Method and Equipment for Detecting the Processing Quality of Rearview Mirror Brackets

Abstract

This application is applicable to the technical field of automotive parts quality inspection, and particularly relates to a method and equipment for inspecting the processing quality of a rearview mirror bracket. The method includes: obtaining the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket to provide a basis for subsequent analysis; determining multiple segmented processing data during the processing of the rearview mirror bracket according to the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket to understand the dynamic changes during the processing of the rearview mirror bracket; determining the processing quality change curve during the processing of the rearview mirror bracket based on the multiple segmented processing data of the rearview mirror bracket to determine the process quality change situation during the processing of the rearview mirror bracket; obtaining the processing quality inspection result of the rearview mirror bracket according to the processing quality change curve during the processing of the rearview mirror bracket to improve the efficiency and accuracy of the inspection during the processing of the rearview mirror bracket.

Description

Technical Field

[0001] This application belongs to the technical field of automotive parts quality inspection, and particularly relates to a method and device for inspecting the processing quality of a rearview mirror bracket. Background Art

[0002] The rearview mirror bracket is an important component for fixing and supporting the automotive rearview mirror. It ensures that the rearview mirror remains stable during vehicle driving, provides a clear rear view, and thus guarantees driving safety.

[0003] The traditional method for inspecting the processing quality of rearview mirror brackets mainly relies on regular manual sampling inspection. The data usually needs to be manually recorded and processed. The monitored data can only reflect the situation at a certain point in time, is prone to errors, and cannot capture the dynamic changes during the processing. The lack of efficient automated inspection tools results in low inspection efficiency and low accuracy. Summary of the Invention

[0004] The embodiments of this application provide a method and device for inspecting the processing quality of a rearview mirror bracket, which can solve the problems of low inspection efficiency and low accuracy in the process of inspecting the processing quality of rearview mirror brackets due to the lack of efficient automated inspection tools.

[0005] In the first aspect, the embodiments of this application provide a method for inspecting the processing quality of a rearview mirror bracket, including:

[0006] Obtaining the process parameter time-series data and real-time image data during the processing of the rearview mirror bracket;

[0007] Determining multiple segmented processing data during the processing of the rearview mirror bracket according to the process parameter time-series data and the real-time image data during the processing of the rearview mirror bracket; wherein, the segmented processing data is used to reflect the processing conditions of different process steps during the processing of the rearview mirror bracket;

[0008] Based on the multiple segmented processing data of the rearview mirror bracket, determining the processing quality change curve during the processing of the rearview mirror bracket; wherein, the processing quality change curve is used to reflect the process quality change situation during the processing of the rearview mirror bracket;

[0009] Obtaining the processing quality inspection result of the rearview mirror bracket according to the processing quality change curve during the processing of the rearview mirror bracket.

[0010] The above technical solutions in the embodiments of this application have at least the following technical effects:

[0011] The method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application obtains the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket, providing a basis for subsequent analysis. According to the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket, multiple segmented processing data during the processing of the rearview mirror bracket are determined to understand the dynamic changes during the processing of the rearview mirror bracket. Based on the multiple segmented processing data of the rearview mirror bracket, a processing quality change curve during the processing of the rearview mirror bracket is determined to determine the process quality change situation during the processing of the rearview mirror bracket. According to the processing quality change curve during the processing of the rearview mirror bracket, the processing quality detection result of the rearview mirror bracket is obtained, improving the efficiency and accuracy of the detection during the processing of the rearview mirror bracket.

[0012] In a second aspect, an embodiment of the present application provides a system for detecting the processing quality of a rearview mirror bracket, including:

[0013] An acquisition unit for acquiring the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket;

[0014] A segmentation unit for determining multiple segmented processing data during the processing of the rearview mirror bracket according to the time-series data of process parameters and the real-time image data during the processing of the rearview mirror bracket; wherein, the segmented processing data is used to reflect the processing conditions of different process steps during the processing of the rearview mirror bracket;

[0015] An analysis unit for determining a processing quality change curve during the processing of the rearview mirror bracket based on the multiple segmented processing data of the rearview mirror bracket; wherein, the processing quality change curve is used to reflect the process quality change situation during the processing of the rearview mirror bracket;

[0016] A result unit for obtaining the processing quality detection result of the rearview mirror bracket according to the processing quality change curve during the processing of the rearview mirror bracket.

[0017] In a third aspect, an embodiment of the present application provides a device for detecting the processing quality of a rearview mirror bracket, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method described in any one of the above aspects is implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on a device for detecting the processing quality of a rearview mirror bracket, the device for detecting the processing quality of a rearview mirror bracket is enabled to execute the method described in any one of the above aspects.

[0019] It can be understood that the beneficial effects of the above second aspect to the fourth aspect can be referred to the relevant descriptions in the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0021] Figure 1 is a schematic flowchart of a method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application;

[0022] Figure 2 is a schematic execution diagram of a method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application;

[0023] Figure 3 is a schematic execution flowchart of step S200 of a method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application;

[0024] Figure 4 is a schematic execution flowchart of step S300 of a method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application;

[0025] Figure 5 is a schematic structural diagram of a system for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application;

[0026] Figure 6 is a schematic structural diagram of a device for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should clearly understand that the present application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0028] It should be understood that when used in the specification of the present application and the appended claims, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0029] It should also be understood that the term "and / or" as used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations.

[0030] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "once" or "in response to determining" or "in response to detecting" depending on the context. Similarly, the phrases "if determined" or "if the described condition or event is detected" can be interpreted as meaning "once determined" or "in response to determining" or "once the described condition or event is detected" or "in response to detecting the described condition or event" depending on the context.

[0031] In addition, in the description of the specification and appended claims of this application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0032] Reference to "an embodiment" or "some embodiments" etc. described in the specification of this application means that a specific feature, structure or characteristic described in connection with the embodiment is included in one or more embodiments of this application. Thus, statements such as "in an embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0033] The processing quality detection method of traditional rearview mirror brackets mainly relies on regular manual sampling inspection. Data usually needs to be manually recorded and processed. The monitored data can only reflect the situation at a certain point in time, is prone to errors, and cannot capture the dynamic changes during the processing. There is a lack of efficient automated detection tools, resulting in not only low detection efficiency but also low accuracy.

[0034] To solve the above problems, an embodiment of the present application provides a method and device for detecting the processing quality of a rearview mirror bracket. In this method, by obtaining the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket, it provides a basis for subsequent analysis. According to the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket, multiple segmented processing data during the processing of the rearview mirror bracket are determined to understand the dynamic changes during the processing of the rearview mirror bracket. Based on the multiple segmented processing data of the rearview mirror bracket, a processing quality change curve during the processing of the rearview mirror bracket is determined to determine the process quality change during the processing of the rearview mirror bracket. According to the processing quality change curve during the processing of the rearview mirror bracket, the processing quality detection result of the rearview mirror bracket is obtained, improving the efficiency and accuracy of the detection during the processing of the rearview mirror bracket.

[0035] The method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application can be applied to a device for detecting the processing quality of a rearview mirror bracket. At this time, the device for detecting the processing quality of a rearview mirror bracket is the execution subject of the method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application. The specific type of the device for detecting the processing quality of a rearview mirror bracket is not limited in any way by an embodiment of the present application.

[0036] For example, the device for detecting the processing quality of a rearview mirror bracket can be various types of intelligent monitoring devices. The device for detecting the processing quality of a rearview mirror bracket may include, but is not limited to, a desktop computer, a smart large screen, a smart TV, a handheld device with wireless communication function, a computing device, a computer, a laptop computer, etc.

[0037] To better understand the method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application, the following provides an exemplary introduction to the specific implementation process of the method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application.

[0038] Figure 1 shows a schematic flowchart of the method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application, Figure 2 shows an execution flowchart of the method for detecting the processing quality of a rearview mirror bracket provided by an embodiment of the present application. The method for detecting the processing quality of a rearview mirror bracket includes:

[0039] S100, obtaining the time-series data of process parameters and real-time image data during the processing of the rearview mirror bracket.

[0040] It can be understood that the process parameter time-series data usually includes various key parameters that change over time during the processing, such as temperature, pressure, rotational speed, feed rate, etc. The process parameter time-series data can reflect the dynamic changes of the processing technology. The real-time image data is the image captured in real time by the image acquisition device (such as a camera, sensor, etc.) installed on the processing equipment, which records information such as the workpiece shape, surface features, and state changes during the processing. The real-time image data can be obtained by receiving the data from the data acquisition system installed on the production line. The data acquisition system can be connected to the equipment control system, PLC (programmable logic controller), image processing unit, etc. in real time to ensure the real-time and accuracy of the data. By obtaining the process parameter time-series data and the real-time image data, it can provide basic information for subsequent data analysis and quality assessment.

[0041] S200. Determine multiple segmented processing data during the processing of the rearview mirror bracket according to the process parameter time-series data and the real-time image data during the processing of the rearview mirror bracket; wherein, the segmented processing data is used to reflect the processing conditions of different process steps during the processing of the rearview mirror bracket.

[0042] It can be understood that the process parameter time-series data reflects the specific process conditions at each moment during the processing, while the real-time image data provides visual feedback on the processing state corresponding to these conditions. By comprehensively analyzing these two types of data, the start and end times of different process steps can be identified, and then the processing process can be divided into multiple consecutive stages (segments), and each stage represents a specific process step during the processing. This can be achieved through methods including time series analysis, image recognition, and pattern recognition, etc. For example, by performing dynamic analysis on the process parameter time-series data, the fluctuation or change law in different processing stages can be found, and the boundary of the segment can be further confirmed through the feature changes in the image data.

[0043] In a possible implementation, please refer to Figure 3 S200. Determine multiple segmented processing data of the rearview mirror bracket according to the process parameter time-series data and the real-time image data during the processing of the rearview mirror bracket, including:

[0044] S210. Perform time series decomposition on the process parameter time-series data during the processing of the rearview mirror bracket to obtain the processing characteristic curve during the processing of the rearview mirror bracket.

[0045] It can be understood that time series decomposition is an analysis method for time series data, aiming to reveal features such as trends, seasonal fluctuations, and periodic changes in the data. During the processing of the rearview mirror bracket, the time series data of process parameters often contains various complex change patterns, which may be caused by factors such as environmental changes, equipment state changes, and operation differences of operators. Through time series decomposition, these complex time series data can be decomposed into multiple components, including long-term trends, periodic fluctuations, and random noise, etc., thereby helping to analyze and understand the change trends of process parameters during the processing. The processing characteristic curve is a trend curve obtained after decomposing the time series data, representing the basic pattern of how process parameters change over time during the processing. Time series decomposition methods can include additive models and multiplicative models, and appropriate models are selected for decomposition according to the characteristics of the data. For example, methods such as moving average method, exponential smoothing method, or Fourier transform can be used for time series decomposition. The finally obtained processing characteristic curve can reflect the long-term change trend of process parameters during the processing, helping with subsequent processing cycle identification and quality monitoring.

[0046] S220. Determine several processing cycles during the processing of the rearview mirror bracket according to the processing characteristic curve during the processing of the rearview mirror bracket; wherein, the processing cycles are different from each other.

[0047] It can be understood that the processing cycle refers to a period of time during the processing when process conditions (such as speed, pressure, etc.) remain relatively stable. By analyzing the change rules of the processing characteristic curve, especially features such as fluctuations and mutations in the curve, different periodic changes during the processing can be identified, and the processing cycles can be divided accordingly. The division of the processing cycle depends on the feature extraction method, which can include peak detection, threshold determination, morphological analysis, etc. By modeling the periodic changes of the processing characteristic curve, the specific time range of each cycle can be determined, providing clear time nodes and data support for subsequent process analysis.

[0048] Optionally, S220. Determine several processing cycles during the processing of the rearview mirror bracket according to the processing characteristic curve during the processing of the rearview mirror bracket, including:

[0049] S221. Determine multiple process time nodes during the processing of the rearview mirror bracket according to the processing characteristic curve during the processing of the rearview mirror bracket; wherein, the process time nodes are used to reflect the process start times of different process steps during the processing of the rearview mirror bracket.

[0050] It can be understood that the determination of process time nodes is to extract representative moments from the processing characteristic curve, and these moments correspond to the start times of key process steps in the processing process. The determination of process time nodes is usually based on the analysis of characteristic data. For example, by identifying features such as peaks, valleys, and change slopes in the curve, to determine when a new process step starts. Process time nodes can be identified through signal processing techniques (such as edge detection, threshold detection, etc.). By accurately determining the process time nodes, the start time of each process step can be effectively calibrated, thus providing key time point information for subsequent analysis and quality monitoring.

[0051] S222, based on multiple process time nodes and the processing characteristic curve in the processing of the rearview mirror bracket, obtain the associated positions of each process time node on the processing characteristic curve.

[0052] It can be understood that by combining the relationship between the process time nodes and the processing characteristic curve, the specific position of each time node on the curve can be located. This association is achieved through the analysis of the process time nodes to find their exact positions on the processing characteristic curve, thereby helping to confirm the characteristics and changes of each process step in the entire processing process. Mathematical tools such as interpolation method and least squares method can be used to determine the exact positions of the process time nodes on the curve. Through this associated way, the processing characteristic curve and the process steps can be closely linked, providing more refined and accurate data support for subsequent analysis.

[0053] S223, based on the associated positions of each process time node on the processing characteristic curve, determine several processing cycles in the processing of the rearview mirror bracket.

[0054] It can be understood that based on the associated positions of the aforementioned process time nodes, by segmenting the processing characteristic curve, multiple processing cycles in the processing process can be clearly identified. The processing cycles can be divided according to the change situation of the process steps, ensuring that the start and end of each cycle are consistent with the process time nodes. By associating the specific positions of the process time nodes with the processing characteristic curve, the cycle can be segmented, providing a basis for subsequent quality assessment and optimization.

[0055] S230, based on different processing cycles in the processing of the rearview mirror bracket, divide the processing characteristic curve to determine multiple processing cycle curves in the processing of the rearview mirror bracket.

[0056] It can be understood that the processing characteristic curves in the processing process of the rearview mirror bracket (reflecting the changes of key variables such as temperature, pressure, speed, etc. in the processing process) can be divided, and multiple processing cycle curves can be obtained according to different processing cycles. The processing cycle curve refers to the processing characteristic curve corresponding to each processing cycle. The processing characteristic curve reflects the changes of process parameters in the processing process of the rearview mirror bracket in different processing cycles, and provides a detailed understanding of the process effects, fluctuations and stabilities within a single cycle.

[0057] Exemplarily, the processing cycle can be divided in various ways:

[0058] Division based on time nodes: If the time structure of the processing process is relatively fixed, the processing cycle can be divided by preset time intervals or process time nodes. For example, every certain period of time or every time a process step is completed is considered a processing cycle.

[0059] Division based on feature changes: The change points (such as mutation points, fluctuating changes) in the processing characteristic curve often indicate the start and end of a new processing cycle. Feature points can be identified through image processing or signal processing techniques (such as peak detection, zero-crossing detection, change rate calculation, etc.).

[0060] Division based on process events: The processing process of the rearview mirror bracket can include multiple different process steps (such as milling, welding, etc.). By identifying the time points or signal characteristics of these process events, different processing cycles can be divided.

[0061] S240, determine multiple segmented processing data of the rearview mirror bracket based on multiple processing cycle curves and real-time image data in the processing process of the rearview mirror bracket.

[0062] It can be understood that each processing cycle curve reflects the process change characteristics within a certain period of time in the processing process, while the real-time image data records the visual information in the processing process during the corresponding period. By combining the processing cycle curve and the real-time image data, the quality and state of each processing stage can be evaluated more precisely, thereby helping to judge potential problems in the processing process in subsequent steps. It can include aligning the image data with the time series data to enable each processing cycle curve to accurately match the corresponding image data. Techniques such as timestamp matching and interpolation can be used to ensure data synchronization, thereby constructing segmented processing data corresponding to each processing cycle.

[0063] Optionally, S240, determine multiple segmented processing data of the rearview mirror bracket based on multiple processing cycle curves and real-time image data in the processing process of the rearview mirror bracket, including:

[0064] S241. Determine the time correspondence information for each processing cycle curve and real-time image data based on multiple processing cycle curves and real-time image data during the processing of the rearview mirror bracket.

[0065] It can be understood that the time period of the processing cycle curve can be time-synchronized with the corresponding real-time image data. For example, an interpolation method based on timestamps can be adopted, or the image data can be cropped using a time window to ensure that the image data within each cycle can be correctly mapped to the corresponding process parameter changes.

[0066] S242. Determine the processing cycle image data corresponding to each processing cycle curve according to the time correspondence information of each processing cycle curve and real-time image data.

[0067] It can be understood that through the obtained time correspondence information, the corresponding image data can be assigned to each processing cycle curve. Each processing cycle curve is associated with a specific image data set, which contains all relevant images within that cycle. Each frame of the image sequence in the image data set may correspond to a specific moment in a processing cycle, and the correspondence between the time series data and the image data can be smoothed by including image timestamp matching or by interpolation methods.

[0068] S243. Obtain multiple segmented processing data of the rearview mirror bracket based on each processing cycle curve and the processing cycle image data corresponding to the processing cycle curve.

[0069] It can be understood that the complete segmented processing data can be generated by combining the curve data and the corresponding image data of each processing cycle. The segmented processing data can reflect the specific processing conditions of the rearview mirror bracket in each processing cycle, including information such as parameter changes, quality status, and surface changes visible in the image. Data fusion techniques can be used to combine the curve data and the image data to generate a multi-dimensional data set. This data set can not only reflect the change trend of the process parameters but also provide visual feedback on the process status. Based on these data, further processing quality monitoring and analysis can be carried out.

[0070] S300. Determine the processing quality change curve during the processing of the rearview mirror bracket based on the multiple segmented processing data of the rearview mirror bracket; wherein, the processing quality change curve is used to reflect the process quality change situation during the processing of the rearview mirror bracket.

[0071] It can be understood that the processing quality change curve is a graphical representation that describes the quality state during the processing process over time. It can reflect the fluctuations and change trends of the processing quality, as well as the quality differences between different processing steps. Each part of the segmented processing data can be analyzed to extract information that can reflect the quality change. The analysis methods can include techniques such as data regression and curve fitting. Through these methods, a smooth quality change curve can be obtained to help further judge potential quality problems in the processing process.

[0072] In one possible implementation, please refer to Figure 4 , S300, based on multiple segmented processing data of the rearview mirror bracket, determine the processing quality change curve during the processing of the rearview mirror bracket, including:

[0073] S310, based on multiple segmented processing data of the rearview mirror bracket, determine the standard evaluation data corresponding to each segmented processing data; wherein, the standard evaluation data is used to determine the quality of the segmented processing data.

[0074] It can be understood that the standard evaluation data is the reference data used to measure the quality of each segmented processing data. The standard evaluation data can be sourced from historical data, industry standards, or laboratory verification. The role of the standard evaluation data is to provide a quality benchmark for each segmented processing data, facilitating the subsequent analysis of its quality status. By comparing and analyzing each segmented processing data, its corresponding standard evaluation data can be determined. The standard evaluation data can include quality indicators such as the qualified rate, error range, and deviation value. By comparing the differences between these standard data and the actual data, it can be determined whether the quality in the processing process meets the expected standards.

[0075] S320, analyze each segmented processing data of the rearview mirror bracket according to the standard evaluation data corresponding to each segmented processing data, and determine the independent quality change curve corresponding to each segmented processing data.

[0076] It can be understood that the analysis of each segmented processing data involves comparing the actual data with the standard evaluation data to generate an independent quality change curve. The independent quality change curve reflects the quality change of each segment (i.e., process step) during the processing process and can help analyze the quality fluctuations in different process steps. The analysis process can include steps such as data fitting, curve smoothing, and outlier detection. Through these methods, a clear quality change trend can be extracted from complex data. By comparing the quality changes in different processing stages, the quality fluctuations and problems existing in the processing process can be effectively identified.

[0077] Optionally, in S320, analyze each piece of machining data of the rearview mirror bracket according to the standard evaluation data corresponding to each piece of machining data, and determine an independent quality change curve corresponding to each piece of machining data, including:

[0078] In S321, for each piece of machining data of the rearview mirror bracket according to the standard evaluation data corresponding to each piece of machining data, determine a first curve data component and image data corresponding to each piece of machining data; wherein the first curve data component is used to reflect the change trend of parameters in the machining process of the rearview mirror bracket, and the image data is used to reflect the visual information of the parameters in the machining process of the rearview mirror bracket.

[0079] It can be understood that the first curve data component reflects the change trend of process parameters over time, such as temperature, pressure, or feed rate, etc., while the image data provides visual feedback on the surface state of the workpiece when these parameters change. Data analysis methods (such as time series analysis, curve fitting, etc.) can be used to directly separate and extract trend data from the process parameter data to obtain the first curve data component. At the same time, key information on surface changes during the machining process can be extracted through image processing methods, providing a more comprehensive understanding of the quality changes during the machining process of the rearview mirror bracket.

[0080] In S322, based on the image data corresponding to each piece of machining data, determine a second curve data component corresponding to each piece of machining data.

[0081] It can be understood that the second curve data component refers to the change trend of features extracted based on the image data, reflecting the change of the surface state of the workpiece during the machining process. After the image data is processed, curve data related to surface quality, defects, etc. can be obtained through feature extraction techniques. Feature extraction methods can include edge detection, texture analysis, morphological analysis, etc. Useful visual information such as changes in surface texture, appearance of defects, and changes in workpiece shape can be extracted from the images. By analyzing these image data components, the change of surface quality during the machining process can be better understood.

[0082] Exemplarily, in S322, based on the image data corresponding to each piece of machining data, determine a second curve data component corresponding to each piece of machining data, including:

[0083] In S3221, obtain the standardized image data corresponding to each piece of machining data; wherein the standardized image data includes the standardized process images of all images in the image data corresponding to each piece of machining data.

[0084] It can be understood that a standardized image refers to an image associated with a standard process and is used as a reference image for comparison with the images obtained during the actual processing. Standardized images represent the image characteristics under ideal processing states or standard process conditions. Therefore, they play an important role in image similarity analysis. By comparing with the actual processing images, the variations during the processing can be effectively evaluated, thereby detecting processing problems or quality fluctuations that deviate from the standard process.

[0085] S3222. Determine the image similarity sequence corresponding to each segmented processing data based on the image data and the standardized image data corresponding to each segmented processing data.

[0086] It can be understood that the image similarity sequence reflects the change trend between images and can be obtained by calculating the similarity between the real-time image data corresponding to each segmented processing data and the standardized image data. For example, by calculating the cosine value between the feature vectors of the images to measure the similarity between the process contents in the images, and through the similarity measurement method, comparing with the standardized image to obtain the similarity value of each image. Arrange all the similarity values in chronological order to form a sequence, which is called the image similarity sequence. This image similarity sequence reflects the similarity degree between the images at each time point or each processing cycle and the standardized image, presenting the changes in the image characteristics during the processing. Through the fluctuations of the image similarity sequence, the stability of the processing process can be monitored in real time, and the abnormal situations that deviate from the standard process during the processing can be detected in a timely manner.

[0087] S3223. Determine the second curve data component corresponding to each segmented processing data based on the image similarity sequence corresponding to each segmented processing data.

[0088] It can be understood that the second curve data component refers to the change curve that reflects the specific quality characteristics during the processing obtained by analyzing the image similarity sequence. In contrast to the first curve data component (based on process parameters or processing characteristics), the second curve data component focuses more on the processing quality changes reflected by image comparison, such as visual-level changes in processing surface quality, process stability, etc. The second curve data component plays a supplementary role in quality detection, helping to provide more dimensional information. The image similarity sequence itself reflects the differences between each processing cycle and the standardized image. Therefore, the second curve data component can be obtained through quantification, fitting, or smoothing processing of these differences. It can include methods such as taking differences, filtering, interpolation, or other mathematical processing methods on the similarity sequence to obtain a smooth and stable curve, and this curve is the second curve data component.

[0089] S323. Determine the independent quality change curve corresponding to each segmented processing data based on the first curve data component and the second curve data component corresponding to each segmented processing data.

[0090] It can be understood that after obtaining the first curve data component and the second curve data component, these data can be fused. The fusion methods include weighted average method, summation method, principal component analysis (PCA), etc. Through the fusion method, the noise and errors in a single data source can be eliminated, and a more accurate and reliable description of the quality change can be obtained.

[0091] Exemplarily, the first curve and the second curve data component can be spliced in chronological order to obtain the quality change information in each segmented processing process. The data at each moment includes the quality change situations corresponding to the first curve and the second curve.

[0092] For example, for each time node t, the following data may be obtained:

[0093] First curve data: f(t, i) (representing the quality change related to the process parameters)

[0094] Second curve data: g(t, i) (representing the quality change obtained through image data)

[0095] By splicing the two, a new data point (t, f(t, i) + g(t, i)) can be formed, representing the quality f(t, i) + g(t, i) at time t, and a comprehensive quality change curve is formed. Through the directly spliced data sequence, an independent quality change curve reflecting the quality change in the segmented processing process can be obtained. This curve combines data from two different sources and can provide a comprehensive view of the processing quality.

[0096] S330. Based on the independent quality change curves corresponding to all segmented processing data, determine the processing quality change curve in the rearview mirror bracket processing process.

[0097] It can be understood that the independent quality change curves of all segmented processing data can be further integrated into a global processing quality change curve. The processing quality change curve summarizes the quality changes in all stages of processing and helps to analyze the quality trend in the entire processing process. By integrating all segmented data, it can be clearly seen whether the processing quality remains stable, whether there is a trend of gradual deterioration, or whether there are quality fluctuations in certain special stages.

[0098] Optionally, S330. Based on the independent quality change curves corresponding to all segmented processing data, determine the processing quality change curve in the rearview mirror bracket processing process, including:

[0099] S331. Assign values to the independent quality change curves corresponding to each segmented processing data to obtain the quality assignment curves corresponding to all independent quality change curves.

[0100] It can be understood that a quality assignment curve can be generated by assigning an independent quality value to each data point on the independent quality change curve of the segmented processing data. This curve can quantitatively represent the quality performance of each process stage. Each segmented processing data represents a specific stage in the processing, involving a certain specific process step or time period. The independent quality change curve corresponding to each segmented processing data shows the dynamic trend of the processing quality changing with time or processing parameters in this stage.

[0101] Exemplarily, in the construction of the quality assignment curve, the quality value of each data point can be adjusted to a fixed standard range (such as [0, 1]) to eliminate the influence of dimensions, enabling direct comparison of data from different process stages. The statistical characteristics of f(t)+g(t) can be used to transform the data points for assignment. By pre-determining the theoretical maximum and minimum values of f(t)+g(t), the assignment parameter M max and M min , the assignment can be carried out through the following formula: F(t)=(f(t)+g(t)-M min ) / M max -M min . Thus, a quality assignment function with respect to time t is obtained, that is, the quality assignment curve is obtained, realizing the unification of the results of image detection and the influence of parameter changes, and eliminating the influence of dimensions between different steps.

[0102] S332, Interpolate the quality assignment curves corresponding to all independent quality change curves to obtain the processing quality change curve in the process of manufacturing the rearview mirror bracket.

[0103] It can be understood that the application of the interpolation method can eliminate the fluctuations caused by sparse or discontinuous data points, making the curve smoother and more coherent. The interpolation methods include linear interpolation, spline interpolation, etc., to obtain the processing quality change curve in the process of manufacturing the rearview mirror bracket. The processing quality change curve ultimately provides the quality change trend of the entire processing process, facilitating a more comprehensive evaluation of the processing quality.

[0104] S400, Based on the processing quality change curve in the process of manufacturing the rearview mirror bracket, obtain the processing quality detection result of the rearview mirror bracket.

[0105] It can be understood that the generated processing quality change curve can be used as a basis, and through further analysis of the processing quality change curve, the processing quality detection result can be finally obtained. The processing quality detection result is a comprehensive evaluation of each process step and the whole process of the processing quality of the rearview mirror bracket, indicating whether the quality meets the expected standard during the whole processing process and pointing out possible problems or abnormalities.

[0106] In a possible implementation, in S400, according to the processing quality change curve during the processing of the rearview mirror bracket, the processing quality detection result of the rearview mirror bracket is obtained, including:

[0107] S410, according to the processing quality change curve during the processing of the rearview mirror bracket, determine multiple data feature points during the processing of the rearview mirror bracket; wherein, the data feature points are used to distinguish the processing conditions of different process steps during the processing of the rearview mirror bracket.

[0108] It can be understood that a series of data feature points can be determined through the processing quality change curve. The feature points are important processing stages or change points marked on the curve, which are used to divide the entire processing process into several different process steps. Each feature point corresponds to the quality state of a processing stage, and thus can reveal the quality performance and processing state in different process steps.

[0109] S420, according to the processing quality change curve and multiple data feature points during the processing of the rearview mirror bracket, obtain multiple independent evaluation results during the processing of the rearview mirror bracket; wherein, the independent evaluation results are used to reflect the processing conditions of a single process step during the processing of the rearview mirror bracket.

[0110] It can be understood that each independent evaluation result is based on the quality evaluation result of a specific process step. According to the data feature points, the entire processing process of the rearview mirror bracket can be divided into processing quality change curves of multiple different steps on the processing quality change curve. For example, the processing quality change curve in the preliminary forming stage (0 - 20 minutes), the processing quality change curve in the heat treatment stage (20 - 25 minutes), and the processing quality change curve in the final forming stage (25 - 60 minutes). For different stages on the processing quality change curve, different score change evaluation systems can be preset at different time periods to achieve process detection of different steps on the same curve, obtain multiple independent evaluation results, and improve the detection efficiency. Each independent evaluation result reflects the quality performance of this step during the processing, and can give a score or a judgment on the quality state (such as qualified, unqualified, good, etc.). The generation of independent evaluation results depends on the quality performance, process parameters of this stage, and the comparison with the standard process.

[0111] Exemplarily, since the values of the data points on the machining quality change curve are mainly affected by the combined action of f(t) and g(t), f(t) and g(t) can vary at different steps. Therefore, based on f(t) + g(t), quality systems for different time periods can be obtained. For example, during the heat treatment process, the evaluation system is set by the value of f1(t) + g1(t); in the final forming stage, considering dimensional accuracy and f2(t) + g2(t) comprehensively, the evaluation system is set by the value of f1(t) + g2(t). That is, the same quality fraction on the same curve may show inconsistent evaluation results due to different time periods. Therefore, different evaluation rules are set according to different time periods as needed. For example, the same score value (e.g., 85) may represent different quality states at different stages:

[0112] In the preliminary forming stage, a score of 85 may indicate good quality.

[0113] In the heat treatment stage, a score of 85 may indicate some deviation and adjustment is needed.

[0114] In the final forming stage, a score of 85 may indicate obvious problems and reprocessing is needed.

[0115] S430. Based on multiple independent evaluation results and the machining quality change curve during the machining process of the rearview mirror bracket, obtain the machining quality detection result of the rearview mirror bracket.

[0116] It can be understood that the final machining quality detection result of the rearview mirror bracket can be obtained by integrating multiple independent evaluation results and the machining quality change curve. By combining the quality evaluation results of each process step and the quality change trend during the entire machining process, a comprehensive quality determination is provided. Each independent evaluation result is based on the quality evaluation of different machining stages (such as preliminary forming, heat treatment, final forming, etc.) and reflects the machining quality performance of that stage. The independent evaluation result can be obtained by comparing the score with the standard process and can include quality states such as qualified, good, unqualified, etc. The machining quality change curve reflects the dynamic change of quality during the entire machining process. By analyzing the quality change curve, quality fluctuations and anomalies in different stages can be revealed, helping to identify potential problems in machining. By integrating multiple independent evaluation results and the machining quality change curve, a comprehensive quality score is obtained. The independent evaluation results of each process step can be weighted according to preset weights to obtain an overall result of quality detection. Each independent evaluation result can also be used as the direct machining quality detection result, providing high flexibility, being able to adapt to different quality evaluation requirements and production environments, and improving the detection efficiency and accuracy.

[0117] Corresponding to the method for detecting the processing quality of the rearview mirror bracket in the above embodiments, an embodiment of the present application also provides a system for detecting the processing quality of the rearview mirror bracket. Each unit of this system can implement each step of the method for detecting the processing quality of the rearview mirror bracket. Figure 5 The block diagram of the system for detecting the processing quality of the rearview mirror bracket provided by the embodiment of the present application is shown. For the sake of convenience of description, only the parts related to the embodiment of the present application are shown.

[0118] Referring to Figure 5 , the system for detecting the processing quality of the rearview mirror bracket includes:

[0119] An acquisition unit, configured to acquire the process parameter time-series data and real-time image data during the processing of the rearview mirror bracket;

[0120] A segmentation unit, configured to determine a plurality of segmented processing data during the processing of the rearview mirror bracket according to the process parameter time-series data and the real-time image data during the processing of the rearview mirror bracket; wherein, the segmented processing data is used to reflect the processing conditions of different process steps during the processing of the rearview mirror bracket;

[0121] An analysis unit, configured to determine a processing quality change curve during the processing of the rearview mirror bracket based on the plurality of segmented processing data of the rearview mirror bracket; wherein, the processing quality change curve is used to reflect the process quality change conditions during the processing of the rearview mirror bracket;

[0122] A result unit, configured to obtain the processing quality detection result of the rearview mirror bracket according to the processing quality change curve during the processing of the rearview mirror bracket.

[0123] It should be noted that the information interaction, execution process, etc. between the above systems / units, due to being based on the same concept as the method embodiment of the present application, for their specific functions and the technical effects brought, please refer to the method embodiment part for details, and will not be elaborated here.

[0124] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated in a processing unit, or each unit module exists physically alone, or two or more unit modules can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiment and will not be elaborated here.

[0125] The embodiment of the present application also provides a processing quality detection device for a rearview mirror bracket. Figure 6 It is a schematic structural diagram of the processing quality detection device for a rearview mirror bracket provided in an embodiment of the present application. As Figure 6 shown, the processing quality detection device 6 for a rearview mirror bracket in this embodiment includes: at least one processor 60 ( Figure 6 only one is shown in the figure), at least one memory 61 ( Figure 6 only one is shown in the figure), and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the processing quality detection device 6 for a rearview mirror bracket realizes the steps in any of the above-mentioned embodiments of the processing quality detection method for a rearview mirror bracket, or the functions of each unit in the above-mentioned system embodiments.

[0126] Exemplarily, the computer program 62 can be divided into one or more units. The one or more units are stored in the memory 61 and executed by the processor 60 to complete the present application. The one or more units can be a series of computer program instruction segments capable of completing specific functions, and these instruction segments are used to describe the execution process of the computer program 62 in the processing quality detection device 6 for a rearview mirror bracket.

[0127] The processing quality detection device for a rearview mirror bracket can be various types of intelligent monitoring devices. The processing quality detection device for a rearview mirror bracket can include, but is not limited to, a processor 60 and a memory 61. Those skilled in the art can understand that Figure 6This is only an example of the processing quality detection device 6 for the rearview mirror bracket, and does not constitute a limitation on the processing quality detection device 6 for the rearview mirror bracket. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, it may also include input / output devices, network access devices, buses, etc.

[0128] The processor 60 may be a central processing unit (CPU), and the processor 60 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0129] In some embodiments, the memory 61 may be an internal storage unit of the processing quality detection device 6 for the rearview mirror bracket, such as the hard disk or memory of the processing quality detection device 6 for the rearview mirror bracket. In other embodiments, the memory 61 may also be an external storage device of the processing quality detection device 6 for the rearview mirror bracket, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the processing quality detection device 6 for the rearview mirror bracket. Further, the memory 61 may also include both the internal storage unit and the external storage device of the processing quality detection device 6 for the rearview mirror bracket. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program, etc. The memory 61 may also be used to temporarily store data that has been output or is to be output.

[0130] The embodiments of the present application also provide a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0131] The embodiments of the present application provide a computer program product, and when the computer program product runs on the processing quality detection device for the rearview mirror bracket, the processing quality detection device for the rearview mirror bracket implements the steps in any of the above method embodiments.

[0132] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the rearview mirror bracket processing quality detection device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0133] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0134] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0135] In the embodiments provided in the present application, it should be understood that the disclosed rearview mirror bracket processing quality detection system / rearview mirror bracket processing quality detection device and method can be implemented in other ways. For example, the above-described rearview mirror bracket processing quality detection system / rearview mirror bracket processing quality detection device embodiments are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0136] The unit described as a separation component may or may not be physically separated. The component displayed as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0137] The above 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for detecting the processing quality of a rearview mirror bracket, characterized in that: include: Obtaining the process parameter timing data and real-time image data during the processing of the rearview mirror bracket; According to the process parameter time series data and the real-time image data in the process of processing the rearview mirror bracket, a plurality of segmented processing data in the process of processing the rearview mirror bracket are determined; wherein the segmented processing data is used to reflect the processing conditions of different process steps in the process of processing the rearview mirror bracket; Based on the plurality of segmented processing data of the rearview mirror bracket, determining a processing quality change curve during the processing of the rearview mirror bracket, including: Based on the plurality of segmented processing data of the rearview mirror bracket, determine the standard evaluation data corresponding to each segmented processing data; wherein the standard evaluation data is used to determine the quality of the segmented processing data; Analyze each of the segmented processing data of the rearview mirror bracket according to the standard evaluation data corresponding to each of the segmented processing data, and determine the independent quality change curve corresponding to each of the segmented processing data, including: determine the first curve data component and image data corresponding to each of the segmented processing data of the rearview mirror bracket according to the standard evaluation data corresponding to each of the segmented processing data; wherein the first curve data component is used to reflect the change trend of the parameters in the processing process of the rearview mirror bracket, and the image data is used to reflect the visual information of the parameters in the processing process of the rearview mirror bracket; based on the image data corresponding to each of the segmented processing data, determine the second curve data component corresponding to each of the segmented processing data; wherein the second curve data component is used to reflect the change of the image similarity between the image data and the standardized image data; determine the independent quality change curve corresponding to each of the segmented processing data according to the first curve data component and the second curve data component corresponding to each of the segmented processing data; Determining the processing quality change curve during the processing of the rearview mirror bracket according to the independent quality change curves corresponding to all the segmented processing data; wherein the processing quality change curve is used to reflect the process quality change during the processing of the rearview mirror bracket; According to the processing quality change curve during the processing of the rearview mirror bracket, the processing quality detection result of the rearview mirror bracket is obtained.

2. The rearview mirror bracket processing quality detection method according to claim 1, characterized in that: The method of determining a plurality of segmented processing data of the rearview mirror bracket according to the process parameter timing data and the real-time image data during the processing of the rearview mirror bracket comprises: Performing time series decomposition on the time series data of the process parameters during the processing of the rearview mirror bracket to obtain a processing characteristic curve during the processing of the rearview mirror bracket; According to the processing characteristic curve during the processing of the rearview mirror bracket, a plurality of processing cycles during the processing of the rearview mirror bracket are determined; wherein the processing cycles are different from each other; Dividing the processing characteristic curve based on different processing cycles in the processing of the rearview mirror bracket to determine a plurality of processing cycle curves in the processing of the rearview mirror bracket; Based on the multiple processing cycle curves and the real-time image data during the processing of the rearview mirror bracket, multiple segmented processing data of the rearview mirror bracket are determined.

3. The rearview mirror bracket processing quality detection method according to claim 2, characterized in that: Determining a plurality of processing cycles during the processing of the rearview mirror bracket according to the processing characteristic curve during the processing of the rearview mirror bracket includes: According to the processing characteristic curve in the processing of the rearview mirror bracket, a plurality of process time nodes in the processing of the rearview mirror bracket are determined; wherein the process time nodes are used to reflect the process start time of different process steps in the processing of the rearview mirror bracket; Based on a plurality of the process time nodes and the process characteristic curve in the process of processing the rearview mirror bracket, obtaining a correlation position of each process time node on the process characteristic curve; According to the associated position of each process time node on the processing characteristic curve, a number of processing cycles in the processing of the rearview mirror bracket are determined.

4. The rearview mirror bracket processing quality detection method according to claim 3, characterized in that: Determining a plurality of segmented processing data of the rearview mirror bracket according to a plurality of processing cycle curves and the real-time image data during the processing of the rearview mirror bracket includes: According to a plurality of the processing cycle curves and the real-time image data in the processing of the rearview mirror bracket, determining time correspondence information between each of the processing cycle curves and the real-time image data; Determine the processing cycle image data corresponding to each processing cycle curve according to the time correspondence information between each processing cycle curve and the real-time image data; Based on each of the processing cycle curves and the processing cycle image data corresponding to the processing cycle curve, a plurality of segmented processing data of the rearview mirror bracket are obtained.

5. The rearview mirror bracket processing quality detection method according to claim 1, characterized in that: The determining, based on the image data corresponding to each of the segmented processed data, a second curve data component corresponding to each of the segmented processed data, comprises: Acquire standardized image data corresponding to each of the segmented processing data; wherein the standardized image data includes standardized process images of all images in the image data corresponding to each of the segmented processing data; Determine an image similarity sequence corresponding to each of the segmented processed data according to the image data corresponding to each of the segmented processed data and the standardized image data; Based on the image similarity sequence corresponding to each of the segmented processed data, a second curve data component corresponding to each of the segmented processed data is determined.

6. The rearview mirror bracket processing quality detection method according to claim 1, characterized in that: Determining the processing quality change curve during the processing of the rearview mirror bracket according to the independent quality change curves corresponding to all the segmented processing data includes: Assigning a value to the independent mass change curve corresponding to each segmented processing data to obtain a mass assignment curve corresponding to all the independent mass change curves; The mass assignment curves corresponding to all the independent mass change curves are interpolated to obtain the processing quality change curve during the processing of the rearview mirror bracket.

7. The rearview mirror bracket processing quality detection method according to claim 1, characterized in that: The processing quality detection result of the rearview mirror bracket is obtained according to the processing quality change curve during the processing of the rearview mirror bracket, including: According to the processing quality change curve during the processing of the rearview mirror bracket, a plurality of data feature points during the processing of the rearview mirror bracket are determined; wherein the data feature points are used to distinguish the processing conditions of different process steps during the processing of the rearview mirror bracket; According to the processing quality change curve and the plurality of data characteristic points during the processing of the rearview mirror bracket, a plurality of independent evaluation results during the processing of the rearview mirror bracket are obtained; wherein the independent evaluation results are used to reflect the processing conditions of a single process step during the processing of the rearview mirror bracket; Based on the multiple independent evaluation results and the processing quality change curve during the processing of the rearview mirror bracket, a processing quality detection result of the rearview mirror bracket is obtained.

8. A rearview mirror bracket processing quality detection device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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