Temperature monitoring method for reflow soldering furnace, electronic equipment and storage medium

Abstract

The invention provides a temperature monitoring method of a reflow soldering furnace, electronic equipment and a storage medium. The method comprises the following steps: acquiring a standard furnace temperature curve of the reflow soldering furnace; acquiring a plurality of historical furnace temperature curves of the reflow soldering furnace, wherein the historical furnace temperature curves comprise furnace temperature curves when the reflow soldering furnace is used for processing to obtain normal products; determining a similarity threshold value between the standard furnace temperature curve and the plurality of historical furnace temperature curves; and the similarity between the real-time furnace temperature curve and the standard furnace temperature curve is determined, and if the similarity is smaller than the similarity threshold value, it is determined that the furnace temperature of the reflow oven is abnormal. The method can assist in monitoring the temperature of the reflow soldering furnace, and improves the temperature monitoring accuracy of the reflow soldering furnace.

Description

Technical Field

[0001] The present application relates to the technical field of reflow soldering temperature monitoring, and in particular to a reflow soldering furnace temperature monitoring method, electronic equipment, and storage medium. Background Art

[0002] Currently, reflow oven temperature monitoring is mostly performed using oven temperature testers such as those from KIC. However, this temperature monitoring method suffers from low accuracy, making it difficult to effectively control the reflow oven temperature and guarantee the yield of reflow products. Summary of the Invention

[0003] In view of the above, it is necessary to propose a reflow oven temperature monitoring method, electronic device and storage medium, which can solve the problem of low yield of reflow oven products due to low accuracy of temperature monitoring methods in related technologies.

[0004] An embodiment of the present application provides a reflow soldering furnace temperature monitoring method, the method comprising: obtaining a standard furnace temperature curve of the reflow soldering furnace; obtaining multiple historical furnace temperature curves of the reflow soldering furnace, the historical furnace temperature curves including furnace temperature curves when normal products are processed using the reflow soldering furnace; determining a similarity threshold between the standard furnace temperature curve and the multiple historical furnace temperature curves; determining the similarity between a real-time furnace temperature curve and the standard furnace temperature curve, and if the similarity is less than the similarity threshold, determining that the furnace temperature of the reflow soldering furnace is abnormal.

[0005] In one embodiment, obtaining the standard furnace temperature curve of the reflow oven includes generating the standard furnace temperature curve according to the processing time and the standard furnace temperature of each temperature zone in the reflow oven.

[0006] In one embodiment, the temperature zones of the reflow oven include a preheating zone, a constant temperature zone, a reflow zone, and a cooling zone; the standard oven temperature includes a first temperature when entering each temperature zone and a second temperature when leaving each temperature zone.

[0007] In one embodiment, determining the similarity threshold between the standard furnace temperature curve and the multiple historical furnace temperature curves includes: determining the similarity between the standard furnace temperature curve and each historical furnace temperature curve based on a preset similarity calculation method; and determining the similarity threshold according to the minimum similarity value.

[0008] In one embodiment, the similarity calculation method includes a fast dynamic time warping (Fast-DTW) algorithm.

[0009] In one embodiment, determining the similarity between the standard furnace temperature curve and each historical furnace temperature curve includes: performing coarse-graining, projection, and fine-graining processing on the standard furnace temperature curve and each historical furnace temperature curve respectively to obtain the similarity between the standard furnace temperature curve and each historical furnace temperature curve.

[0010] In one embodiment, determining the similarity between the real-time furnace temperature curve and the standard furnace temperature curve includes: performing coarse-graining, projection, and fine-graining processing on the standard furnace temperature curve and the real-time furnace temperature curve to obtain the similarity between the standard furnace temperature curve and the real-time furnace temperature curve.

[0011] In one embodiment, after determining that the temperature of the reflow oven is abnormal, the method further includes: stopping the processing of the reflow oven; feeding back the abnormality to the temperature control system of the reflow oven; and adjusting the temperature of the reflow oven using the temperature control system.

[0012] An embodiment of the present application provides a reflow soldering furnace temperature monitoring device, which includes: an acquisition module for acquiring a standard furnace temperature curve of the reflow soldering furnace; the acquisition module is also used to acquire multiple historical furnace temperature curves of the reflow soldering furnace, and the historical furnace temperature curves include the furnace temperature curve when a normal product is processed using the reflow soldering furnace; a determination module is used to determine a similarity threshold between the standard furnace temperature curve and the multiple historical furnace temperature curves; the determination module is also used to determine the similarity between the real-time furnace temperature curve and the standard furnace temperature curve. If the similarity is less than the similarity threshold, it is determined that the furnace temperature of the reflow soldering furnace is abnormal.

[0013] An embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the reflow oven temperature monitoring method is implemented.

[0014] An embodiment of the present application provides an electronic device, which includes a memory and at least one processor. The memory stores at least one instruction, and when the at least one instruction is executed by the at least one processor, the reflow oven temperature monitoring method is implemented.

[0015] In summary, the reflow oven temperature monitoring method provided in the embodiments of the present application obtains a standard oven temperature curve for the reflow oven; collects a large number of historical oven temperature curves corresponding to normal products, and determines a similarity threshold between the historical oven temperature curves corresponding to normal products and the standard oven temperature curve by comparing the standard oven temperature curve with the historical oven temperature curves; determines the similarity between the real-time oven temperature curve and the standard oven temperature curve, and if the similarity is less than the similarity threshold, determines that the oven temperature of the reflow oven is abnormal. The present application can determine a similarity threshold with the standard oven temperature curve based on the historical oven temperature curves of normal products, and deem oven temperature curves that exceed the similarity threshold as abnormal curves, thereby achieving accurate abnormal monitoring of the oven temperature of the reflow oven. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0017] Figure 1 This is an architectural diagram of an electronic device provided in an embodiment of the present application.

[0018] Figure 2 This is a flow chart of a reflow oven temperature monitoring method provided in an embodiment of the present application.

[0019] Figure 3 This is an example diagram of the furnace temperature curve provided in the embodiment of the present application.

[0020] Figure 4 This is an example diagram of the Fast-DTW algorithm provided in an embodiment of the present application.

[0021] Figure 5 This is an example diagram for obtaining furnace temperature data provided in an embodiment of the present application.

[0022] Figure 6 This is a flow chart of a reflow oven temperature monitoring method provided in another embodiment of the present application.

[0023] Figure 7 This is a flow chart of a furnace temperature data comparison method provided in one embodiment of the present application.

[0024] Figure 8 This is a structural diagram of a reflow oven temperature monitoring device provided in an embodiment of the present application.

[0025] The following specific implementation methods will further illustrate the present application in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0026] In order to more clearly understand the above-mentioned objectives, features and advantages of the present application, the present application is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features therein can be combined with each other in the absence of conflict.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing embodiments in one embodiment only and are not intended to limit this application.

[0028] It should be noted that in this application, "at least one" means one or more, and "more than one" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0029] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete manner. The following embodiments and features in the embodiments may be combined with each other unless there is a conflict.

[0030] In one embodiment, the temperature of a reflow oven is currently monitored using a furnace temperature tester such as KIC. However, this temperature monitoring method has low accuracy, making it difficult to control the temperature of the reflow oven and ensure the yield of reflow products.

[0031] For example, surface mount technology (SMT) is a common method for manufacturing electronic products, such as printed circuit boards (PCBs). The front-end of SMT involves printing solder paste and mounting electronic components, while the back-end requires high-temperature soldering using a reflow oven. A reflow oven has multiple temperature zones, broadly divided into: a preheating zone, a constant temperature zone (or immersion zone), a reflow zone, and a cooling zone.

[0032] When using a reflow oven for product processing, the product can be placed on the reflow oven's conveyor equipment (e.g., a conveyor belt), which includes multiple rotating shafts and operates at a constant speed. The product undergoing reflow in the reflow oven can be transported into the reflow oven via the conveyor equipment and sequentially pass through the preheating zone, constant temperature zone, reflow zone, and cooling zone.

[0033] In one embodiment, the standard furnace temperature in each temperature zone during reflow soldering of different products can be determined based on user input. For example, when processing a PCB board, the standard furnace temperature in the preheating zone is heated from room temperature (e.g., 26°C) to 140°C, and the slope of the furnace temperature curve needs to be controlled accordingly in the preheating zone; the standard furnace temperature in the constant temperature zone is from 140°C to the melting point of the solder paste, so that all components of the PCB board reach thermal equilibrium; the reflow zone is the area with the highest temperature in the reflow soldering oven, which is generally 30-50°C higher than the melting point of the solder paste; and the cooling zone is mainly used to cool the temperature of the product coming out of the reflow zone to room temperature.

[0034] In one embodiment, the furnace temperature curve can be used to reflect the temperature changes in the reflow oven during the entire reflow process. An unreasonable furnace temperature curve may cause the reflow product (e.g., PCB board) to deform due to uneven heating, crack due to thermal shock, excessive oxidation due to excessive temperature, tin explosion in the reflow area, and other defects including bridging and tombstoning, thereby reducing the product yield.

[0035] While reflow ovens typically have temperature control systems for each temperature zone, the technology typically uses temperature sensors and other devices to monitor the temperature within the reflow oven. When the monitored temperature deviates significantly from the standard oven temperature, feedback is provided to the temperature control system to adjust the temperature. However, this approach cannot accurately determine the difference between the standard oven temperature and the actual processing temperature at each time point. For relatively delicate components or modules, traditional methods such as individual control are increasingly unable to meet the requirements of advanced process production.

[0036] To address the above-mentioned issues, an embodiment of the present application provides a reflow oven temperature monitoring method, which obtains a standard oven temperature curve for the reflow oven; collects a large number of historical oven temperature curves corresponding to normal products, and determines a similarity threshold between the historical oven temperature curves corresponding to normal products and the standard oven temperature curves by comparing the standard oven temperature curves with the historical oven temperature curves; and determines the similarity between the real-time oven temperature curve and the standard oven temperature curve. If the similarity is less than the similarity threshold, it is determined that the oven temperature of the reflow oven is abnormal. The present application can determine a similarity threshold with the standard oven temperature curve based on the historical oven temperature curves of normal products, and treat oven temperature curves that exceed the similarity threshold as abnormal curves, thereby achieving accurate abnormal monitoring of the oven temperature of the reflow oven.

[0037] Figure 1This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device 10 can be a mobile phone, a tablet computer, a smart wearable device, an augmented reality (AR) / virtual reality (VR) device, a laptop computer, a netbook, an energy storage device, a power distribution device, an in-vehicle device, a self-propelled device, or other electronic device. The embodiments of the present application do not impose any restrictions on the specific type of electronic device.

[0038] like Figure 1 As shown, the electronic device 10 may include a communication module 101, a memory 102, a processor 103, an input / output (I / O) interface 104, and a bus 105. The processor 103 is coupled to the communication module 101, the memory 102, and the I / O interface 104 via the bus 105.

[0039] The communication module 101 may include a wired communication module and / or a wireless communication module. The wired communication module may provide one or more wired communication solutions such as universal serial bus (USB) and controller area network bus (CAN). The wireless communication module may provide one or more wireless communication solutions such as wireless fidelity (Wi-Fi), Bluetooth (BT), mobile communication network, frequency modulation (FM), near field communication technology (NFC), infrared technology (IR), etc.

[0040] Memory 102 may include one or more random access memories (RAMs) and one or more non-volatile memories (NVMs). The RAM can be directly read and written by the processor 103 and can be used to store executable programs (e.g., machine instructions) of the operating system or other running programs, as well as user and application data. RAM may include static random-access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc.

[0041] The non-volatile memory can also store executable programs and user and application data, etc., which can be pre-loaded into the random access memory for direct reading and writing by the processor 103. The non-volatile memory can include disk storage devices and flash memory.

[0042] The memory 102 is configured to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include multiple instructions. When the multiple instructions are executed by the processor 103, the reflow oven temperature monitoring method executed on the electronic device 10 can be implemented.

[0043] In other embodiments, the electronic device 10 further includes an external memory interface for connecting to an external memory to expand the storage capacity of the electronic device 10 .

[0044] The processor 103 may include one or more processing units. For example, the processor 103 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU). The different processing units may be independent devices or integrated into one or more processors.

[0045] The processor 103 provides computing and control capabilities. For example, the processor 103 is configured to execute a computer program stored in the memory 102 to implement the above-mentioned reflow oven temperature monitoring method.

[0046] The I / O interface 104 is used to provide a channel for user input or output. For example, the I / O interface 104 can be used to connect various input and output devices, such as a mouse, keyboard, touch device, display screen, etc., so that the user can enter information or visualize information. The I / O interface 104 can also be used to provide a connection with a reflow oven (e.g. Figure 1 The data transmission channel of the reflow soldering furnace 2) shown in the figure, for example, the electronic device can obtain the real-time temperature curve of the product processing from the reflow soldering furnace through the I / O interface 104, control the processing progress of the reflow soldering furnace, send abnormal feedback to the temperature control system of the reflow soldering furnace, control the temperature control system to adjust the temperature of the reflow soldering furnace, etc.

[0047] The bus 105 is at least used to provide a channel for mutual communication among the communication module 101 , the memory 102 , the processor 103 , and the I / O interface 104 in the electronic device 10 .

[0048] It should be understood that the structures illustrated in the embodiments of the present application do not constitute a specific limitation on the electronic device 10. In other embodiments of the present application, the electronic device 10 may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0049] Figure 2 The flow chart of the reflow oven temperature monitoring method provided in the embodiment of the present application is shown in FIG. The reflow oven temperature monitoring method is applied to electronic equipment, such as Figure 1 The electronic device 1 specifically includes the following steps. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0050] Step S31, obtaining a standard furnace temperature curve of a reflow oven.

[0051] In an embodiment of the present application, obtaining a standard temperature curve for a reflow oven includes generating the standard temperature curve based on the processing time and standard temperature of each temperature zone in the reflow oven. The temperature zones of the reflow oven include a preheating zone, a constant temperature zone, a reflow zone, and a cooling zone; the standard temperature includes a first temperature upon entering each temperature zone and a second temperature upon exiting each temperature zone.

[0052] The processing time and standard furnace temperature of each temperature zone can be obtained by receiving user input. The standard furnace temperature can also include a third temperature of multiple time nodes. Each time node represents a time node within the processing time of each temperature zone, and the time interval between each two adjacent time nodes is the same. For example, starting from the start of processing using the reflow oven, the start time node is set to 0 minutes and 0 seconds, then each subsequent time node can be a time node with an interval of 1 minute, such as 1 minute and 0 seconds, 2 minutes and 0 seconds, etc.

[0053] In an embodiment of the present application, multiple historical furnace temperatures of the reflow oven can also be obtained, and the historical furnace temperatures include the furnace temperatures at each time node when the reflow oven is used to process a normal product; the standard furnace temperature at any time node is determined by taking the average of the furnace temperatures of all normal products at any time node. For example, if there are three historical furnace temperatures of normal products, and their historical furnace temperatures at a certain time node are 101°C, 99°C, and 100°C, respectively, then the standard furnace temperature at that time node can be determined as (101°C + 99°C + 100°C) / 3 = 100°C.

[0054] In the embodiments of the present application, since the standard furnace temperature includes a first temperature upon entering each temperature zone and a second temperature upon leaving each temperature zone, the process of changing from the first temperature to the second temperature can be considered a continuous function, with the independent variable of the continuous function being the processing time and the dependent variable being the furnace temperature. Therefore, generating a standard furnace temperature curve can be understood as generating a function curve of this continuous function, which can be plotted based on the slope of the function curve at each discrete independent variable.

[0055] In the embodiment of the present application, the slope of the standard furnace temperature curve corresponding to each time node in each temperature zone can be determined based on the processing time of each temperature zone, the first temperature, the second temperature, and the standard furnace temperature at each time node. The standard furnace temperature curve in each temperature zone is drawn based on the slope, and then the standard furnace temperature curves corresponding to all temperature zones are spliced ​​together so that the standard furnace temperature curve includes the temperature change of the entire reflow soldering process in all temperature zones. For example, Figure 3As shown in the figure, the horizontal axis of the standard furnace temperature curve represents the processing time, and the vertical axis represents the furnace temperature.

[0056] In an embodiment of the present application, after drawing the standard furnace temperature curve, in order to improve the smoothness of the standard furnace temperature curve, a smoothing algorithm can be used to smooth the standard furnace temperature curve. For example, the smoothing algorithm can include but is not limited to a Lagrangian algorithm and a Newton interpolation algorithm.

[0057] It should be noted that since the temperature in the preheating zone of the reflow oven starts at room temperature and the temperature in the cooling zone gradually decreases to room temperature, the standard oven temperature at the beginning and end of the reflow process is related to the room temperature. Different standard oven temperature curves can be determined based on different room temperatures. When using standard oven temperature curves for anomaly comparison, the standard oven temperature curve corresponding to the current room temperature can be selected to eliminate the impact of different room temperatures and improve the accuracy of oven temperature anomaly detection.

[0058] Step S32: Acquire multiple historical temperature curves of the reflow oven.

[0059] In an embodiment of the present application, the historical furnace temperature curve includes the furnace temperature curve when a normal product is processed using the reflow oven. The multiple (for example, 10,000) historical furnace temperature curves can be obtained using a method similar to that of obtaining a standard furnace temperature curve, and multiple historical furnace temperature curves input by a user can also be received.

[0060] In the embodiments of the present application, normal products refer to products without abnormalities, such as PCB boards without abnormalities. Since the yield of normal products is relatively high in actual production, it is easier to obtain multiple historical furnace temperature curves for a large number of normal products than to obtain furnace temperature curves for a large number of abnormal products.

[0061] Step S33: determining a similarity threshold between the standard furnace temperature curve and the plurality of historical furnace temperature curves.

[0062] In the embodiment of the present application, since the historical furnace temperature curve is the furnace temperature curve corresponding to the normal product produced, it can be considered that the difference between each historical furnace temperature curve and the standard furnace temperature curve at each time node is small, that is, the overall similarity between each historical furnace temperature curve and the standard furnace temperature curve is high. Therefore, by determining the similarity between the standard furnace temperature curve and a large number of historical furnace temperature curves, a similarity threshold that can be used for furnace temperature anomaly monitoring can be determined. For example Figure 3 As shown in Figure 2, the overall similarity between the historical furnace temperature curve and the standard furnace temperature curve is very high.

[0063] In an embodiment of the present application, determining a similarity threshold between the standard furnace temperature curve and the plurality of historical furnace temperature curves includes: determining the similarity between the standard furnace temperature curve and each of the historical furnace temperature curves based on a preset similarity calculation method; and determining the similarity threshold based on a minimum similarity value. For example, the minimum similarity value can be used as the similarity threshold (e.g., 0.9).

[0064] In an embodiment of the present application, the similarity calculation method includes but is not limited to a Fast Dynamic Time Warping (Fast-DTW) algorithm. Determining the similarity between the standard furnace temperature curve and each historical furnace temperature curve includes: performing coarse-graining, projection, and fine-graining processing on the standard furnace temperature curve and each historical furnace temperature curve, respectively, to obtain the similarity between the standard furnace temperature curve and each historical furnace temperature curve.

[0065] Specifically, the DTW algorithm is a distance metric used to compare two time series. It aligns sequences of different lengths on a time axis and calculates the similarity between them. The basic idea of ​​the DTW algorithm is to match corresponding elements of two sequences and find the optimal alignment between them to minimize the total distance. By defining a cumulative distance matrix, the DTW algorithm calculates the local distance between each pair of elements and combines it with the previously calculated distance values ​​to obtain the minimum cumulative distance.

[0066] Since each furnace temperature in the furnace temperature curve (such as the standard furnace temperature curve and the historical furnace temperature curve) is regarded as the data of each time node in a time series, the DTW algorithm can be used to measure the distance between different furnace temperature curves, and the similarity between different furnace temperatures can be determined based on the inverse of the distance.

[0067] When both time series are long, the basic DTW algorithm has high complexity and low efficiency. You can use an algorithm that accelerates DTW to improve the efficiency of similarity calculation, including but not limited to the aforementioned Fast-DTW algorithm, or algorithms such as SparseDTW, LB_Keogh, and LB_Improved.

[0068] In the embodiment of the present application, the Fast-DTW algorithm uses a combination of restriction and data abstraction to accelerate the calculation of the DTW algorithm, which mainly includes three steps: (1) coarse-graining: that is, firstly perform data abstraction on the original time series, for example Figure 4As shown in Figure 1, data abstraction can be iterated multiple times 1 / 1→1 / 2→1 / 4→1 / 8. When the pixels are merged into 1 / 8, the path can be determined, that is, the coarse-grained data point from the lower left corner to the upper right corner is the average of its corresponding multiple fine-grained data points; (2) Projection: Run the DTW algorithm on the time series at a coarser granularity; (3) Fine-graining: The grids passed by the normalized path obtained at the coarser granularity are further fine-grained to the finer granularity time series, for example Figure 4 As shown, the path under the pixel is determined from 1 / 8 back to 1 / 4, then 1 / 2, and finally 1 / 1; in addition to fine-graining, K granularities can be expanded outward (including horizontal, vertical, and diagonal directions) within the finer-grained space, where K is a radius parameter, which can be set to 1 or 2, for example.

[0069] In an embodiment of the present application, the Fast-DTW algorithm may be implemented using a Python program, and the called functions may include but are not limited to: __expand_window, dtw, and __reduce_by_half.

[0070] Step S34, determining the similarity between the real-time furnace temperature curve and the standard furnace temperature curve, and if the similarity is less than the similarity threshold, determining that the furnace temperature of the reflow oven is abnormal.

[0071] In an embodiment of the present application, determining the similarity between the real-time furnace temperature curve and the standard furnace temperature curve includes performing coarse-graining, projection, and fine-graining processing on the standard furnace temperature curve and the real-time furnace temperature curve to obtain the similarity between the standard furnace temperature curve and the real-time furnace temperature curve. For details, please refer to the introduction of the Fast-DTW algorithm in the above embodiment.

[0072] In the embodiment of the present application, if the similarity between the real-time furnace temperature curve and the standard furnace temperature curve is less than the similarity threshold, it means that the distance between the real-time furnace temperature curve and the standard furnace temperature curve is large, and the distance between the real-time furnace temperature curve and the historical furnace temperature curve of normal products is large, which may cause the reflow soldering product to be abnormal. Therefore, it can be determined that the furnace temperature of the reflow soldering furnace is abnormal. For example Figure 3 As shown, the distance between the real-time furnace temperature curve and the standard furnace temperature curve is high at certain time nodes, that is, the similarity is low and anomalies occur.

[0073] In the embodiments of the present application, a real-time furnace temperature curve can be generated at each time node as the reflow process lasts. Similarity calculations are performed between the generated real-time furnace temperature curve and the standard furnace temperature curve for each time node to determine whether the furnace temperature at each time node is abnormal, thereby enabling real-time abnormality monitoring of the furnace temperature. Furthermore, because the Fast-DTW algorithm used can align sequences of different lengths on the time axis and calculate the similarity between them, it is well suited for this real-time similarity calculation.

[0074] In an embodiment of the present application, after determining that the temperature of the reflow oven is abnormal, the method further includes: stopping the processing process of the reflow oven; feeding back the abnormality to the temperature control system of the reflow oven; and using the temperature control system to adjust the temperature of the reflow oven.

[0075] In an embodiment of the present application, when calculating the similarity between the real-time oven temperature curve and the standard oven temperature curve, the difference between the real-time oven temperature and the standard oven temperature at each time point can be determined, and this difference can be fed back to the temperature control system of the reflow oven, so that the temperature control system of the reflow oven can perform temperature control based on this difference. For example, if the difference indicates that the real-time oven temperature at a certain time point is 5 degrees higher than the standard oven temperature, the temperature control system of the reflow oven can be instructed to reduce the oven temperature.

[0076] In one embodiment, upon determining that the reflow oven temperature is abnormal, the electronic device may also issue a warning prompt using a display device. The display device may issue a warning prompt by displaying a warning image or emitting a warning sound. In other embodiments, a voice warning may be issued through other devices (e.g., speakers, etc.). Furthermore, the warning prompt may be sent to a preset terminal device (e.g., a designated user's mobile phone) to remind the reflow oven temperature control.

[0077] The reflow oven temperature monitoring method provided in this application obtains a standard oven temperature curve for the reflow oven; collects a large number of historical oven temperature curves corresponding to normal products, and determines a similarity threshold between the historical oven temperature curves corresponding to normal products and the standard oven temperature curve by comparing the standard oven temperature curve with the historical oven temperature curves; and determines the similarity between the real-time oven temperature curve and the standard oven temperature curve. If the similarity is less than the similarity threshold, it is determined that the oven temperature of the reflow oven is abnormal. This application can determine a similarity threshold with the standard oven temperature curve based on the historical oven temperature curves of normal products, and treat oven temperature curves that exceed the similarity threshold as abnormal curves, thereby achieving accurate abnormal monitoring of the oven temperature of the reflow oven.

[0078] For example Figure 5The figure shows an example of obtaining furnace temperature data provided by an embodiment of the present application. Here, a sensor within the furnace (e.g., a temperature sensor) can be used to collect furnace temperature data (including historical furnace temperature, standard furnace temperature, real-time furnace temperature, etc.) of the reflow soldering furnace; a data collector can be used to collect the furnace temperature data; a computer outside the furnace can be used to obtain the furnace temperature data in the data collector via a data receiver, and the temperature data can be fed back to the furnace temperature control system (i.e., the temperature control system); alternatively, the computer outside the furnace can be used to send the temperature data to a cloud server for storage; a remote computer can be used to obtain the temperature data from the cloud server, and the temperature data can be fed back to the furnace temperature control system.

[0079] For example Figure 6 The figure shows a flow chart of a reflow oven temperature monitoring method provided by another embodiment of the present application. The method specifically includes: setting a standard temperature for each zone of the reflow oven; obtaining real-time data from each temperature zone of the reflow oven; comparing the real-time data with the standard temperature difference; determining whether the difference value is within a difference threshold range, wherein the difference threshold range can be obtained by comparing the standard temperature with a large number of historical temperatures; if the difference value is within the difference threshold range, continuing production; or, if the difference value is not within the difference threshold range, stopping the feeding of new products to be processed into the reflow oven and feeding back the temperature to the temperature control system; and after the temperature is modified, resuming the feeding of products and continuing production.

[0080] For example Figure 7 The figure shows a flow chart of a furnace temperature data comparison method according to an embodiment of the present application. Specifically, the method includes: generating standards and related parameters (e.g., standard furnace temperatures at each time point) based on normal product production data and temperature zone standards; storing data in the cloud / server; establishing a standard database; reading the standard database; and generating a trend comparison chart of the standard furnace temperature curve and the real-time temperature curve.

[0081] Figure 8 This is a structural diagram of a reflow oven temperature monitoring device provided in an embodiment of the present application.

[0082] In some embodiments, the reflow oven temperature monitoring device 40 may include a plurality of functional modules composed of computer program segments. The computer program of each program segment in the reflow oven temperature monitoring device 40 may be stored in a memory of an electronic device and executed by at least one processor to perform (see Figure 2 Description) Reflow oven temperature monitoring function.

[0083] In this embodiment, the reflow oven temperature monitoring device 40 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: an acquisition module 401 and a determination module 402. The module referred to in this application refers to a series of computer program segments that can be executed by at least one processor and can perform fixed functions, which are stored in a memory. In this embodiment, the functional implementation of each module in the reflow oven temperature monitoring device 40 can be found in the above definition of the reflow oven temperature monitoring method, and will not be repeated here.

[0084] The acquisition module 401 is used to acquire a standard temperature curve of a reflow oven.

[0085] The acquisition module 401 is further configured to acquire a plurality of historical furnace temperature curves of the reflow oven, wherein the historical furnace temperature curves include furnace temperature curves when normal products are processed using the reflow oven.

[0086] The determination module 402 is configured to determine a similarity threshold between the standard furnace temperature curve and the plurality of historical furnace temperature curves.

[0087] The determining module 402 is further configured to determine the similarity between the real-time furnace temperature curve and the standard furnace temperature curve. If the similarity is less than the similarity threshold, it is determined that the furnace temperature of the reflow oven is abnormal.

[0088] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the methods in the above-mentioned embodiments of the present application.

[0089] The computer-readable storage medium may be an internal memory of the electronic device described in the above embodiment, such as a hard disk or memory of the electronic device. The computer-readable storage medium may also be an external storage device of the electronic device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash memory card, etc. equipped on the electronic device.

[0090] In some embodiments, the computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, applications required for at least one function, etc.; the data storage area may store data created according to the use of the electronic device, etc.

[0091] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0092] Those skilled 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 beyond the scope of this application.

[0093] In the embodiments provided in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0095] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A reflow oven temperature monitoring method, characterized in that: The method comprises: Get the standard temperature curve of the reflow oven; Acquire multiple historical furnace temperature curves of the reflow oven, wherein the historical furnace temperature curves include furnace temperature curves when a normal product is processed using the reflow oven; Determining a similarity threshold between the standard furnace temperature curve and the plurality of historical furnace temperature curves; The similarity between the real-time furnace temperature curve and the standard furnace temperature curve is determined, and if the similarity is less than the similarity threshold, it is determined that the furnace temperature of the reflow oven is abnormal.

2. The reflow oven temperature monitoring method according to claim 1, wherein: The method of obtaining the standard temperature curve of the reflow oven comprises: The standard furnace temperature curve is generated according to the processing time and standard furnace temperature of each temperature zone in the reflow oven.

3. The reflow oven temperature monitoring method according to claim 2, wherein: The temperature zones of the reflow oven include a preheating zone, a constant temperature zone, a reflow zone, and a cooling zone; The standard furnace temperature includes a first temperature when entering each temperature zone and a second temperature when leaving each temperature zone.

4. The reflow oven temperature monitoring method according to claim 1, wherein: Determining the similarity threshold between the standard furnace temperature curve and the plurality of historical furnace temperature curves includes: Determining the similarity between the standard furnace temperature curve and each of the historical furnace temperature curves based on a preset similarity calculation method; The similarity threshold is determined according to the minimum similarity value.

5. The reflow oven temperature monitoring method according to claim 4, wherein: The similarity calculation method includes a fast dynamic time warping (Fast-DTW) algorithm.

6. The reflow oven temperature monitoring method according to claim 4, wherein: Determining the similarity between the standard furnace temperature curve and each of the historical furnace temperature curves includes: The standard furnace temperature curve and each of the historical furnace temperature curves are respectively subjected to coarse-graining, projection, and fine-graining processing to obtain similarities between the standard furnace temperature curve and each of the historical furnace temperature curves.

7. The reflow oven temperature monitoring method according to claim 1, wherein: Determining the similarity between the real-time furnace temperature curve and the standard furnace temperature curve includes: The standard furnace temperature curve and the real-time furnace temperature curve are subjected to coarse-graining, projection and fine-graining processing to obtain similarity between the standard furnace temperature curve and the real-time furnace temperature curve.

8. The reflow oven temperature monitoring method according to claim 1, wherein: After determining that the temperature of the reflow oven is abnormal, the method further includes: Stopping the processing process of the reflow oven; Feedback the abnormality to the temperature control system of the reflow oven; The temperature control system is used to adjust the temperature of the reflow oven.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, and the processor is configured to implement the reflow oven temperature monitoring method according to any one of claims 1 to 8 when executing a computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the reflow oven temperature monitoring method according to any one of claims 1 to 8 is implemented.

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