Lithium battery shell surface quality detection method, clamp and system for mobile phone

Abstract

The present application relates to the technical field of lithium battery, in particular to a lithium battery shell surface quality detection method, clamp and system for mobile phones. It is applied to a system comprising a clamp and a shooting assembly, a pressure detection device is arranged on the clamp, the method comprises: obtaining the pressure data sequence of the clamp when the lithium battery is clamped based on the pressure detection device; obtaining the gray scale diagram of the lithium battery through the shooting assembly, and establishing the correlation between the gray scale diagram of a single lithium battery and the corresponding pressure data sequence; analyzing the improper nature of the clamp pressure of the lithium battery based on the correlation, and analyzing the texture masking condition in the gray scale diagram of the lithium battery according to the improper nature of the clamp pressure, so as to calculate the information masking degree of the lithium battery; setting a judgment threshold according to the information masking degree, marking the detection results exceeding the threshold as suspicious detection results, and re-detecting the lithium battery corresponding to the suspicious detection results. The present application reduces the false detection rate of lithium battery shell surface quality detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of lithium batteries, in particular to a lithium battery shell surface quality detection method, clamp and system for mobile phones. BACKGROUND

[0002] The lithium battery shell of a mobile phone refers to the shell of an aluminum-plastic composite film soft-pack battery or the metal shell of a hard-shell battery, which wraps the lithium battery inside to achieve the goal of protecting the lithium battery. In order to more conveniently and quickly monitor the shell of the lithium battery, a lithium battery automatic detection device is usually configured, which uses a cylinder suction cup to adsorb the lithium battery on the product line to achieve automatic feeding, and then transfers the lithium battery to a clamp to fix the lithium battery, and transfers the lithium battery to a CCD camera through a turntable for vertical image shooting and intelligent quality detection.

[0003] In the actual process, when the lithium battery adsorbed by the cylinder suction cup is transferred to the clamp, due to improper operation, wear of the built-in spring of the clamp and other factors, the lithium battery may not be completely clamped against the clamp, resulting in a certain degree of inclination of the surface of the lithium battery, the actual loss (hidden) of part of the image information of the surface of the lithium battery shell by the CCD camera, and the possibility of misjudgment of the result of the quality detection of the surface of the lithium battery shell, thereby reducing the reliability of the quality detection result of the surface of the lithium battery shell. SUMMARY

[0004] In order to solve the technical problem that the result of the quality detection of the surface of the lithium battery shell may be misjudged, the purpose of the present application is to provide a lithium battery shell surface quality detection method, clamp and system for mobile phones, and the technical solution adopted is as follows:

[0005] In the first aspect, the present application provides a lithium battery shell surface quality detection method for mobile phones, which comprises the following steps:

[0006] A lithium battery shell surface quality detection method for mobile phones is applied to a system comprising a clamp and a shooting assembly, wherein the clamp is provided with a pressure detection device, and the method comprises the following steps:

[0007] A pressure data sequence of the clamp when the lithium battery is clamped is obtained based on the pressure detection device;

[0008] A gray scale image of the lithium battery is obtained through the shooting assembly, and an association between the gray scale image of a single lithium battery and the corresponding pressure data sequence is established;

[0009] The improper nature of the clamp pressure of the lithium battery is analyzed based on the association, and the texture masking condition in the gray scale image of the lithium battery is analyzed according to the improper nature of the clamp pressure, so as to calculate the information masking degree of the lithium battery;

[0010] Set a judgment threshold according to the information masking degree, mark the detection result exceeding the judgment threshold as a suspicious detection result, and re-detect the lithium battery corresponding to the suspicious detection result.

[0011] In some embodiments, the clamp pressure inappropriateness of the lithium battery is analyzed based on the correlation relationship, including:

[0012] Segment the pressure data sequence to obtain a plurality of pressure fluctuation segments;

[0013] Curve fitting is performed on each pressure fluctuation segment, and the pressure fluctuation segment with a negative slope is selected as a clamp closing stage;

[0014] Calculate the pressure driving property for each clamp closing stage, and determine an ideal detection interval according to the pressure data sequence;

[0015] Combine the ideal detection interval and the pressure driving property to calculate the clamp pressure inappropriateness of the lithium battery.

[0016] In some embodiments, the texture masking situation in the lithium battery grayscale image is analyzed, including:

[0017] A preliminary defect detection is performed on the lithium battery grayscale image using a preset defect recognition model;

[0018] If the detection result is that there is a defect, the texture edge identified as a defect in the lithium battery grayscale image with a defect is removed, the texture not identified as a defect is retained, and the processed texture is taken as the to-be-analyzed texture of the corresponding lithium battery grayscale image;

[0019] If the detection result is that there is no defect, all textures in the lithium battery grayscale image without a defect are taken as the to-be-analyzed texture of the corresponding lithium battery grayscale image.

[0020] In some embodiments, the information masking degree of the lithium battery is calculated, including:

[0021] The minimum distance between the to-be-analyzed textures is counted and normalized, and the to-be-analyzed texture corresponding to the normalized value meeting a preset condition is determined as a suspected masking texture;

[0022] Linear fitting is performed on the suspected masking texture, and the texture deviation difference is calculated, the clamp pressure inappropriateness and the minimum distance are combined to judge the masking possibility between the suspected masking textures;

[0023] The suspected masking texture corresponding to the masking possibility meeting a preset standard is selected as an initial masking texture, and the texture shielding degree is calculated based on the angle relationship between the initial masking texture and the edge of the lithium battery;

[0024] Combine the texture occlusion degree and the lithium battery grayscale image to calculate the information masking degree of the lithium battery.

[0025] In some embodiments, further comprising:

[0026] The lithium battery corresponding to the suspicious detection result is re-placed in the clamp, and the clamp is adjusted to a standard clamping state.

[0027] The lithium battery grayscale image is re-acquired, and the information masking degree of the lithium battery is calculated again.

[0028] If the information masking degree calculated again is still greater than the determination threshold, it is determined that the corresponding lithium battery is unqualified.

[0029] In some embodiments, further comprising:

[0030] Based on historical detection data, a mapping relationship between each lithium battery model and corresponding optimal detection parameters is constructed.

[0031] When the lithium battery model to be detected is changed, the system automatically calls the optimal detection parameters corresponding to the lithium battery model to be detected.

[0032] If the lithium battery model to be detected is a new model, detection is performed based on initial parameters, and the detection parameter library is updated in real time.

[0033] In some embodiments, further comprising:

[0034] The detection data of the lithium battery is summarized according to production batches.

[0035] The average information masking degree, and / or the defect occurrence rate, and / or the re-inspection qualified rate of each batch are calculated, and the quality fluctuation between batches is identified through a control chart analysis method. When the fluctuation exceeds a preset control threshold, a quality anomaly analysis report is generated, and the production process parameters corresponding to the production batch are associated.

[0036] In some embodiments, further comprising:

[0037] After the lithium battery grayscale image is acquired, the identified defects are classified through a pre-trained deep learning model, wherein the defect types include scratches, and / or depressions, and / or stains, and / or deformations.

[0038] Each defect type is classified according to a preset defect severity grading standard, and finally a detection report containing the defect type, the location of the defect, and the severity is output and stored.

[0039] In a second aspect, an embodiment of the present application provides a clamp, comprising:

[0040] A clamp body for clamping a lithium battery.

[0041] A pressure detection device is installed on the clamp body to detect and record pressure data in real time when the lithium battery is clamped.

[0042] A data transmission module is connected with the pressure detection device to transmit the pressure data to a processing unit.

[0043] In a third aspect, an embodiment of the present application provides a lithium battery shell surface quality detection system for a mobile phone, which is applied to a system including a clamp and a shooting assembly, and the clamp is provided with a pressure detection device.

[0044] An acquisition module is configured to acquire a pressure data sequence of the clamp when the lithium battery is clamped based on the pressure detection device.

[0045] An establishment module is configured to acquire a gray-scale image of the lithium battery through the shooting assembly, and establish an association between the gray-scale image of the single lithium battery and the corresponding pressure data sequence.

[0046] A calculation module is configured to analyze a clamp pressure improperness of the lithium battery based on the association, analyze a texture masking condition in the gray-scale image of the lithium battery according to the clamp pressure improperness, and calculate an information masking degree of the lithium battery.

[0047] A marking module is configured to set a judgment threshold according to the information masking degree, mark a detection result exceeding the judgment threshold as a suspicious detection result, and re-detect the lithium battery corresponding to the suspicious result.

[0048] In a fourth aspect, an embodiment of the present application provides an electronic device including a memory and a processor, the memory stores executable code, and the processor executes the executable code to implement the embodiments of each possible implementation of the first aspect.

[0049] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes computer program code, and when the computer program code runs on a computer, the computer executes the method in the first aspect or any one of the possible implementation manners of the first aspect.

[0050] In a sixth aspect, an embodiment of the present application provides a computer readable storage medium, which stores a computer program, and when the computer program is executed in a computer, the computer executes the embodiments of each possible implementation of the first aspect.

[0051] The embodiments of the present application have at least the following beneficial effects:

[0052] The application fuses the real-time data of the pressure detection device and the shooting assembly, constructs a pressure data-gray scale image dynamic correlation model, and accurately identifies the surface defect masking problem caused by abnormal clamp pressure (such as shell deformation caused by excessive pressure or contact blur caused by insufficient pressure) by using an information masking degree quantization algorithm, thereby effectively reducing the false detection rate of lithium battery shell surface quality detection. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art and the advantages thereof, a brief introduction will be given to the drawings needed in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on these drawings.

[0054] Figure 1 The method flowchart of the lithium battery shell surface quality detection method for mobile phones provided by an embodiment of the present application;

[0055] Figure 2 The system block diagram of the lithium battery shell surface quality detection system for mobile phones provided by an embodiment of the present application;

[0056] Figure 3 The structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0057] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined object, the following describes the lithium battery shell surface quality detection method, clamp and system for mobile phones according to the present application, the specific implementation, structure, features and effects thereof in detail in combination with the drawings and preferred embodiments.

[0058] In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0059] In the description of the embodiments of the present application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B: "and / or" in the text is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0060] Hereinafter, the terms "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eighth" are used only for descriptive purposes and should not be construed as implying or suggesting relative importance or an indicated number of technical features. Thus, features defined with "first", "second", "third", "fourth", "fifth", "sixth", "seventh" and "eighth" can explicitly or implicitly include one or more of the features.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0062] The embodiments of the present application are described below with reference to the accompanying drawings. Those skilled in the art can know that, with the development of technology and the emergence of new scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0063] The specific solutions of the method, clamp and system for detecting the surface quality of a lithium battery shell for a mobile phone provided by the present application are described below in detail with reference to the accompanying drawings.

[0064] Please refer to Figure 1 , which shows the step flowchart of the method for detecting the surface quality of a lithium battery shell for a mobile phone provided by an embodiment of the present application. The method of the present embodiment is applied to a system comprising a clamp and a shooting assembly. The clamp is provided with a pressure detection device. The method comprises the following steps:

[0065] S10. Obtain a pressure data sequence of the clamp when the lithium battery is clamped based on the pressure detection device.

[0066] The method of the present embodiment is applied to a system comprising a clamp and a shooting assembly. In actual application, a pressure detection device (for example, a pressure sensor) is installed at the bottom of the clamp. When the lithium battery is put into the clamp, the measurement starts at a preset frequency (for example, 0.5 seconds / time) and the measured pressure data of the clamp is recorded in real time until the image is collected by the shooting assembly (for example, a Charge-Coupled Device Camera (CCD)) and the recording stops. The sequence of pressure data is taken as the pressure data sequence of the lithium battery, thereby completing the clamping stage of the lithium battery.

[0067] S11. Obtain a gray scale image of the lithium battery through the shooting assembly and establish an association between the gray scale image of a single lithium battery and the corresponding pressure data sequence.

[0068] Further, the clamp clamping the lithium battery is moved (for example, through a turntable or a conveyor belt or other means) to below the pre-installed CCD camera. The CCD camera detects the object and takes an image, thereby obtaining the corresponding gray scale image of the lithium battery. The corresponding pressure data sequence of the detected lithium battery and the gray scale image of the lithium battery are uniformly transmitted to the built-in computing chip.

[0069] wherein each lithium battery corresponds to a lithium battery grayscale image and a pressure data sequence.

[0070] S12. Analyze the clamp pressure inappropriateness of the lithium battery based on the association relationship, and analyze the texture masking in the lithium battery grayscale image according to the clamp pressure inappropriateness, so as to calculate the information masking degree of the lithium battery.

[0071] In some embodiments, the analysis of the clamp pressure inappropriateness of the lithium battery based on the association relationship comprises:

[0072] Segmenting the pressure data sequence to obtain a plurality of pressure fluctuation segments;

[0073] Curve fitting is performed on each pressure fluctuation segment, and the pressure fluctuation segment with a negative slope is selected as the clamp closing stage;

[0074] Calculate the pressure pushing property for each clamp closing stage, and determine the ideal detection interval according to the pressure data sequence;

[0075] In combination with the ideal detection interval and the pressure pushing property, the clamp pressure inappropriateness of the lithium battery is calculated.

[0076] Under normal circumstances, the lithium battery automatic detection equipment will be connected with the lithium batteries produced on the production line to form an industrial automatic operation of the entire production process of the lithium battery. Therefore, when the surface of the lithium battery is detected by the lithium battery automatic detection equipment, since the quality detection of different lithium batteries by the CCD is consistent, under normal circumstances, different lithium batteries will be image collected at a relatively fixed time interval, and the pressure state corresponding to the clamp will also be basically consistent; but if there is a situation of improper clamping of the clamp, the clamp will usually need more time to adjust when clamping the lithium battery, so that the lithium battery will not be broken away from the clamp, and the more unbalanced the stress state formed by the contact between the clamp and the lithium battery, the more fluctuations will be generated in the pressure state corresponding to the clamp. Therefore, according to the pressure data of the clamp, the clamp pressure inappropriateness of the corresponding lithium battery can be calculated when the image of the lithium battery is monitored under the CCD camera.

[0077] Specifically, if the lithium battery is not completely clamped with the clamp, it means that there is a certain inclined space between the lithium battery and the horizontal surface of the clamp, which will change the original horizontal extrusion balance state of the two sides of the clamp piece in contact with the edge of the lithium battery into an incomplete horizontal extrusion balance state. During the process of transferring the lithium battery under the CCD camera, the clamp may gradually push the lithium battery over time, resulting in a decrease in the pressure detected by the clamp:

[0078] For example, in the actual process, the process of the clamp pushing is usually intermittent: in the clamp pressure sequence of the lithium battery, the combination of the adjacent slope change greater than the preset value (for example, 0.6) is taken as the clamp pressure data demarcation section; and the center of the clamp pressure data demarcation section (between two clamp pressure data) is taken as the demarcation point, so as to segment the clamp pressure sequence and obtain a plurality of clamp pressure fluctuation sections.

[0079] Further, curve fitting is performed on each clamp pressure fluctuation section to obtain a plurality of pressure fluctuation curves; and the clamp pressure fluctuation section with a negative curve slope is taken as a clamp closing stage. Each clamp pressure fluctuation section corresponds to a pressure fluctuation curve, and each lithium battery corresponds to a plurality of clamp closing stages.

[0080] Further, the formula for calculating the pressure pushing property A of each clamp closing stage is as follows:

[0081]

[0082] The greater the pressure pushing property is, the greater the pushing pressure of the lithium battery in the corresponding clamp closing stage is.

[0083] At the same time, in the industrial environment, each lithium battery is usually (due to the same operation process and no abnormal situation) collected at a fixed frequency. Under normal circumstances, if the lithium battery clamp is not clamped properly, the lithium battery may be broken off from the clamp before being transferred to image collection, thereby affecting the progress of quality detection. Therefore, if the lithium battery clamp is not clamped properly, the clamp piece is usually released, the lithium battery is re-adsorbed and placed by the air cylinder suction cup, and then the image collection of the corresponding lithium battery is continued. As can be seen from the above, the distribution state of the collection time of the lithium battery can be analyzed on the basis of the pressure pushing property, and the clamp pressure improperness of the corresponding lithium battery can be calculated.

[0084] Before the lithium battery, the lithium battery that has not been repositioned is taken as an ideal operation lithium battery; and the mean value of the length L of the clamp pressure sequence of the ideal operation lithium battery is taken as the ideal detection interval T.

[0085] It should be noted that for the same lithium battery, if it is repositioned, the corresponding process will be recorded in the clamp pressure sequence. Therefore, for a lithium battery, whether it is repositioned by the air cylinder suction cup or not, the corresponding pressure data sequence will record the clamp pressure data in the corresponding process.

[0086] According to the ideal detection interval , the pressure pushing property ​​, calculate the clamp pressure inappropriateness of the lithium battery :

[0087]

[0088] wherein, represents the clamp pressure sequence length of the lithium battery; represents the total amount of clamp closing stages contained in the clamp pressure sequence of the lithium battery; represents the pressure push of the th clamp closing stage; represents the duration of the th clamp closing stage. represents the delay of the lithium battery affecting the normal image collection progress, the greater the value, the more likely the lithium battery has the influence of improper clamp in the subsequent collected images; represents the actual possible degree of the lithium battery affected by the clamp push, the greater the value, the more likely the lithium battery produces real position movement. The greater the clamp pressure inappropriateness, the more likely the lithium battery produces real position movement, and the more likely there is improper clamp in the subsequent collected images.

[0089] In some embodiments, the analysis of the texture masking situation in the lithium battery grayscale image comprises:

[0090] using a preset defect recognition model to perform preliminary defect detection on the lithium battery grayscale image;

[0091] if the detection result is that there is a defect, the texture edge identified as a defect in the lithium battery grayscale image with a defect is removed, the texture not identified as a defect is retained, and the processed texture is taken as the to-be-analyzed texture of the corresponding lithium battery grayscale image;

[0092] if the detection result is that there is no defect, all textures in the lithium battery grayscale image without defects are taken as the to-be-analyzed texture of the corresponding lithium battery grayscale image.

[0093] In some embodiments, the calculation of the information masking degree of the lithium battery comprises:

[0094] statistically processing the minimum distance between the to-be-analyzed textures and normalizing, and determining the to-be-analyzed texture corresponding to the normalized value meeting the preset condition as a suspected masking texture;

[0095] performing linear fitting on the suspected masking texture and calculating the texture deviation difference, combining the clamp pressure inappropriateness and the minimum distance, and judging the masking possibility between the suspected masking textures;

[0096] screening the suspected shading texture corresponding to the shading possibility as the initial shading texture according to the preset standard, and calculating a texture occlusion degree based on an angle relationship between the initial shading texture and the lithium battery edge;

[0097] combining the texture occlusion degree and the lithium battery grayscale image, and calculating an information masking degree of the lithium battery.

[0098] In actual scenarios, the phenomenon of the clamp moving and closing is usually not obvious even if it actually exists, and it is not possible to visually observe whether the lithium battery is clamped firmly. The improper clamp pressure calculated through the above steps starts from the pressure fluctuation state of the clamp itself before the CCD camera captures the image, and although it can reflect the abnormal situation of the clamp when clamping the lithium battery to a certain extent (i.e., improper clamping), since the effect is ultimately presented on the image (not all improper clamping situations will affect the final quality detection result of the lithium battery shell, and some improper clamping states are not sufficient to cause identification interference to the image captured by the CCD camera), it is not advisable to use only the improper clamp pressure to reflect the situation that the final lithium battery image is disturbed by improper clamping. It is also necessary to consider the effect of the corresponding lithium battery in the photographed image to make a comprehensive judgment.

[0099] In the lithium battery grayscale image actually photographed by the lithium battery, the situation that improper clamping interferes with the identification of lithium battery grayscale image defects is that the lithium battery is not clamped completely against the clamp, and there is a certain amount of inclined space between the lithium battery and the clamp, so that when the CCD camera captures the image, the angle of view of the CCD camera is not completely perpendicular to the surface of the lithium battery shell, thereby causing the image information of part of the surface of the lithium battery shell to be actually lost (hidden) by the CCD camera. Other improper clamping situations (such as upside-down placement of the lithium battery) will not interfere with the normal collection of image information of the surface of the lithium battery shell. If there is a certain amount of inclined space between the lithium battery and the clamp, the image information of the surface of the lithium battery shell captured by the CCD camera will be hidden, causing part of the texture on the surface of the lithium battery shell to be broken. Therefore, based on the improper clamp pressure, the texture shading in the corresponding lithium battery grayscale image can be analyzed to calculate the information masking degree of the corresponding lithium battery;

[0100] Since the texture shading in the lithium battery grayscale image may be affected by the traditional detection result, it is necessary to first consider excluding the interference of the traditional detection result on the texture shading. Taking the lithium battery grayscale image of any lithium battery as an example, the existing defect identification model is used to detect defects in the lithium battery grayscale image. The use method of such a defect identification model is a well-known technical means to those skilled in the art, and will not be described here.

[0101] If the detection result is an image with defects: remove the texture edges identified as defects in the gray image of the lithium battery, retain the textures not identified as defects (for example, the textures can be identified by an edge detection algorithm), and use them as the textures to be analyzed in the gray image of the lithium battery.

[0102] If the detection result is an image without defects: all textures in the gray image of the lithium battery are used as the textures to be analyzed in the gray image of the lithium battery.

[0103] The textures to be analyzed obtained through the above steps can determine the objects suitable for analyzing texture fracture masking, and further calculate the texture masking degree of the gray image of the lithium battery:

[0104] The minimum distance D between different (two) textures is calculated; and the texture corresponding to the normalized value of the minimum distance D less than a preset value (for example, 0.3) is used as a suspected masking texture of the gray image of the lithium battery.

[0105] A straight line fitting is performed on each suspected masking texture to obtain a fitting straight line of each suspected masking texture.

[0106] The fitting straight lines of different suspected masking textures are placed in the corresponding positions in the gray image of the lithium battery, and the extension lines on the respective fitting straight lines are drawn, and the included angle formed by different extension lines is used as the texture deviation difference p between different suspected masking textures.

[0107] According to the texture deviation difference p , the minimum distance D , the improper clamp pressure , the masking possibility between different suspected masking textures is calculated , wherein norm represents a normalization function. The greater the value, the more coherent and reasonable the overall change state of the texture formed after the simulation docking of the corresponding suspected masking textures, and the more likely the corresponding suspected masking textures are masked. In the embodiment of the present application, the normalization function can use a maximum-minimum value normalization function.

[0108] Since the textures distributed on the surface of the conventional lithium battery shell are usually regularly distributed, when some textures are masked due to the inclination of the lithium battery, the information of this part of the masked textures will be masked in the overall direction along the edge of the lithium battery due to the formation of the inclination direction:

[0109] Suspected masking textures with a masking possibility greater than a preset value (for example, 0.7) are used as initial masking textures; and the minimum circumscribed rectangle of the overall area occupied by the initial masking textures is used as the initial masking distribution area of the lithium battery.

[0110] Calculate the angle between the length of the initial shading distribution area and the four edges of the lithium battery, and take the inverse value of the minimum angle as the texture shielding degree K of the lithium battery grayscale image. The greater the value, the more likely it is that the area distribution of the shading texture in the lithium battery grayscale image conforms to the shading distribution state formed when the lithium battery is tilted; the more likely it is that the shading information exists in the lithium battery grayscale image.

[0111] In addition, when the lithium battery is tilted, the size of the lithium battery formed in the corresponding lithium battery grayscale image will be reduced to a certain extent, so the analysis of the size change of the lithium battery can be added on the basis of the texture shielding degree to finally obtain the information masking degree of the corresponding lithium battery :

[0112]

[0113] Among them, represents the difference between the area of the lithium battery in the lithium battery grayscale image of the lithium battery and the standard area of the same model lithium battery stored in the database. The greater the information masking degree, the more likely it is that the lithium battery will tilt due to improper clamping by the clamp, resulting in a tilt space between the lithium battery and the clamp, so that more information of the lithium battery is masked in the lithium battery grayscale image obtained by the CCD camera, and the interference with the surface quality detection of the lithium battery shell is greater.

[0114] S13. According to the information masking degree, set a decision threshold (for example, 0.4), and mark the detection results exceeding the decision threshold as suspicious detection results. The lithium battery corresponding to the suspicious detection results is re-detected.

[0115] In some embodiments, further comprising:

[0116] Placing the lithium battery corresponding to the suspicious detection results on the clamp again and adjusting the clamp to a standard clamping state;

[0117] Reacquiring the lithium battery grayscale image and calculating the information masking degree of the lithium battery again;

[0118] If the information masking degree calculated again still exceeds the decision threshold, it is determined that the corresponding lithium battery is unqualified.

[0119] In some embodiments, further comprising:

[0120] Based on historical detection data, a mapping relationship between each lithium battery model and the corresponding optimal detection parameters is constructed;

[0121] When the lithium battery model to be detected is replaced, the system automatically calls the optimal detection parameters corresponding to the lithium battery model to be detected;

[0122] If the lithium battery model to be detected is a new model, detection is performed based on the initial parameters and the detection parameter library is updated in real time.

[0123] In practical applications, a mapping relationship library of lithium battery models and optimal detection parameters can be constructed, and the detection parameters can include imaging exposure time, light source intensity, image processing threshold, etc. Specifically, a decision tree algorithm (such as Classification and Regression Tree (CART)) can be used to analyze historical detection data to generate parameter-model association rules (such as if model = A → exposure time = 20 ms, light source intensity = 80%). Then, the model of the lithium battery to be detected is read through a barcode scanner or Radio Frequency Identification (RFID), and the system automatically calls the corresponding optimal parameters from the parameter library and configures the detection equipment.

[0124] If the detection model is a new model (no record in the parameter library), the default initial parameters (such as exposure time = 15 ms) are used for detection, and the detection results (such as defect types) are recorded in real time. Online learning algorithms (such as incremental Support Vector Machine (SVM)) are used to gradually optimize the parameters of the new model, and the parameter library is updated every N times of detection (N is a preset learning sample size).

[0125] In some embodiments, further comprising:

[0126] The detection data of the lithium batteries are aggregated according to production batches;

[0127] The average information masking degree, and / or defect occurrence rate, and / or re-inspection qualified rate of each batch are calculated, and the quality fluctuations between batches are identified through control chart analysis method, and when the fluctuations exceed the preset control threshold, a quality abnormality analysis report is generated and the production process parameters of the corresponding production batch are associated.

[0128] Specifically, the detection data are aggregated according to production batches, including defect types and location information of each lithium battery. A time series database can be used to store batch data, supporting quick queries according to time range. Further, the batch average information masking degree, defect occurrence rate (defect sample number / total sample number), and re-inspection qualified rate (second detection qualified number / first detection unqualified number) are calculated.

[0129] Further, X-bar control charts (monitoring average information masking degree) or P control charts (monitoring defect occurrence rate) can be drawn by integrating Minitab or Python stats models library, and when the indicators exceed the control threshold (such as ±3σ, where σ represents standard deviation), a quality abnormality report is generated.

[0130] In some embodiments, further comprising:

[0131] After obtaining the lithium battery grayscale image, the identified defects are classified through a pre-trained deep learning model, wherein the defect types include scratches, and / or depressions, and / or stains, and / or deformations;

[0132] Each defect type is graded according to a preset defect severity grading standard, and finally a detection report containing the defect type, the location of the defect and the severity is output and stored.

[0133] Specifically, a deep learning model (such as ResNet-50) can be trained based on a pre-labeled defect image dataset (containing four types of scratches, depressions, stains, and deformations (additional defect types can be added according to actual needs)). The model training is realized using the PyTorch framework, and the data enhancement strategy includes rotation, flipping and noise injection, etc. Further, the collected lithium battery grayscale image is input into the trained model to output the defect type and location (annotated by the bounding box). Finally, according to the preset grading standard (such as scratches longer than 5mm are severe, 2-5mm are moderate, and <2mm are slight), the defects are graded, a JSON format report containing the type, location and grading is generated, and stored in the database for traceability.

[0134] The embodiment of the present application provides a clamp, comprising:

[0135] A clamp body for clamping a lithium battery;

[0136] A pressure detection device mounted on the clamp body for real-time detection and recording of pressure data when clamping the lithium battery;

[0137] A data transmission module connected with the pressure detection device for transmitting the pressure data to a processing unit.

[0138] Embodiment two:

[0139] Please refer to Figure 2 which shows a lithium battery shell surface quality detection system for mobile phones provided by an embodiment of the present application, applied to a system comprising a clamp and a shooting assembly, wherein the clamp is provided with a pressure detection device, and the system comprises the following modules:

[0140] An acquisition module 20 for acquiring a pressure data sequence of the clamp when the lithium battery is clamped based on the pressure detection device;

[0141] An establishment module 21 for acquiring a lithium battery grayscale image through the shooting assembly and establishing an association between the single lithium battery grayscale image and the corresponding pressure data sequence;

[0142] The computing module 22 is configured to analyze the clamp pressure abnormality of the lithium battery based on the correlation relationship, and analyze the texture masking in the gray image of the lithium battery according to the clamp pressure abnormality, so as to calculate the information masking degree of the lithium battery.

[0143] The marking module 23 is configured to set a judgment threshold according to the information masking degree, mark the detection result exceeding the judgment threshold as a suspicious detection result, and re-detect the lithium battery corresponding to the suspicious result.

[0144] Optionally, the transmission medium can be a wired link, such as but not limited to a coaxial cable, an optical fiber, a digital subscriber line, etc., or a wireless link, such as but not limited to Wireless Fidelity (WIFI), Bluetooth, and a mobile device network, etc.

[0145] It should be noted that the device provided in the above embodiment is only exemplified by the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the computer device is divided into different functional modules to complete all or part of the above-described functions.

[0146] Figure 3 is a structural schematic diagram of a computer device provided by an embodiment of the present application. As shown in the example, Figure 3 the computer device 30 includes a memory 31, a processor 32, and a computer program 33 stored in the memory 31 and running on the processor 32, wherein the processor 32 executes the computer program 33, so that the computer device can execute any of the above-described lithium battery shell surface quality detection methods for mobile phones.

[0147] In addition, an embodiment of the present application also protects a device, which can include a memory and a processor, wherein the memory stores executable program code, and the processor is configured to call and execute the executable program code to execute the lithium battery shell surface quality detection method for mobile phones provided by the embodiment of the present application.

[0148] The embodiment of the present application can divide the device into functional modules according to the above method examples, for example, each functional module can be corresponding, or two or more functions can be integrated in one processing module, and the above integrated module can be realized in the form of hardware. It should be noted that the division of the modules in the present embodiment is illustrative, and is only a logical function division, and another division mode can be used in actual implementation.

[0149] It should be understood that the device provided by the embodiment of the present application is used to execute the above-described lithium battery shell surface quality detection method for mobile phones, and thus the same effect as the above-mentioned implementation method can be achieved.

[0150] In the case of employing the integrated unit, the device can include a processing module, a storage module. Wherein, when the device is applied to the equipment, the processing module can be used to control and manage the action of the equipment. The storage module can be used to support the equipment to execute the mutual program code and the like. Wherein, the processing module can be a processor or a controller, which can realize or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure. The processor can also be a combination of computing functions, such as including one or more microprocessor combinations, a combination of digital signal processing (Digital Signal Processing, DSP) and microprocessor, and the like, and the storage module can be a memory.

[0151] In addition, the device provided by the embodiment of the present application can be a chip, an assembly or a module, the chip can include a connected processor and a memory; wherein the memory is used to store instructions, when the processor calls and executes the instructions, the chip can execute the method for detecting the surface quality of the lithium battery shell of the mobile phone provided by the above embodiment.

[0152] The embodiment of the present application also provides a computer readable storage medium, the computer readable storage medium has computer program code stored therein, when the computer program code runs on the computer, the computer program code makes the computer execute the above related method steps to realize the method for detecting the surface quality of the lithium battery shell of the mobile phone provided by the above embodiment.

[0153] The embodiment of the present application also provides a computer program product, when the computer program product runs on the computer, the computer program product makes the computer execute the above related steps to realize the method for detecting the surface quality of the lithium battery shell of the mobile phone provided by the above embodiment.

[0154] Wherein, the device, the computer readable storage medium, the computer program product or the chip provided by the embodiment of the present application are used to execute the corresponding method provided above, therefore, the beneficial effects that can be achieved are referred to the beneficial effects in the corresponding method provided above, which will not be repeated here. Through the description of the above embodiment, the person skilled in the art can understand that, for the convenience and brevity of description, only the above functional module division is taken as an example, in actual application, the above functions can be completed by different functional modules according to the needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In the embodiment provided by the present application, it should be understood that the disclosed device and method can be realized by other ways.

[0155] The apparatus embodiments described above are only illustrative, for example, the division of the modules or units is only a logical function division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another apparatus, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, and can be electrical, mechanical or other forms.

[0156] It should also be noted that the terms "comprising", "including", or any other variant thereof, are intended to cover a non-exclusive inclusion, such that processes, methods, articles, or apparatuses that comprise a list of elements not only include those elements, but also include other elements not expressly listed or inherent to such processes, methods, articles, or apparatuses. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0157] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0158] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0159] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for detecting the surface quality of a lithium battery casing for mobile phones, characterized in that, Applied to a system including a clamp and a shooting assembly, wherein the clamp is equipped with a pressure detection device, the method includes the following steps: The pressure data sequence of the clamp when the lithium battery is clamped is obtained based on the pressure detection device; The imaging component acquires a grayscale image of the lithium battery and establishes a correlation between the grayscale image of a single lithium battery and the corresponding pressure data sequence. Based on the aforementioned correlation, the improper clamping pressure of the lithium battery is analyzed, and based on the improper clamping pressure, the texture occlusion in the grayscale image of the lithium battery is analyzed, thereby calculating the degree of information occlusion of the lithium battery. A judgment threshold is set based on the degree of information obfuscation. Detection results that exceed the judgment threshold are marked as suspicious detection results, and the lithium batteries corresponding to the suspicious detection results are re-detected.

2. The method for detecting the surface quality of a lithium battery casing for mobile phones according to claim 1, characterized in that, The analysis of improper clamping pressure of lithium batteries based on the aforementioned correlation includes: The pressure data sequence is segmented to obtain several pressure fluctuation segments; Curve fitting is performed on each of the pressure fluctuation segments, and the pressure fluctuation segments with negative slopes are selected as the clamp closing stage. The pressure driving force is calculated for each clamp closing stage, and the ideal detection interval is determined based on the pressure data sequence; The improper clamping pressure of the lithium battery is calculated by combining the ideal detection interval and the pressure driving force.

3. The method for detecting the surface quality of a lithium battery casing for mobile phones according to claim 1, characterized in that, The analysis of texture occlusion in the grayscale image of the lithium battery includes: A pre-defined defect identification model is used to perform preliminary defect detection on the grayscale image of the lithium battery. If the detection result indicates the presence of defects, the texture edges identified as defects in the grayscale image of the lithium battery with defects will be removed, while the textures not identified as defects will be retained, and the processed textures will be used as the textures to be analyzed in the corresponding grayscale image of the lithium battery. If the detection result indicates that there are no defects, then all textures in the grayscale image of the lithium battery without defects will be used as the textures to be analyzed in the corresponding grayscale image of the lithium battery.

4. The method for detecting the surface quality of a lithium battery casing for mobile phones according to claim 3, characterized in that, The calculation of the information masking degree of the lithium battery includes: The minimum distance between the textures to be analyzed is calculated and normalized. The textures to be analyzed that meet the normalized values ​​are identified as suspected occlusion textures. The suspected occlusion textures are fitted with straight lines and the texture bias difference is calculated. The occlusion probability between the suspected occlusion textures is determined by combining the improper clamp pressure with the minimum distance. Select the suspected occlusion textures that meet the preset criteria as the initial occlusion textures, and calculate the degree of texture occlusion based on the angular relationship between the initial occlusion textures and the edge of the lithium battery. The degree of information masking of the lithium battery is calculated by combining the texture occlusion degree and the lithium battery grayscale image.

5. The method for detecting the surface quality of a lithium battery casing for mobile phones according to claim 1, characterized in that, Also includes: The lithium battery corresponding to the questionable test result is placed back into the fixture, and the fixture is adjusted to the standard clamping state; Re-acquire the grayscale image of the lithium battery and recalculate the degree of information masking of the lithium battery; If the degree of information obfuscation still exceeds the judgment threshold after recalculation, the corresponding lithium battery is judged to be unqualified.

6. The method for detecting the surface quality of a lithium battery casing for a mobile phone according to claim 1, characterized in that, Also includes: A mapping relationship between each lithium battery model and its corresponding optimal testing parameters was constructed based on historical testing data. When the lithium battery model to be tested is changed, the system automatically calls the optimal testing parameters corresponding to the lithium battery model to be tested; If the lithium battery model to be tested is a new model, the test will be performed based on the initial parameters and the test parameter library will be updated in real time.

7. The method for detecting the surface quality of a lithium battery casing for mobile phones according to claim 1, characterized in that, Also includes: The test data of lithium batteries are summarized according to production batch; Calculate the average information masking level, and / or defect incidence rate, and / or re-inspection pass rate for each batch, and identify quality fluctuations between batches through control chart analysis. When the fluctuation exceeds the preset control threshold, generate a quality anomaly analysis report and associate it with the production process parameters of the corresponding production batch.

8. The method for detecting the surface quality of a lithium battery casing for mobile phones according to claim 1, characterized in that, Also includes: After obtaining the grayscale image of the lithium battery, the identified defects are classified using a pre-trained deep learning model. The defect types include scratches, and / or dents, and / or stains, and / or deformations. Each defect type is classified according to a preset defect severity grading standard, and a detection report containing the defect type, defect location, and severity is output and stored.

9. A clamp, characterized in that, The fixture is used to implement the method for detecting the surface quality of a lithium battery casing for a mobile phone according to any one of claims 1-8, and the fixture includes: The clamp body is used to hold the lithium battery; A pressure detection device is installed on the clamp body to detect and record pressure data when clamping the lithium battery in real time; The data transmission module is connected to the pressure detection device and is used to transmit pressure data to the processing unit.

10. A surface quality inspection system for a lithium battery casing of a mobile phone, characterized in that, An application is made in a system including a clamp and a shooting assembly, wherein the clamp is equipped with a pressure detection device, and the system includes the following modules: The acquisition module is used to acquire the pressure data sequence of the clamp when the lithium battery is clamped based on the pressure detection device; A module is established to acquire a grayscale image of a lithium battery through the imaging component and to establish a correlation between the grayscale image of a single lithium battery and the corresponding pressure data sequence. The calculation module is used to analyze the improper clamping pressure of the lithium battery based on the correlation relationship, and to analyze the texture occlusion in the grayscale image of the lithium battery based on the improper clamping pressure, thereby calculating the degree of information occlusion of the lithium battery. The marking module is used to set a judgment threshold based on the degree of information obfuscation, mark the detection results that exceed the judgment threshold as suspicious detection results, and re-detect the lithium batteries corresponding to the suspicious results.

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