interval T to obtain second statistical data. Based on the second statistical data, calculate the percentage of each defective copper strip type.

[0040] The defect types include a first defect type, a second defect type, and a third defect type. The first defect type refers to a defect primarily caused by a first factor; the second defect type refers to a defect primarily caused by a second factor; and the third defect type refers to any other defect type besides the first and second defect types.

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

[0042] Step S3: Adjust or not adjust the equipment maintenance cycle based on the yield fluctuation level and the proportion of defect types.

[0043] In some embodiments, the first time interval T can be determined based on the equipment's operating characteristics and historical maintenance records. For example, for equipment 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 not 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 times can be adjusted according to actual conditions. Within the time interval that is 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, thus proactively controlling the equipment maintenance cycle.

[0044] Within a defined first time interval T, production data for photovoltaic round copper strips are collected, including product yield data and defect type data for substandard copper strips. Product yield data can be obtained through a quality inspection system. Specifically, during the production process of photovoltaic round copper strips, within the first time interval T, the photovoltaic round copper strips after key processes such as rolling and drawing are inspected. In some embodiments, this inspection can be performed using automated inspection devices.

[0045] In some embodiments, the first time interval T can be divided into multiple sub-intervals t, and each sub-interval t corresponds to the generation of the yield y for that sub-interval t. For example, if T is 20 days, it is divided into 10 sub-intervals t, each t being 2 days, and the yield y1 to y10 are calculated for each day. The yield fluctuation value Y is determined by the yield mean m and yield fluctuation rate v of the yield y for each sub-interval t. It is understood that the yield fluctuation value Y can be characterized in various ways, such as the yield mean and yield fluctuation rate in this embodiment, or it can be represented by interquartile range, moving average, autocorrelation function, etc., and this application does not limit it.

[0046] In one embodiment, the yield fluctuation value Y = mean m, and the formula for calculating m is 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, and the formula for calculating v is as follows:

[0049] v = y max -y min / y min , where y max y represents the maximum yield within the first time interval T. min This represents the minimum yield within the first time interval T.

[0050] In some embodiments, the yield fluctuation value can also be based on a weighted sum of the yield mean m and the yield fluctuation v, specifically Y = αm + βv, where α and β represent weighting 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] Understandably, factors leading to decreased yield can include raw material quality, production process parameters, and equipment stability. Yield changes corresponding to equipment instability are typically less predictable, with yield data fluctuating significantly over a given timeframe. For example, equipment issues often include: wear on the drawing die causing deviations in wire diameter (e.g., localized thinning or excessive ellipticity); mechanical vibration causing scratches or dimensional fluctuations on the copper strip surface; residual metal debris or grease contaminating the copper strip surface, creating defects; and misalignment of the equipment causing abnormal wire hardness or insufficient ductility, leading to breakage. Within a given timeframe, raw materials are usually from the same batch, and process parameter formulations are fixed. Therefore, yield fluctuations primarily caused by raw materials and process parameter formulations are generally more stable than those primarily caused by equipment issues. In other words, within a given timeframe, yield fluctuations primarily caused by equipment issues are usually of a higher degree than those primarily caused 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 corresponds to a lower yield fluctuation level, and vice versa. For instance, 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, a smaller yield fluctuation rate v corresponds to a lower yield fluctuation level, while a larger yield fluctuation rate v corresponds to a higher yield fluctuation level. For instance, a fluctuation rate v less than 10% is low; a fluctuation rate v between 10% and 30% is medium; and a fluctuation rate v greater than 30% is high. Furthermore, the yield fluctuation value Y can also be derived from a weighted calculation based on the average yield m and the yield fluctuation rate v, which will not be elaborated upon in this embodiment. It is understood that the m and v values ​​corresponding to the above levels can be set according to actual conditions, and this embodiment does not impose any restrictions.

[0053] To avoid the impact of yield spikes caused by statistical errors or other system reasons on the calculation of yield fluctuation values, in some embodiments these spikes are removed before using the above formula to calculate the yield fluctuation value. Here, spikes refer to yield data where the yield y is less than a first threshold or greater than a second threshold. The first and second thresholds can be determined by referring to yield data from multiple sub-intervals and combining historical yield data. The first threshold can be 30% and the second threshold can be 95%, thereby correcting the yield fluctuation and making the classification of yield fluctuation levels more accurate.

[0054] In some embodiments, to further improve the accuracy of yield fluctuation level classification, this embodiment also includes dynamically adjusting the duration of the first time interval T based on the health status of the equipment. For example, assuming the equipment has been in operation for 3 years and the number of repairs to key components exceeds a preset threshold, the equipment health status is relatively low, and the first time interval T is set to a relatively short duration, such as 5 days. If the equipment has been in operation for less than 1 year and the number of repairs to key components 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 equipment health has previously experienced problems, even after repair, its operational stability will be somewhat compromised. Under the same conditions, obviously, the more repairs, the greater the stability compromise. In this embodiment, the equipment health prediction considers not only the length of time the equipment has been in operation but also the number of repairs to key components during equipment operation, further improving the accuracy of yield fluctuation level classification. It is understood that key components of the equipment may include, but are not limited to, wire drawing dies, annealing and cooling mechanisms, and calendering mechanisms.

[0055] Furthermore, the statistics and percentage calculations for defective product types are as follows: Specifically, within the same first time interval T, the defect types of detected defective products are statistically analyzed to obtain second statistical data. Defect types include, but are not limited to, the following three categories: (a) First defect type: Defects mainly caused by equipment problems, for example, including but not limited to wear of drawing dies, localized thinning of diameter or excessive ellipticity caused by equipment mechanical vibration, residual metal debris or grease contamination, etc.; (b) Second defect type: Defects mainly caused by non-equipment problems, for example, including but not limited to fluctuations in raw material quality, abnormal process parameters, etc.; (c) Third defect type: Other defect types besides the first and second defect types, i.e., other defect types not identified as the first or second defect type. It is understood that the situations classified as the third defect type include, but are not limited to, the following: One situation is that, besides those mainly caused by equipment problems or... Defects primarily caused by non-equipment issues may also be caused by a combination of equipment and non-equipment problems, making it difficult to distinguish which problem is more likely to cause the defect. For example, defects caused by a combination of equipment and process parameter issues are difficult to differentiate, such as scratches and voids on the copper strip surface caused by the combined effects of drawing dies and raw material impurities, or cracks on the copper strip surface caused by improper coolant formulation and equipment tension component problems. The second scenario involves defects where, due to limited detection and identification accuracy, the algorithm itself may not be able to accurately distinguish between the first and second defect types. These are uniformly classified as the third defect type. Furthermore, the frequency of each defect type is statistically analyzed, and the proportion of each type to the total number of defective products is calculated, i.e., the defect type percentage.

[0056] In some embodiments, the identification of defect types in defective products can be achieved automatically through machine vision inspection, resistance and conductivity testing, tensile testing, and mechanical performance analysis. For hidden defects or undetected special defects, manual sampling can be used as a supplementary means to automatic detection. Specifically, for example, product appearance image data can be acquired through an image inspection device, resistance and conductivity data can be acquired through an electrical testing device, and product tensile data can be acquired through a tensile testing device. The acquired data is 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 based on the input data. The deep learning model and its training process can use existing models and corresponding existing training methods, which will not be elaborated upon or limited in this application.

[0057] Furthermore, by combining the yield fluctuation level and defect type percentage information, the system determines whether to adjust the equipment maintenance cycle. Specifically, when the yield fluctuation level is high, it indicates that there are significant unstable factors in the production process. If the percentage of the first defect type (defects mainly caused by equipment problems) exceeds a first preset threshold, it can be clearly determined that the equipment problem is the main cause of the yield decline. In this case, the system will immediately issue a maintenance warning signal and automatically adjust the equipment maintenance plan, shortening the next maintenance time to resolve the equipment problem as soon as possible and restore production stability. For example, assuming the first preset threshold is set to 50%, when the percentage 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 a significant factor leading to a decrease in yield, the yield fluctuation level is not the highest. In this case, the system can issue a maintenance warning signal after a preset time to arrange for a shorter equipment maintenance cycle in advance. 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 users to shorten the maintenance cycle after a certain period of time. This allows for timely handling of potential equipment problems without affecting production schedules. The "certain period of time" can be set according to actual conditions; this embodiment does not limit it.

[0059] For the third defect type, the percentage information is insufficient to directly reflect the factors causing product defects. When the percentage of the third defect type exceeds the third preset threshold (which is greater than the second preset threshold), the system cannot determine whether the equipment has a problem based solely on the percentage. However, the yield fluctuation level is positively correlated with the yield decrease 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 percentage of the third defect type 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 percentage of the third defect type reaches 72% and the yield fluctuation level is high, the system will adopt a delayed warning strategy to remind users 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, it can be preliminarily judged that the probability of equipment problems is small. In this case, in order to maintain the normal cycle time of the production line, it is possible to choose not to adjust the equipment maintenance cycle, or even appropriately extend the maintenance cycle, in order to avoid unnecessary equipment downtime and improve production efficiency.

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

[0062] This embodiment analyzes the yield fluctuation level within a certain time interval before the fixed maintenance cycle of the equipment during the photovoltaic round wire copper strip production process. Combined with the types of product defects detected within this time interval, it determines whether to adjust the equipment maintenance cycle. Furthermore, during the statistical analysis process, by eliminating burr data and dynamically adjusting the time interval, the yield fluctuation is accurately quantified, thereby more effectively balancing the equipment maintenance frequency and production continuity. This avoids both excessive equipment maintenance leading to resource waste and production downtime, and untimely equipment maintenance leading to a decrease in the yield of photovoltaic copper strips. It effectively balances the production efficiency of the photovoltaic copper strip production line and the yield of photovoltaic copper strips.

[0063] A control method for a photovoltaic round wire copper strip production equipment corresponding to the above embodiment, Figure 2 This diagram illustrates the structural block diagram of a control system for a photovoltaic round wire copper strip production equipment according to an embodiment of this application. For ease of explanation, only the parts related to the embodiment of this application are shown.

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

[0065] The first statistical module is used to obtain the first statistical data by statistically analyzing the yield of photovoltaic round wire copper strip in the first time interval T, and to calculate the yield fluctuation level in the first time interval based on the first statistical data.

[0066] The second statistical module is used to statistically analyze the types of defective copper strips during the first time interval T to obtain second statistical data, and to calculate the percentage of each defective copper strip type based on the second statistical data.

[0067] The defect types include a first defect type, a second defect type, and a third defect type. 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. The third defect type refers to other defect types besides the first and second defect types. The first factor is a device problem, and the second factor is a non-device problem.

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

[0069] Accordingly, embodiments of this application also provide an electronic device, which can be a terminal or a server. For example... Figure 3 As shown, Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0070] The electronic device 300 includes a processor 301 with one or more processing cores, a memory 302 with one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 and the memory 302 are electrically connected. Those skilled in the art will understand that the electronic device structure shown in the figures does not constitute a limitation on the electronic device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0071] The processor 301 is the control center of the electronic device 300. It connects various parts of the electronic device 300 through various interfaces and lines. 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 performs various functions of the electronic device 300 and processes data, thereby monitoring the electronic device 300 as a whole.

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

[0073] The yield of photovoltaic round wire copper strip in the first time interval T is statistically analyzed to obtain the first statistical data. Based on the first statistical data, the yield fluctuation level of the first time interval is calculated.

[0074] The second statistical data is obtained by statistically analyzing the types of defective copper strips in the first time interval T, and the percentage of each defective copper strip type is obtained based on the second statistical data.

[0075] The defect types include a first defect type, a second defect type, and a third defect type. 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 any defect type other than the first and second defect types.

[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 proportion of the defect type, the equipment maintenance cycle may or may not be adjusted.

[0078] For details on the specific implementation of each of the above operations, please refer to the aforementioned embodiments, which will not be repeated here.

[0079] Optional, such as Figure 3 As shown, the electronic device 300 also 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. Those skilled in the art will understand that... Figure 3 The electronic device structure shown does not constitute a limitation on the electronic device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

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

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

[0082] The input unit 305 can be used to receive input numbers, characters, or user characteristic information (such as fingerprints, iris, facial information, etc.), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs 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 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. Power supply 306 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0084] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

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

[0086] Therefore, embodiments of this application provide a computer-readable storage medium storing multiple computer programs that can be loaded by a processor to execute the steps of a control method for a photovoltaic round wire copper strip production equipment provided in embodiments of this application.

[0087] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0088] The computer-readable storage medium may include: read-only memory (ROM), random access memory (RAM), disk or 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 this application, the beneficial effects of the control method of any photovoltaic round wire copper strip production equipment provided in the embodiments of this application can be achieved. For details, please refer to the previous embodiments, which will not be repeated here.

[0090] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0091] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0092] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

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

Claims

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

2. The control method for the photovoltaic round wire copper strip production equipment according to claim 1, characterized in that, The method further includes: the first time interval T is less than the equipment's regular maintenance cycle or less than the equipment's previous maintenance cycle, wherein, for equipment put into operation for the first time, the first time interval T is less than the equipment's regular maintenance cycle, and for equipment not put into operation for the first time, the first time interval T is less than the equipment's previous maintenance cycle.

3. The control method for the photovoltaic round wire copper strip production equipment according to claim 1, characterized in that, The method further includes: dividing the first time interval T into multiple sub-intervals t, and calculating the yield y corresponding to each sub-interval t.

4. The control method for the photovoltaic round wire copper strip production equipment according to claim 3, characterized in that, The method further includes: dividing the calculated yield fluctuation value Y into several levels based on a pre-set threshold to obtain the yield fluctuation level, wherein the yield fluctuation value Y is determined by the mean m of the yield y and / or the fluctuation rate v corresponding to each sub-interval t.

5. The control method for the photovoltaic round wire copper strip production equipment according to claim 4, characterized in that, The method further includes: correcting the yield fluctuation value Y, including removing spikes in the yield y of the sub-interval t, wherein spikes are yields where the yield y is less than a first preset threshold or greater than a second preset threshold. The yield fluctuation value Y is determined based on multiple yields after burr removal.

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

7. The control method for the photovoltaic round wire copper strip production equipment according to claim 4, characterized in that, The method further includes: adjusting or not adjusting the equipment maintenance cycle based on the yield fluctuation level and the defect type proportion, comprising: if the yield fluctuation level is high and the proportion of the first defect type exceeds a first preset threshold; or, if the yield fluctuation level is medium and the proportion of the first defect type exceeds a second preset threshold; or, if the yield fluctuation level is high and the proportion of the third defect type exceeds a third preset threshold, then shortening the equipment maintenance cycle, 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 the photovoltaic round wire copper strip production equipment according to claim 4, characterized in that, The method further includes: the yield fluctuation value Y is determined by the mean m and / or volatility v of the yield y of the multiple sub-intervals t, including, the yield fluctuation value Y is the mean m of the yield y of the multiple sub-intervals t, or, the yield fluctuation value Y is the volatility v of the yield y of the multiple sub-intervals t, or, the yield fluctuation value Y is a weighted calculation of the mean m and the volatility v.

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

10. An electronic device, characterized in that: The method includes a memory storing executable program code and a processor coupled to the memory; wherein the processor invokes the executable program code stored in the memory to perform the method as described in any one of claims 1-8.

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