Method and apparatus for quality control in production line, and storage medium

Abstract

The invention relates to a method and device for quality control in a production line and a storage medium. The method comprises the steps that a data set of process parameters associated with the production line in a first time period is obtained; determining one or more thresholds for the process parameter based at least on the dataset of the process parameter over a first time period; in response to at least one of the following conditions, sending a first prompt to prompt that an abnormal condition exists: determining that one or more pieces of data of the process parameters in a second time period after the first time period exceed a first upper limit threshold in the one or more threshold values; or determining that one or more data of the process parameter is below a first lower threshold of the one or more thresholds within a second time period after the first time period.

Description

Technical Field

[0001] The present disclosure relates to a method and apparatus and a storage medium for quality control in a production line. Background Art

[0002] Quality control is an important task for enterprises, especially manufacturing industries. Quality control can include activities taken to ensure that products meet quality requirements, and its goal is to ensure that products meet the requirements. Since each process on a production line (e.g., an automobile production line) affects the quality of the product (e.g., an automobile), it is necessary to effectively control the quality of each process on the production line. Summary of the invention

[0003] An object of the present disclosure is to provide an improved method and apparatus and storage medium for quality control in a production line.

[0004] Some embodiments of the present disclosure provide a computer-implemented method for quality control in a production line, the method comprising: obtaining a data set of process parameters associated with the production line within a first time period; determining one or more threshold values ​​for the process parameters based at least on the data set of the process parameters within the first time period; issuing a first reminder to indicate the presence of an abnormal situation in response to at least one of the following: determining that one or more data of the process parameters within a second time period after the first time period exceeds a first upper threshold value among the one or more threshold values; or determining that one or more data of the process parameters within the second time period after the first time period is lower than a first lower threshold value among the one or more threshold values.

[0005] Some embodiments of the present disclosure provide an apparatus for quality control in a production line, the apparatus comprising: components for executing steps of any method described in the embodiments of the present disclosure.

[0006] Some embodiments of the present disclosure provide a device for quality control in a production line, the device comprising: at least one processor; and at least one memory, the at least one memory storing instructions, which, when executed by the at least one processor, enable the at least one processor to execute any method described in the embodiments of the present disclosure.

[0007] Some embodiments of the present disclosure provide a non-transitory computer-readable storage medium storing instructions, which, when executed by a processor, enable execution of any method described in the embodiments of the present disclosure.

[0008] Some embodiments of the present disclosure provide a computer program product including program instructions, which, when executed by one or more processors of a computer, causes the computer to perform any method described according to the embodiments of the present disclosure.

[0009] Other features and advantages of the present disclosure will become apparent from the following description with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure without limitation. In the various figures, like reference numerals are used to represent like items.

[0011] Figure 1 is a block diagram of an exemplary apparatus for quality control in a production line according to some embodiments of the present disclosure.

[0012] Figure 2 is a flow chart illustrating an exemplary method for quality control in a production line according to some embodiments of the present disclosure.

[0013] Figure 3 An exemplary user interface for quality control in a production line according to some embodiments of the present disclosure is shown.

[0014] Figure 4 A general hardware environment in which the present disclosure may be applied according to some embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0015] In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the described exemplary embodiments. However, it is clear to those skilled in the art that the described embodiments may be practiced without some or all of these specific details. In the described exemplary embodiments, well-known structures or processing steps are not described in detail in order to avoid unnecessarily obscuring the concepts of the present disclosure.

[0016] The blocks in each block diagram shown below can be implemented by hardware, software, firmware or any combination thereof to implement the principles of the present disclosure. It should be understood by those skilled in the art that the blocks described in each block diagram can be combined or divided into sub-blocks to implement the principles of the present disclosure.

[0017] The steps of the method presented in this disclosure are intended to be illustrative. In certain embodiments, the method can be completed with one or more additional steps not described and / or completed without the one or more steps discussed. In addition, the order in which the steps of the method are illustrated and described is not intended to be restrictive.

[0018] As mentioned above, it is very important to effectively control the quality of each process on the production line. Taking the automobile manufacturing industry as an example, the existing methods for quality control in the production line mainly include statistical process control (SPC) and failure mode and effect analysis (FMEA). However, these methods cannot solve the sudden quality problems that occur on the production line. Therefore, an improved solution for quality control in the production line is needed.

[0019] Process parameters may refer to parameters that affect the quality of products produced on a production line. Therefore, real-time monitoring and control of process parameters can help ensure that the products reach the desired quality. However, existing methods for quality control in production lines generally monitor process parameters based on fixed thresholds, which requires operators and maintenance personnel to manually set thresholds based on experience. In addition, there are certain risks in monitoring process parameters based on fixed thresholds, because process parameters often fluctuate up and down over time, and setting a fixed threshold is difficult to meet the needs of all time periods. If the threshold is set too high, missed reports may occur; if the threshold is set too low, false reports may occur. In addition, if operators and maintenance personnel are required to adjust the threshold frequently, it is difficult to determine the accurate value for each time period based on experience on the one hand, and it may bring a very large workload and low efficiency on the other hand. According to an embodiment of the present disclosure, the threshold for process parameters can be automatically and dynamically adjusted by monitoring the process parameters on the production line in real time. Thereby, not only can sudden quality problems be effectively identified, but also the stability and quality control level of the production line can be improved.

[0020] Figure 1 is a block diagram of an exemplary apparatus 100 for quality control in a production line according to some embodiments of the present disclosure. Figure 1 As shown, the apparatus 100 may include a process parameter data acquisition component 110, a threshold determination component 120, and a reminder issuing component 130. Figure 1 The above three units are illustrated, but this is only a schematic structure of the device for quality control in the production line. The device 100 for quality control in the production line may also include other possible units, or may include a combination unit of any of the above three units.

[0021] The process parameter data acquisition component 110 may be configured to obtain a data set of process parameters associated with the production line within a first time period. According to some embodiments, the process parameters may include at least one of the following: oil gun refueling data, programmable logic controller PLC call response time, error rate of electronic equipment on the production line, etc. It should be understood that the examples of process parameters are not limited thereto, but may include any parameters in the production line related to product quality. For example, oil gun refueling data is an important indicator in the manufacturing process of fuel vehicles, and it may include parameters related to refueling such as refueling amount and refueling time. Before the vehicle production is completed, it is necessary to monitor the oil gun refueling data to ensure that the vehicle's oil volume is accurate and stable. If the oil gun refueling data is abnormal (for example, too much or too little refueling), it may indicate that there is a problem in the production process, and timely early warning and processing are required. For another example, many electronic devices on the production line (for example, robotic arms, automatic guided vehicles (Automated Guided Vehicle, AGV), etc.) may have programmable logic controllers PLC, which are used to control the operation of electronic devices. During the operation of electronic equipment, the PLC may need to interact with the server to receive commands or request return results, etc. Therefore, when calling the PLC, it may take a certain amount of time (for example, several milliseconds or tens of milliseconds) to receive a response. If the PLC call response time is too long, it may cause delays or stagnation of the production line, thereby affecting production efficiency and product quality. Therefore, the PLC call response time is one of the important process parameters in the production line. For example, the error rate of electronic equipment on the production line is also an important indicator that can be used to evaluate product quality and production efficiency. On the production line, various electronic equipment may have errors during operation. The error rate within a certain range is tolerable, but an excessively high error rate may mean that there is an abnormality in the operation. The error rate can be stored locally on the electronic device or on the server (for example, in a log file). By monitoring the error rate, abnormal conditions can be discovered in a timely manner, and corresponding adjustments and repairs can be made.

[0022] According to some embodiments, the data of the process parameters may be recorded locally or on a server, for example, in a log file, by electronic equipment on a production line (i.e., equipment involved in the production of the corresponding product, such as a stamping machine, an assembly machine, etc.). According to some embodiments, the data of the process parameters may be obtained by sensor devices on a production line, which may not directly participate in the production of the corresponding product, but are specifically used to monitor the process parameters, for example, they may include but are not limited to sensor devices for monitoring force, temperature, light, sound or chemical composition, etc. Similarly, the sensor device may record the data of the relevant process parameters locally or on a server communicating with it.

[0023] The first time period may have any suitable length of time, for example, one hour, one day, one week, one month, etc. That is, data of process parameters within a time period of any suitable length may be collected, and the data may be collected at any suitable interval (for example, every millisecond, every second, every minute, etc.). Due to the production rules and business characteristics, the process parameters on the production line may usually have a certain periodicity, for example, it may be a cycle of one day or a cycle of one week, etc. After collecting a certain amount of data, the cycle of the process parameters and the change rules of the process parameters within a cycle may be analyzed. According to some embodiments, the process parameter data acquisition component 110 may obtain data in the form of a continuous data stream.

[0024] The threshold determination component 120 may be configured to determine one or more thresholds for the process parameters based at least on a data set of the process parameters within a first time period (e.g., a data set obtained by the process parameter data acquisition component 110). The one or more thresholds may include only (one or more) upper thresholds, only (one or more) lower thresholds, or both (one or more) upper thresholds and (one or more) lower thresholds, which may depend on the type of process parameters or be configured by the user. In the case where there are multiple upper thresholds (or lower thresholds), different operations (e.g., issuing different reminders) may be performed based on exceeding different upper thresholds, which will be further described later.

[0025] According to some embodiments, the threshold determination component 120 may determine one or more thresholds for process parameters according to a dynamic baseline algorithm (also referred to as a dynamic threshold algorithm). It should be understood that the dynamic baseline algorithm may include an algorithm for analyzing and predicting the threshold of time series data based on historical data and for real-time monitoring by continuously updating the threshold. It may include an algorithm based on statistics (e.g., Autoregressive Integrated Moving Average (ARIMA), Seasonal Auto Regressive Integrated Moving Average (SARIMA), etc.), or an algorithm based on machine learning (e.g., an algorithm based on clustering, an algorithm based on support vector machines, etc.) or a combination thereof. Using these algorithms to process a data set may include data preprocessing operations, such as filtering out irrelevant data, outlier data, etc., normalizing the data, filling in missing data (e.g., interpolation), etc. For algorithms based on machine learning, operations such as model training and verification may also be included. Those skilled in the relevant art can easily implement these operations based on the teachings of the present disclosure, and will not be repeated here. The determination of the threshold value can be adjusted based on actual needs to achieve a compromise between the false alarm rate and the false negative rate. For example, the threshold value can be adjusted by setting different confidence levels (e.g., 95%, 90%, or 85%, etc.), and then using the confidence interval with the confidence level to generate the threshold interval. That is, the upper threshold value can be the upper confidence limit and / or the lower threshold value can be the lower confidence limit.

[0026] The threshold value can be continuously updated during the operation of the production line, thereby continuously improving the accuracy of quality control. According to some embodiments, the threshold value determination component 120 determines the threshold value based on at least the threshold value of the process parameter before the first time period (for example, the threshold value is set by the relevant personnel based on experience, the default value, or is determined based on the data of the process parameter before the first time period). At least one of the one or more threshold values ​​determined by the threshold determination component 120 may be different from at least one previous threshold value for the process parameter (for example, one upper threshold value is different, or two upper threshold values ​​are different, or one upper threshold value and one lower threshold value are different, etc.). It should be understood that the threshold value determined after obtaining new data may also be the same as the original threshold value. The threshold value determined by the threshold determination component 120 may be a time-based array. For example, the upper threshold value determined for the error rate of electronic equipment on the production line may be [10%, 15%, 10%, 8%], which corresponds to the time period of the day [0 to 6 o'clock, 6 to 12 o'clock, 12 to 18 o'clock, 18 to 24 o'clock]. The threshold determination component 120 may automatically update the threshold value regularly, or may update the threshold value in response to receiving an instruction.

[0027] The reminder issuing component 130 may be configured to issue a first reminder to indicate the existence of an abnormal situation in response to at least one of the following: determining that one or more data of the process parameters in the second time period after the first time period (for example, obtained by the process parameter data obtaining component 110) exceeds the first upper threshold value of the determined one or more threshold values; or determining that one or more data of the process parameters in the second time period after the first time period is lower than the first lower threshold value of the determined one or more threshold values. For some process parameters (for example, PLC call response time), it may only be necessary to monitor whether it exceeds the upper limit, and the threshold determination component 120 may only determine the first upper threshold value. In this case, the reminder issuing component 130 may issue a first reminder when it is determined that one or more data of the process parameters in the second time period after the first time period exceeds the determined first upper threshold value. For other process parameters, it may only be necessary to monitor whether it is lower than the lower limit (for example, the laser energy density in laser welding can only be stable deep penetration welding when it exceeds a specific threshold value), and the threshold determination component 120 may only determine the first lower threshold value. In this case, the reminder issuing component 130 may issue a first reminder when it is determined that one or more data of the process parameters in the second time period after the first time period is lower than the determined first lower threshold value. For other process parameters (e.g., oil gun refueling data), it may be necessary to monitor whether it exceeds the upper limit and whether it is lower than the lower limit. Therefore, the threshold determination component 120 can determine both the first upper limit threshold and the first lower limit threshold. In this case, the reminder issuing component 130 can issue a first reminder when it is determined that one or more data of the process parameters in the second time period after the first time period exceeds the determined first upper limit threshold or is lower than the determined first lower limit threshold. The second time period can be a time of any length after the first time period. In order to reduce the false alarm rate, the reminder issuing component 130 can issue a first reminder when it is determined that the data of the process parameters in the second time period that meets certain conditions exceeds the determined first upper limit threshold or is lower than the determined first lower limit threshold, wherein the certain conditions may include one or more of the following: the number of data exceeds the quantity threshold (e.g., the number of data exceeding the upper limit threshold or lower than the lower limit threshold exceeds 50), the duration corresponding to the data exceeds the time threshold (e.g., the data within 10 consecutive minutes exceeds the upper limit threshold or is lower than the lower limit threshold), etc. The quantity threshold and the time threshold can be set by the user or automatically configured by the reminder issuing component 130 (e.g., based on learning user input).

[0028] According to some embodiments, the reminder issuing component 130 issuing the first reminder may include at least one of the following: the reminder issuing component 130 outputs a prompt to the user, or sends a message to an electronic device associated with the process parameter on the production line. The prompt may be provided to the user, for example, through an output interface (not shown) of the device 100, other devices on the production line, and / or a user's personal device to indicate the presence of an abnormal situation. The prompt may include, but is not limited to, one or more of the following: a visual prompt (e.g., text, graphics, etc.), a sound prompt (e.g., an alarm sound or voice, etc.), a tactile prompt (e.g., vibration), etc. Thus, the relevant operation or maintenance personnel can be reminded in time to take measures to adjust and improve the stability and quality control level of the production line. Alternatively or additionally, the reminder issuing component 130 may send a message (e.g., an instruction or request) to an electronic device associated with the process parameter on the production line to automatically adjust without manual intervention, thereby quickly responding to quality problems on the production line and improving the efficiency of quality control. For example, when the PLC call response time on the production line exceeds a threshold (possibly due to excessive load on the server interacting with the PLC), the reminder issuing component 130 can issue instructions to the server interacting with the PLC to terminate or suspend one or more lower priority tasks, etc.

[0029] According to some embodiments, the reminder issuing component 130 may also perform abnormal association analysis. The reminder issuing component 130 may determine that the data of one or more other process parameters in the second time period are associated with the abnormal situation, so as to output information related to the one or more other process parameters when issuing the first reminder. The data of the monitored process parameters in the second time period may be subjected to association analysis with the data of one or more other process parameters to determine whether there is an association between the two (for example, whether there is a fluctuation correlation, etc.). The association analysis may be based on one or more of the following algorithms: nearest neighbor algorithm, FP tree algorithm, dynamic time warping (Dynamic Time Warping, DTW) algorithm, etc. For example, the reminder issuing component 130 may determine that the error rate of the first device on the production line from 3:00 to 3:10 pm exceeds its upper threshold, and determine that the memory occupancy rate of the second device upstream of the first device on the production line from 2:55 to 3:05 pm exceeds its upper threshold. Then, based on the result of the association analysis, it may be determined that the abnormal situation that the error rate of the first device exceeds the threshold is related to the memory occupancy rate of the second device exceeding the threshold. The reminder issuing component 130 may cause the issued reminder to include information related to the memory occupancy rate of the second device. For example, the data curve of the memory usage of the second device can be displayed, and text or voice prompts such as "Please check the memory of the second device" can be displayed. Through abnormal correlation analysis, process parameters related to the current abnormal situation can be automatically identified, and recommendations and displays can be made. This helps to quickly identify the cause of the abnormal situation and reduce the cost and time of problem analysis and processing.

[0030] According to some embodiments, the reminder issuing component 130 may issue a second reminder different from the first reminder in response to at least one of the following: determining that one or more data of the process parameters in the third time period after the first time period exceeds the second upper threshold value of the one or more threshold values; or determining that one or more data of the process parameters in the third time period after the first time period is lower than the second lower threshold value of the one or more threshold values. That is, multiple thresholds determined by the threshold determination component 120 may be monitored, and different reminders may be issued when different threshold values ​​(i.e., the severity of the abnormal situation is different) are exceeded. For example, when the first upper threshold value is exceeded, the user may be reminded to check the corresponding equipment on the production line without stopping the machine; and when the second upper threshold value is exceeded, the user may be reminded to stop the machine immediately or even directly output an instruction to stop the current task to the relevant equipment. The second reminder may have the same or different category as the first reminder. For example, the first reminder may be a visual reminder, and the second reminder may be a sound reminder. For another example, the first reminder and the second reminder may both be sound reminders, but the volume of the second reminder is louder. Similar to the first reminder, in order to reduce the false alarm rate, the reminder issuing component 130 may issue a second reminder when it is determined that the data of the process parameters in the third time period that meet certain conditions exceeds the determined second upper threshold or is lower than the determined second lower threshold. This article will not repeat it here.

[0031] Figure 2 is a flow chart illustrating an exemplary method 200 for quality control in a production line according to some embodiments of the present disclosure. Figure 2 This is only a schematic flow chart of a method for quality control in a production line. The method 200 for quality control in a production line may also include other possible steps, or may include Figure 2 A combination of any of the steps shown.

[0032] The method 200 starts from step S210, which can be Figure 1 The process parameter data acquisition component 110 is executed. At step S210, a data set of process parameters associated with the production line in a first time period is obtained.

[0033] The method 200 proceeds to step S220, which may be performed by Figure 1 The threshold determination component 120 is executed. At step S220, one or more thresholds for the process parameters are determined based on at least a data set of the process parameters within a first time period.

[0034] Depending on the type of user configuration or process parameters, method 200 may proceed to at least one of step S230 or step S240, which may be performed by Figure 1The reminder issuing component 130 is executed. At step S230, it is determined that one or more data of the process parameters in the second time period after the first time period exceeds the first upper threshold value of the one or more threshold values. At step S240, it is determined that one or more data of the process parameters in the second time period after the first time period is lower than the first lower threshold value of the one or more threshold values.

[0035] The method 200 proceeds to step S250, which may be performed by Figure 1 The reminder issuing component 130 is executed. At step S250, a first reminder is issued to indicate that an abnormal situation exists.

[0036] Figure 3 An exemplary user interface 300 for quality control in a production line according to some embodiments of the present disclosure is shown. Figure 3 Further description. It should be understood that although Figure 3 A curve chart of the process parameter data displayed in the graphical user interface is shown, but the present disclosure is not limited to this. The process parameter data and the determined threshold value can be displayed in any suitable manner (for example, a bar chart, line chart, or other graphic or data list).

[0037] like Figure 3 As shown, the horizontal axis represents the time corresponding to the data of the process parameter, and the vertical axis represents the value of the process parameter. Figure 3 A data curve 320 of a process parameter (e.g., memory utilization of an electronic device on a production line) within 24 hours on July 18, 2023, and an upper threshold curve 330 and a lower threshold curve 310 for the process parameter are shown. The upper threshold and the lower threshold can be determined based on data obtained before July 18, 2023 (e.g., data from June 17 to July 17, 2023). It can be seen that neither the upper threshold nor the lower threshold is a fixed value, but fluctuates up and down during the day. At a certain time point between 12:00 and 16:00 on July 18, the data of the monitored process parameter exceeds the upper threshold, and between 16:00 and 20:00, the data of the monitored process parameter falls back to between the upper threshold and the lower threshold. Accordingly, a reminder can be issued in any suitable manner to indicate the presence of an abnormal situation. For example, the portion of the curve 320 that exceeds the curve 330 can be emphasized (e.g., filled with color or shadow, etc.), or a text prompt (e.g., "exceeding the upper limit") can be displayed at an appropriate location in the user interface, and so on.

[0038] As described above, the method for quality control in a production line according to the present disclosure can monitor the process parameters on the production line in real time, identify sudden quality problems, and facilitate timely measures to make adjustments, which can greatly improve the stability and quality control level of the production line. In addition, by continuously updating the thresholds of the process parameters, adaptive adjustments can be made to the changes on the production line to make quality control more accurate. By quickly responding to quality problems on the production line, production efficiency, stability and quality control levels can be improved.

[0039] Hardware Implementation

[0040] Figure 4 A general hardware environment 700 is shown in which the present disclosure may be applied according to an exemplary embodiment of the present disclosure.

[0041] refer to Figure 4 , a computing device 700 will now be described as an example of a hardware device applicable to various aspects of the present disclosure. The computing device 700 may be any machine configured to perform processing and / or computing, and may be, but is not limited to, a workstation, a server, a desktop computer, a laptop computer, a tablet computer, a personal digital assistant, a smart phone, a portable camera, or any combination thereof. The above-mentioned apparatus 100 may be implemented in whole or at least in part by the computing device 700 or a similar device or system.

[0042] The computing device 700 may include an element that can be connected to or communicate with the bus 702 via one or more interfaces. For example, the computing device 700 may include a bus 702, one or more processors 704, one or more input devices 706, and one or more output devices 708. The one or more processors 704 may be any type of processor, and may include, but are not limited to, one or more general-purpose processors and / or one or more special-purpose processors (such as special-purpose processing chips). The input device 706 may be any type of device that can input information to the computing device, and may include, but are not limited to, a mouse, a keyboard, a touch screen, a microphone, and / or a remote controller. The output device 708 may be any type of device that can present information, and may include, but are not limited to, a display, a speaker, a video / audio output terminal, and / or a printer. The computing device 700 may also include or be connected to a non-transient storage device 710, which may be any storage device that is non-transient and can implement a data repository, and may include but is not limited to a disk drive, an optical storage device, a solid-state storage device, a floppy disk, a flexible disk, a hard disk, a magnetic tape or any other magnetic medium, a compact disk or any other optical medium, a ROM (read-only memory), a RAM (random access memory), a cache memory and / or any other memory chip or cartridge, and / or any other medium from which a computer can read data, instructions and / or code. The non-transient storage device 710 may be detachable from the interface. The non-transient storage device 710 may have data / instructions / code for implementing the above methods and steps. The computing device 700 may also include a communication device 712. The communication device 712 may be any type of device or system capable of communicating with an external device and / or with a network, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication equipment and / or a communication device such as Bluetooth. TM Chipsets for devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication facilities, etc.

[0043] The bus 702 may include, but is not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an Enhanced ISA (EISA) bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0044] The computing device 700 may also include a working memory 714 , which may be any type of working memory that can store instructions and / or data useful for the operation of the processor 704 , and may include, but is not limited to, random access memory and / or read-only memory devices.

[0045] The software elements may be located in the working memory 714, including but not limited to an operating system 716, one or more applications 718, drivers and / or other data and codes. Instructions for executing the above methods and steps may be included in one or more applications 718, and the components of the above apparatus 100 may be implemented by the processor 704 reading and executing the instructions of one or more applications 718. More specifically, the process parameter data acquisition component 110 may be implemented, for example, by the processor 704 when executing the application 718 having instructions to execute step S210. The threshold determination component 120 may be implemented, for example, by the processor 704 when executing the application 718 having instructions to execute step S220. The reminder issuing component 130 may be implemented, for example, by the processor 704 when executing the application 718 having instructions to execute step S250. The executable code or source code of the instructions of the software elements may be stored in a non-transitory computer-readable storage medium (such as the above-mentioned (one or more) storage devices 710) and may be read into the working memory 714 when it is possible to compile and / or install. The executable code or source code of the instructions of the software element may also be downloaded from a remote location.

[0046] From the above embodiments, it will be clear to those skilled in the art that the present disclosure can be implemented by software and necessary hardware, or can be implemented by hardware, firmware, etc. Based on this understanding, the embodiments of the present disclosure can be partially implemented in the form of software. Computer software can be stored in a computer-readable storage medium, such as a floppy disk, a hard disk, an optical disk, or a flash memory. Computer software includes a series of instructions that enable a computer (such as a personal computer, a service station, or a network terminal) to run a method according to each embodiment of the present disclosure or a part thereof.

[0047] The disclosure having thus been described, it will be apparent that the same may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the disclosure, and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims.

Claims

1. A computer-implemented method for quality control in a production line, comprising: obtaining a data set of process parameters associated with the production line during a first time period; determining one or more threshold values ​​for the process parameter based at least on a data set of the process parameter over a first time period; In response to at least one of the following, a first reminder is issued to indicate that an abnormal situation exists: determining that one or more data of the process parameter in a second time period after the first time period exceeds a first upper threshold value among the one or more threshold values; or It is determined that one or more data of the process parameter in a second time period after the first time period is lower than a first lower threshold value among the one or more threshold values.

2. The method of claim 1 , wherein the one or more threshold values ​​for the process parameter are determined by a dynamic baseline algorithm, and wherein at least one of the one or more threshold values ​​is different from at least one previous threshold value for the process parameter.

3. The method according to claim 1, further comprising: In response to at least one of the following, issuing a second reminder different from the first reminder: determining that one or more data of the process parameter in a third time period after the first time period exceeds a second upper threshold value among the one or more threshold values; or It is determined that one or more data of the process parameter in a third time period after the first time period is lower than a second lower threshold value among the one or more threshold values.

4. The method according to any one of claims 1 to 3, wherein the data of the process parameters are obtained by a sensor device on the production line.

5. The method according to any one of claims 1-3, wherein the process parameters include at least one of the following: oil gun refueling data, programmable logic controller (PLC) call response time, and error rate of electronic equipment on the production line.

6. The method according to any one of claims 1 to 3, further comprising: It is determined that data of one or more other process parameters within a second time period are associated with the abnormal situation, wherein issuing the first reminder further comprises outputting information related to the one or more other process parameters. 7 . The method according to claim 1 , wherein issuing a first reminder comprises at least one of the following: outputting a prompt to a user or sending a message to an electronic device associated with the process parameter on a production line.

8. A device for quality control in a production line, characterized in that: include: Means for performing the steps of the method as claimed in any one of claims 1-7.

9. A device for quality control in a production line, characterized in that: include: at least one processor; as well as At least one memory, wherein the at least one memory stores instructions, and when the instructions are executed by the at least one processor, the at least one processor executes the method according to any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, characterized in that: Instructions are stored which, when executed by a processor, cause the method according to any one of claims 1 to 7 to be performed.

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