A dry battery negative cover flaw detection method and system based on image processing

Abstract

The present application relates to a kind of dry battery negative cover flaw detection method and system based on image processing, it is related to image processing technical field, it includes obtaining dry battery negative cover model;According to dry battery negative cover model, find pressure sensing module number and standard moving end point;Control pressure sensing module is installed on electric sliding table, receives the installation completion signal fed back by pressure sensing module;After receiving installation completion signal, pressure sensing module is moved to standard moving end point along the direction of movement, and obtains bearing pressure;According to actual pressure obtained by bearing pressure;According to actual pressure, obtain pressure distribution image;Pressure distribution image is compared with standard pressure distribution image to obtain abnormal area and pressure difference;When pressure difference is all less than 0, output pit flaw signal;When pressure difference is all greater than 0, output protrusion flaw signal;When abnormal area does not exist, output qualified signal.The present application has the effect of improving the accuracy of dry battery negative cover flaw detection.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to a method and system for detecting defects on the negative electrode cover of a dry battery based on image processing. Background Art

[0002] The negative electrode cover of a dry battery is a metal (or composite) component located at the bottom of a cylindrical dry battery, which is the electrical output contact of the negative electrode of the battery and simultaneously plays a key role in sealing, fixing, and current collection.

[0003] Currently, most detections adopt a machine vision solution. The system collaboratively acquires multi-directional images of the product through multiple cameras. After preprocessing for noise reduction, enhancement, and distortion correction, it extracts defect areas, perimeters, contours, and texture features. Combining with a deep learning model to identify defects such as scratches, burrs, deformations, and abnormal coatings, and determining whether it is qualified through a threshold.

[0004] Regarding the above related technologies, most of these detection methods are in an environment that requires lighting. During such a detection process, it is easily affected by environmental light and noise, and is prone to over-brightness, resulting in highlight overflow, loss of details, and whiteness, making the picture have abnormal exposure, thus causing a problem of deviation in identifying defects. Summary of the Invention

[0005] In order to improve the accuracy of detecting defects on the negative electrode cover of a dry battery, the present invention provides a method and system for detecting defects on the negative electrode cover of a dry battery based on image processing.

[0006] In the first aspect, the present invention provides a method for detecting defects on the negative electrode cover of a dry battery based on image processing, adopting the following technical solution: A method for detecting defects on the negative electrode cover of a dry battery based on image processing includes: Responding to a detection signal to obtain the model number of the negative electrode cover of the dry battery; Searching for the corresponding pressure sensing module number and the corresponding standard moving end point according to the model number of the negative electrode cover of the dry battery; Controlling the pressure sensing module corresponding to the pressure sensing module number to be installed on the electric sliding table and receiving the installation completion signal fed back by the pressure sensing module; When receiving the installation completion signal, moving the pressure sensing module corresponding to the pressure sensing module number along the preset moving direction to the standard moving end point and obtaining the pressure borne by all pressure sensing units in the pressure sensing module; Obtaining the actual pressure at each pixel point according to the pressure borne; Obtaining a pressure distribution image according to the actual pressure; Comparing the pressure distribution image with a preset standard pressure distribution image to obtain an abnormal area and the corresponding pressure difference; When the pressure difference is less than 0, a preset pit defect signal is output. When the pressure difference is greater than 0, a preset protrusion defect signal is output; When the abnormal region does not exist, a preset qualified signal is output.

[0007] By adopting the above technical solution, the defect type of the dry cell negative electrode cover is obtained by comparing the pressure distribution image obtained by the pressure sensing module with the standard pressure distribution image. Based on the pressure feedback between surfaces, the influence of ambient light and noise on the detection is reduced, and the accuracy of the detection is improved.

[0008] Optional, also includes: Move the pressure sensing module corresponding to the pressure sensing module number along the moving direction until a real-time pressure distribution image is generated. The actual pressure at each pixel in the real-time pressure distribution image is greater than 0. When the real-time pressure distribution image is completely consistent with the standard pressure distribution image, the movement position of the pressure sensing module is obtained; When the moving location is not the standard moving endpoint, the moving difference is obtained by analyzing the moving location and the standard moving endpoint. When the movement difference is within the preset acceptable threshold range, an acceptable signal is output; When the movement difference is outside the acceptable threshold range, a preset unacceptable signal is output; When the real-time pressure distribution image is not completely consistent with the standard pressure distribution image, the real-time pressure difference is obtained by comparing the real-time pressure distribution image with the standard pressure distribution image. When the real-time pressure difference is less than 0, output a pit defect signal; When the real-time pressure difference is greater than 0, a bulge defect signal is output.

[0009] By adopting the above technical solution, the battery height of the dry cell can be determined based on the actual moving distance of the obtained real-time pressure distribution image, and dry cell batteries with unqualified battery height can be directly rejected, thus improving the accuracy of detection.

[0010] Optional, also includes: Acquire the real-time pressure of all pressure sensing units as the device moves along the direction of movement; When the real-time pressure equals the preset damage threshold, the detection stops, the pressure sensing module is controlled to move along the preset return direction to the preset starting point, and an unqualified signal is output.

[0011] By adopting the above technical solution, the pressure sensing module is prevented from being damaged by excessive pressure by real-time pressure detection, thus improving the safety of the detection.

[0012] Optional, also includes: When an abnormal area exists, obtain the historical pressure distribution image of the pressure sensing module corresponding to the pressure sensing module number during the movement along the moving direction; Historical pressure regions were identified based on historical pressure distribution images. Based on the historical pressure areas, the trend of regional range change is obtained. The trend of regional range change includes the trend of unchanged regional range and the trend of regional range expansion. When the trend of the area range change is that the area range is expanding, the current detection is completed and a preset detection completion signal is issued; Upon receiving the detection completion signal, the pressure sensing module is moved to the preset cleaning position to perform the preset cleaning task and receive the cleaning completion signal from the pressure sensing module. When a cleaning completion signal is received, the control pressure sensor module moves to the starting point and outputs a preset oil stain defect signal. When the trend of the area range change is the same as the trend of the area range remaining unchanged, output a pit defect signal or a raised defect signal.

[0013] By adopting the above technical solution, the abnormal area range changes during the descent of the pressure sensing module, which determines whether the abnormality is caused by oil contamination. The pressure sensing module is then cleaned, thus improving the accuracy of the detection.

[0014] Optional, also includes: Once the pressure sensing module moves to the cleaning position, the abnormal area is obtained by analyzing the abnormal region. The pressure difference obtained by comparing the pressure distribution image with the standard pressure distribution image is used to search for the corresponding oil viscosity value in the preset viscosity database. The corresponding wiping path is obtained by performing path analysis based on the abnormal area; If the viscosity of the oil stain is greater than the preset viscosity threshold, the preset cleaning device is controlled to spray a special solvent onto the surface of the pressure sensing module, and the spraying completion signal is received from the pressure sensing module. After receiving the spraying completion signal, stop spraying the special solvent, control the cleaning device to wipe the surface of the pressure sensing module along the wiping path and receive the cleaning completion signal fed back by the pressure sensing module; If the viscosity value of the oil stain is less than the viscosity threshold and the abnormal area is greater than the preset wiping area threshold, the preset blowing device is turned on, and the cleaning device is controlled to wipe the surface of the pressure sensing module along the wiping path and receive the cleaning completion signal fed back by the pressure sensing module. After receiving the cleaning completion signal, turn off the blower. If the pressure difference is less than the viscosity threshold and the abnormal area is less than the wiping area threshold, the blowing device is turned on and the cleaning completion signal is received from the pressure sensing module. After receiving the cleaning completion signal, turn off the blower.

[0015] By adopting the above technical solution, a suitable cleaning scheme can be determined based on the coverage and viscosity of the oil stains, thereby improving the safety of the detection.

[0016] Optional, also includes: Obtain the coordinates of pixels in the abnormal region and define these coordinates as abnormal coordinates; When all abnormal coordinates are within the preset edge region, the abnormal pixels are processed to obtain the abnormal connected region. Location features are obtained by analyzing abnormal connected regions; When the location feature is the preset outermost feature, the pressure distribution image is compared with the standard pressure distribution image to obtain the pressure difference; When the pressure difference is greater than 0, a preset upward warping defect signal is output. When the pressure difference is less than 0, a preset downward warping defect signal is output.

[0017] By adopting the above technical solution, the specific type of defect can be determined based on the specific location of the abnormal area on the negative electrode cover of the dry cell, thereby improving the accuracy of detection.

[0018] Optional, also includes: The highest pressure is obtained by comparing the actual pressure at each pixel. Find the pixel corresponding to the highest pressure based on the highest pressure, and define that pixel as the starting pixel. The surrounding pixels are obtained by performing a surrounding lookup based on the starting pixel. The surrounding pressure is determined by finding the surrounding pixels; The pressure gradient value is obtained by gradient analysis based on the surrounding pressure. When the pressure gradient value is greater than the preset high gradient threshold, a preset foreign object defect signal is output. When the pressure gradient value is less than the high gradient threshold, a protrusion defect signal is output.

[0019] By adopting the above technical solution, the specific type of the negative electrode cover of the dry cell can be determined based on the gradient change of pressure in the abnormal area, thus improving the accuracy of detection.

[0020] Optional, also includes: The area of ​​the protrusion is obtained from the analysis of the abnormal area; Find the functional area of ​​the dry cell negative terminal cover that corresponds to the dry cell negative terminal cover model. The location of the protrusion was determined by analyzing the abnormal area and pressure distribution images. When the following conditions are met simultaneously: the pressure gradient value is less than the preset qualified gradient threshold, the protrusion area is less than the preset qualified area threshold, and the protrusion position is not within the functional area, a qualified signal is output. When the following conditions are not met simultaneously: the pressure gradient value is less than the preset qualified gradient threshold, the protrusion area is less than the preset qualified area threshold, and the protrusion position is not within the functional area, an unqualified signal is output.

[0021] By adopting the above technical solution, the impact of the protrusion on the use of dry cell batteries can be determined based on the specific location, pressure gradient, and area size of the protrusion, thereby improving the accuracy of the detection.

[0022] Optional, also includes: Obtain historical data on abnormal areas; Overlapping anomaly regions are identified through location analysis based on historical anomaly region data. The number of times the overlapping anomaly region appears is obtained by searching the preset anomaly count database. When the number of occurrences exceeds the preset suspected fault threshold, a preset self-test current is output to the pressure sensing module to obtain a self-test pressure distribution image; When the self-test pressure distribution image has preset fault characteristics, the detection stops and a preset fault signal is sent.

[0023] By adopting the above technical solution, the location of the pressure sensing module that has repeatedly shown abnormalities is self-checked, preventing misjudgments caused by detection equipment failure and improving the accuracy of detection.

[0024] Secondly, this invention provides a dry cell battery negative electrode cap defect detection system based on image processing, employing the following technical solution: A dry cell battery negative electrode cap defect detection system based on image processing includes: The acquisition module is used to acquire data on the model of the negative terminal cover of the dry cell battery, the pressure it can withstand, the real-time pressure, the abnormal coordinates, and the historical abnormal area data. A memory for storing the program of the image processing-based dry cell negative electrode cap defect detection method as described above; The processor loads and executes programs from memory.

[0025] By adopting the above technical solution, the defect type of the dry cell negative electrode cover is obtained by comparing the pressure distribution image obtained by the pressure sensing module with the standard pressure distribution image. Based on the pressure feedback between surfaces, the influence of ambient light and noise on the detection is reduced, and the accuracy of the detection is improved.

[0026] In summary, the present invention has at least one of the following beneficial technical effects: The distribution of defects on the negative electrode cap of the dry cell battery is obtained by comparing the pressure distribution image with the standard pressure image, which reduces the influence of ambient light and noise on the detection and improves the accuracy of the detection. By detecting real-time pressure, excessive pressure is prevented from damaging the pressure sensing module, thus improving the safety of the detection. The location of the pressure sensing module that repeatedly shows abnormalities is self-checked to prevent misjudgment due to equipment failure and improve detection accuracy. Attached Figure Description

[0027] Figure 1 This is a flowchart of a method for detecting defects in the negative electrode cap of a dry cell battery based on image processing, as described in an embodiment of this application. Detailed Implementation

[0028] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments.

[0029] This invention discloses a method for detecting defects in the negative electrode cap of a dry cell battery based on image processing. (Refer to...) Figure 1 A method for detecting defects in the negative electrode cap of a dry cell battery based on image processing includes: Step 1: In response to the detection signal, obtain the model number of the dry cell negative terminal cap.

[0030] The detection signal refers to the start signal that triggers the detection process. It is generated and sent as a completion response after the dry cell battery is correctly positioned, detected by automated sensing devices (such as photoelectric sensors) on the production line. The dry cell battery negative terminal cap model number refers to the specification marking of the dry cell battery negative terminal cap. This is obtained through barcode scanning on the production line.

[0031] Step 2: Locate the corresponding pressure sensor module number and the corresponding standard moving endpoint based on the dry cell negative terminal cap model.

[0032] The pressure sensor module number is a unique identifier assigned to different specifications of pressure sensor modules. The standard endpoint refers to the target position where the pressure sensor module achieves a standard fit and completes the test. Both are obtained by entering the dry cell battery negative terminal cap model into a pre-set test method database. This database is created beforehand by staff who input different dry cell battery negative terminal caps, their corresponding pressure sensor module numbers, and standard endpoints into the system. The standard endpoint is determined by personnel conducting pressure tests on different models of flawless, qualified dry cell battery negative terminal caps under standard testing conditions. The position where the clearest pressure distribution image is obtained is the corresponding standard endpoint.

[0033] Step 3: Install the pressure sensor module corresponding to the pressure sensor module number on the electric slide and receive the installation completion signal from the pressure sensor module.

[0034] The pressure sensing module is a detection module used to detect the distribution of defects in the negative terminal cap of a corresponding dry cell battery. The electric slide is an automated transmission device that drives the pressure sensing module to move precisely in a preset linear direction. Through control commands from the detection system, the connection structure between the pressure sensing module and the electric slide is automatically engaged, completing the control process. The installation completion signal is a status feedback signal sent by the pressure sensing module to the detection system processor after the pressure sensing module and the electric slide are reliably installed. When the pressure sensing module and the electric slide are installed, the sensor triggers and generates an electrical signal, transmitting the installation completion signal.

[0035] Step 4: When the installation completion signal is received, move the pressure sensing module corresponding to the pressure sensing module number along the preset moving direction to the standard moving end point, and obtain the pressure that all pressure sensing units in the pressure sensing module can withstand.

[0036] The direction of movement refers to the pre-set direction of movement of the pressure sensing module in the detection system. A movement control command is sent to the electric slide to control the electric slide to move the pressure sensing module linearly along a fixed direction (such as the negative Z-axis). The pressure sensing unit is the basic detection unit of the pressure sensing module. The pressure withstand is the actual physical pressure value experienced by each pressure sensing unit after contact with the surface of the negative terminal cover of the dry cell battery. Through the pressure-to-electrical signal conversion characteristics of the pressure sensing unit, the physical pressure experienced by each unit is converted into an acquireable electrical signal (such as a voltage / current signal). By collecting the pressure data of all units, the pressure withstand is obtained.

[0037] Once the installation completion signal is received, it indicates that the pressure sensing module has been installed and subsequent testing steps can be performed.

[0038] Step 5: Obtain the actual pressure on each pixel based on the pressure it bears.

[0039] A pixel is the smallest unit in a pressure distribution image. Actual pressure refers to the pressure value corresponding to each pixel in the pressure distribution image after data conversion. The pressure values ​​are standardized and calibrated (e.g., to eliminate zero-point drift and unit bias) to obtain the actual pressure corresponding to each pixel.

[0040] Step 6: Obtain the pressure distribution image based on the actual pressure.

[0041] A pressure distribution image is an image that visually represents the pressure distribution on the surface of the negative electrode cap of a dry cell battery. Based on the coordinates of each pixel and its corresponding actual pressure, a visualized pressure distribution image is generated according to a preset pressure value-grayscale gradient correlation. The pressure value-grayscale gradient correlation is constructed by researchers inputting different pressure values ​​and their corresponding grayscale values ​​into the system; for example, the higher the pressure value, the higher the grayscale value, and 0 pressure is displayed as white.

[0042] Step 7: Compare the pressure distribution image with the preset standard pressure distribution image to obtain the abnormal areas and the corresponding pressure differences.

[0043] A standard pressure distribution image refers to the pressure distribution image of a flawless, qualified negative electrode cap for a dry cell battery. The standard pressure distribution image is obtained by conducting pressure tests on negative electrode caps of different models of dry cell batteries under standard testing conditions, and the clearest pressure distribution image is used as the standard pressure distribution image. An abnormal region refers to a continuous area formed by pixels with significantly deviated pressure values. The pressure values ​​of pixels at the same coordinate in the standard pressure distribution image and the standard pressure distribution image are compared point-by-point. Pixels with pressure values ​​deviating beyond a preset normal range are marked as abnormal pixels. Consecutive abnormal pixels are then connected to form abnormal regions. The pressure distribution images of the same flawless, qualified negative electrode cap for a dry cell battery are obtained through repeated testing by the experimenter, and the variation in pressure at the same pixel is analyzed to determine the normal range. The pressure difference is the difference between the pressure value of each pixel in the pressure distribution image and the pressure value of the corresponding pixel at the same coordinate in the standard pressure distribution image. Pressure difference = Pressure of pixel in pressure distribution image - Pressure of pixel in corresponding standard pressure distribution image.

[0044] Step 70: When the pressure difference is less than 0, output the preset pit defect signal.

[0045] The dent defect signal refers to a specific fault signal output when a dent defect is detected in the negative terminal cap of a dry cell battery. A signal transmission device installed on the pressure sensing module searches for the corresponding electrical signal from a preset signal library based on the defect characteristics and sends it. All subsequent outputs are implemented using this method. The signal library is constructed by the experimenter through a one-to-one correspondence between defect characteristics and signals.

[0046] When the pressure difference is less than 0, it indicates that there is a pit defect in the negative terminal cover of the dry cell, and a pit defect signal is output.

[0047] Step 71: When the pressure difference is greater than 0, output the preset protrusion defect signal.

[0048] The bulge defect signal refers to a specific fault signal output when the negative terminal cap of a dry cell battery is determined to have a bulge defect.

[0049] When the pressure difference is greater than 0, it indicates that there is a bulging defect in the negative electrode cover of the dry cell, and a bulging defect signal is output.

[0050] Step 72: When the abnormal area does not exist, output the preset qualified signal.

[0051] A pass signal refers to the status signal output when the negative terminal cover of a dry cell battery is determined to be defect-free and meets the testing standards.

[0052] When the abnormal area is not present, it indicates that the negative terminal cover of the dry cell battery is free of defects, and a qualified signal is output.

[0053] This also includes: Step 40: Move the pressure sensing module corresponding to the pressure sensing module number along the moving direction until a real-time pressure distribution image is generated. The actual pressure at each pixel in the real-time pressure distribution image is greater than 0.

[0054] A real-time pressure distribution image refers to a pressure distribution image generated by the pressure sensing module as it moves, acquiring pressure data in real time. The electric slide moves the pressure sensing module along the direction of movement, while the module simultaneously acquires pressure data and generates a pressure distribution image. This process continues until the pressure value of all pixels in the image is greater than 0. At this point, the pressure distribution image for that frame is saved and considered a real-time pressure distribution image.

[0055] Step 400: When the real-time pressure distribution image is completely consistent with the standard pressure distribution image, obtain the movement position of the pressure sensing module.

[0056] The movement position refers to the spatial position of the pressure sensing module when acquiring the real-time pressure distribution image. The spatial coordinates of the pressure sensing module are locked during the generation of the real-time pressure distribution image using the upper position detection module of the electric slide. These spatial coordinates are considered the movement position. When the real-time pressure distribution image is completely consistent with the standard pressure distribution image, it indicates that the negative electrode cap of the dry cell battery is not defective. It is then necessary to determine whether the battery height meets the standard and obtain the movement position of the pressure sensing module.

[0057] Step 4000: When the moving position is not the standard moving endpoint, the moving difference is obtained by analyzing the moving position and the standard moving endpoint.

[0058] The movement difference refers to the distance difference between the moving position of the pressure sensing module and the standard moving endpoint in the direction of movement. Movement difference = |Standard moving endpoint Z-axis coordinate value - Moving position Z-axis coordinate value|.

[0059] When the moving position is not the standard moving endpoint, it means that the battery height of the dry cell is not the standard battery height. The difference needs to be calculated to determine whether the dry cell is qualified. The moving difference is obtained by analyzing the moving position and the standard moving endpoint.

[0060] Step 40000: When the movement difference is within the preset qualified threshold range, output a qualified signal.

[0061] The acceptable threshold range refers to the benchmark for determining whether the height of a dry cell battery is acceptable. Experimenters set upper and lower limits for the movement difference based on the dimensional tolerances and testing accuracy of different battery models and input these limits into the system to obtain the acceptable threshold range.

[0062] When the movement difference is within the acceptable threshold range, it indicates that the battery height of the dry cell battery meets the size standard, and an acceptable signal is output.

[0063] Step 40001: When the movement difference is not within the acceptable threshold range, output a preset unacceptable signal.

[0064] The non-conforming signal refers to the general fault signal output when the negative terminal cover of the dry cell is determined to have dimensional defects (such as height exceeding tolerance) or flaws.

[0065] When the movement difference is outside the acceptable threshold range, it indicates that the battery height of the dry cell does not meet the size standard, and an unacceptable signal is output.

[0066] Step 401: When the real-time pressure distribution image is not completely consistent with the standard pressure distribution image, compare the real-time pressure distribution image with the standard pressure distribution image to obtain the real-time pressure difference.

[0067] Real-time pressure difference refers to the difference between the actual pressure value of each pixel in the real-time pressure distribution image and the standard pressure value of the corresponding pixel in the standard pressure distribution image. Real-time pressure difference = Pressure of pixel in real-time pressure distribution image - Pressure of pixel in corresponding standard pressure distribution image.

[0068] When the real-time pressure distribution image is not completely consistent with the standard pressure distribution image, it indicates that there is a defect in the negative electrode cap of the dry cell battery, and it is necessary to calculate the real-time pressure difference to determine the type of defect.

[0069] Step 4010: When the real-time pressure difference is less than 0, output the pit defect signal.

[0070] When the real-time pressure difference is less than 0, it indicates that there is a pit defect in the negative terminal cover of the dry cell, and a pit defect signal is output.

[0071] Step 4011: When the real-time pressure difference is greater than 0, output a bulge defect signal.

[0072] When the real-time pressure difference is greater than 0, it indicates that there is a bulge defect in the negative terminal cover of the dry cell battery, and a bulge defect signal is output.

[0073] This also includes: Step 41: Obtain the real-time pressure of all pressure sensing units as the device moves along the direction of movement.

[0074] Real-time pressure refers to the instantaneous pressure value collected by the pressure sensing units in real time as the pressure sensing module moves along the direction of movement. During the movement, the pressure sensing module collects the pressure values ​​of all sensing units at a preset frequency (e.g., milliseconds) to obtain the real-time pressure.

[0075] Step 42: When the real-time pressure equals the preset damage threshold, stop the detection, control the pressure sensing module to move along the preset return direction to the preset starting point, and output a failure signal.

[0076] The damage threshold refers to the maximum pressure that the pressure sensing module can withstand. Researchers apply pressure to the pressure sensing module during experiments, and the pressure value at which the module breaks is taken as the damage threshold. The return direction refers to the direction in which the pressure sensing module returns to its starting point. The starting point is the initial position of the pressure sensing module before it begins detection.

[0077] When the real-time pressure equals the preset damage threshold, it indicates that there is an abnormal bulge in the negative terminal cover of the dry cell battery. Continuing to detect will damage the pressure sensing module. Therefore, the detection is stopped, the pressure sensing module is controlled to move to the starting point, and an unqualified signal is output.

[0078] This also includes: Step 73: When there is an abnormal area, obtain the historical pressure distribution image of the pressure sensing module corresponding to the pressure sensing module number during the movement along the moving direction.

[0079] Historical pressure distribution images refer to pressure distribution images acquired at a certain frequency as the pressure sensing module moves along the direction of movement. During the movement of the pressure sensing module, pressure distribution images are generated and stored at preset acquisition intervals (e.g., one frame is acquired every 0.1 mm of movement), forming historical pressure distribution images.

[0080] When abnormal areas exist, it is necessary to determine the type of defect based on the changes in the abnormal areas and obtain historical pressure distribution images.

[0081] Step 74: Analyze the historical pressure distribution image to obtain the historical pressure region.

[0082] The historical pressure region is the abnormal region corresponding to each frame of the pressure distribution image generated before the pressure sensing module moves to the standard endpoint. Each frame of the historical pressure distribution image is compared with the standard pressure distribution image. The pressure difference of each pixel is compared, and the pixels with inconsistent pressure are connected by 8-neighborhood to obtain the historical pressure region.

[0083] Step 75: Obtain the trend of regional range change based on the historical pressure area. The trend of regional range change includes the trend of unchanged regional range and the trend of regional range expansion.

[0084] The trend of region range change refers to the pattern of change in the area, outline, and other range characteristics of the historical pressure region as the pressure sensing module moves. The pixel area of ​​the historical pressure region in each frame is calculated, and the pixel areas are compared in chronological order. When the pixel area remains unchanged, the region range change trend is considered constant. When the pixel area changes by more than a preset number of pixels, the region range change trend is considered expanding. The number of pixels is input by the experimenter. A constant region range trend means that the area of ​​the historical pressure region does not change significantly during the movement of the pressure sensing module. An expanding region range trend means that the area of ​​the historical pressure region shows a continuous increase during the movement of the pressure sensing module.

[0085] Step 750: When the trend of the area range change is that the area range is expanding, complete the current detection and issue a preset detection completion signal.

[0086] The detection completion signal refers to the status signal output when the current dry cell negative electrode cover detection process is completed.

[0087] When the area range changes in a trend of expansion, it indicates the presence of oil contamination. The oil contamination on the pressure sensing module needs to be cleaned. After completing the current detection, a detection completion signal is output.

[0088] Step 751: After receiving the detection completion signal, move the pressure sensing module to the preset cleaning position to execute the preset cleaning task and receive the cleaning completion signal fed back by the pressure sensing module.

[0089] The cleaning position refers to the fixed location where the pressure sensing module performs its cleaning task. The spatial coordinates of the cleaning position are calibrated, and the pressure sensing module is moved to that location. The cleaning task refers to the automated cleaning process for oil stains on the surface of the pressure sensing module. Researchers design and input cleaning tasks into the system based on different types of oil stains and their location and area on the pressure sensing module. The cleaning completion signal is the status feedback signal sent by the pressure sensing module after it has completed oil stain cleaning. A stroke sensor is installed on the pressure sensing module; when the cleaning task is completed according to the program, the sensor triggers and generates and outputs an electrical signal, completing the feedback.

[0090] Once the detection completion signal is received, it indicates that the current detection has been completed and the subsequent steps can be executed. The pressure sensing module is moved to the cleaning position to perform the cleaning task and the cleaning completion signal is received from the pressure sensing module.

[0091] Step 752: When the cleaning completion signal is received, control the pressure sensing module to move to the starting point and output the preset oil stain defect signal.

[0092] Oil contamination defect signal refers to a specific fault signal output when it is determined that an abnormal area in the pressure distribution image is caused by oil contamination defect.

[0093] When a cleaning completion signal is received, it indicates that the cleaning is complete. The control pressure sensor module moves to the starting point to execute subsequent detection steps and outputs an oil stain defect signal.

[0094] Step 753: When the trend of the area range change is the same as the trend of the area range remaining unchanged, output the pit defect signal or the bulge defect signal.

[0095] When the trend of the area range change is the same as the trend of the area range remaining unchanged, it indicates that the abnormal area of ​​the pressure distribution image is caused by pit defects or raised defects, and the pit defect signal or raised defect signal is output.

[0096] This also includes: Step 7510: After the pressure sensing module moves to the cleaning position, the abnormal area is obtained by analyzing the abnormal area.

[0097] The abnormal area refers to the actual area of ​​the abnormal region. The actual area of ​​the abnormal region is calculated by counting the total number of pixels within the abnormal region and using a pixel-to-actual-area conversion ratio (e.g., 1 pixel corresponds to 0.01 mm²). Researchers constructed this conversion ratio based on the area of ​​a single pixel in the pressure sensing module and input it into the system.

[0098] Once the pressure sensing module moves to the cleaning location, it is necessary to find the corresponding cleaning task based on the specific situation of the oil stains and calculate the abnormal area.

[0099] Step 7511: Based on the pressure difference obtained by comparing the pressure distribution image with the standard pressure distribution image, search for the corresponding oil viscosity value in the preset viscosity database.

[0100] The viscosity database is a database linking pressure difference to oil viscosity values. Researchers conduct pressure experiments on oils of different viscosities to obtain the pressure differences caused by different viscosities of oil to a standard pressure value under the same pressure. These differences are then input into the system to create the viscosity database. Inputting the pressure difference into the viscosity database yields the corresponding oil viscosity value. The oil viscosity value refers to the viscosity of the oil adhering to the negative terminal cap of a dry cell battery.

[0101] Step 7512: Perform path analysis based on the abnormal area to obtain the corresponding wiping path.

[0102] The wiping path refers to the movement path of the wiping mechanism of the cleaning device obtained by analyzing the abnormal area. The location of the oil stains on the pressure sensing module is obtained by mapping the area and position of the abnormal area. Based on the location of the oil stains, a reciprocating path that can cover the oil stains from left to right and from top to bottom is determined, and this reciprocating path is set as the wiping path.

[0103] Step 75120: If the viscosity value of the oil stain is greater than the preset viscosity threshold, control the preset cleaning device to spray the special solvent onto the surface of the pressure sensing module, and receive the spraying completion signal fed back by the pressure sensing module.

[0104] Viscosity threshold refers to the benchmark for distinguishing between high and low viscosity oil stains and selecting different cleaning methods based on the oil stain viscosity value. Researchers conduct wiping experiments on oil stains of different viscosities to determine the viscosity value of the oil stain that requires a specific solvent to remove, and use this viscosity value as the viscosity threshold. The cleaning device refers to the automated equipment used to clean oil stains from the surface of the pressure sensor module. The specific solvent refers to a cleaning solvent (such as isopropanol or anhydrous ethanol) specifically selected for the type of oil stain on the pressure sensor module surface. The spraying completion signal is the status feedback signal sent by the cleaning device after completing the specific solvent spraying action. The pressure sensor module surface refers to the part of the pressure sensor module that contacts the negative terminal cover of the dry cell battery.

[0105] If the viscosity of the oil stain is greater than the preset viscosity threshold, it means that a special solvent is needed to remove the oil stain. Control the cleaning device to spray the special solvent onto the surface of the pressure sensing module and receive the spraying completion signal from the pressure sensing module.

[0106] Step 75121: After receiving the spraying completion signal, stop spraying the special solvent, control the cleaning device to wipe the surface of the pressure sensing module along the wiping path and receive the cleaning completion signal fed back by the pressure sensing module.

[0107] Step 75122: If the viscosity value of the oil stain is less than the viscosity threshold and the abnormal area is greater than the preset wiping area threshold, turn on the preset blowing device, control the cleaning device to wipe the surface of the pressure sensing module along the wiping path and receive the cleaning completion signal fed back by the pressure sensing module.

[0108] The wiping area threshold refers to the critical value of abnormal area, serving as a benchmark for determining whether wiping is necessary. Researchers conduct blowing experiments on oil stains of different areas to determine the area of ​​oil stain that cannot be removed by standard blowing intensity per unit time, and set this area as the wiping area threshold. A blowing device is a device that uses high-pressure gas (such as nitrogen or compressed air) to blow away low-viscosity, small-area oil stains from the surface of a pressure sensing module.

[0109] If the viscosity value of the oil stain is less than the viscosity threshold and the abnormal area is greater than the preset wiping area threshold, it means that the blower cannot remove the oil stain and that the oil stain can be removed without special solvent. Turn on the blower and control the cleaning device to wipe the surface of the pressure sensing module along the wiping path.

[0110] Step 75123: After receiving the cleaning completion signal, turn off the blower.

[0111] Once the cleaning completion signal is received, it indicates that the cleaning is complete, and the blower should be turned off.

[0112] Step 75124: If the pressure difference is less than the viscosity threshold and the abnormal area is less than the wiping area threshold, turn on the blower and receive the cleaning completion signal from the pressure sensing module.

[0113] If the pressure difference is less than the viscosity threshold and the abnormal area is less than the wiping area threshold, it means that the blower can remove the oil stains without the need for special solvents. Turn on the blower.

[0114] Step 75125: After receiving the cleaning completion signal, turn off the blower.

[0115] Once the cleaning completion signal is received, it indicates that the cleaning is complete, and the blower should be turned off.

[0116] This also includes: Step 8: Obtain the coordinates of the pixels in the abnormal region and define these coordinates as abnormal coordinates.

[0117] Anomaly coordinates refer to the planar coordinates (x, y) of each pixel within an abnormal region in a pressure distribution image. The center point of the pressure sensing module is set to coordinates (0, 0), and the X-axis and Y-axis coordinates of the pixels within the abnormal region are extracted to obtain the planar coordinates (x, y).

[0118] Step 9: When all abnormal coordinates are within the preset edge region, perform region connectivity processing on the abnormal pixels to obtain abnormal connected regions.

[0119] Edge regions refer to the set of pixels in the pressure distribution image corresponding to the edge position of the negative electrode cap of a dry cell battery. The edge positions of the negative electrode caps of different dry cell battery models were defined by the experimenters and input into the system to obtain the edge regions. Anomalous connected regions refer to regions consisting of a continuous set of anomalous pixels obtained after region connectivity processing. An 8-neighborhood connectivity algorithm was used to connect anomalous pixels, grouping adjacent anomalous pixels into the same region to obtain anomalous connected regions.

[0120] When all abnormal coordinates are within the preset edge region, it indicates that the negative electrode cover of the dry cell may have warping defects. It is necessary to determine whether the warping defect is an upper warping defect or a lower warping defect by the pressure difference of the abnormal region, and to perform region connectivity processing on the abnormal pixels to obtain the abnormal connected region.

[0121] Step 10: Analyze the abnormal connected regions to obtain location features.

[0122] Location features refer to the spatial location attributes of anomalous connected regions in a pressure distribution image. The location of the anomalous connected region within the edge region is determined (e.g., at the center of the edge region, or occupying half the area of ​​the edge region), and the corresponding location features are searched in a pre-defined location feature database. Researchers input the location information of the anomalous connected regions within the edge region and their corresponding location features into the system to obtain the location feature database.

[0123] Step 11: When the location feature is the preset outermost feature, the pressure distribution image is obtained and compared with the standard pressure distribution image to obtain the pressure difference.

[0124] The outermost edge feature refers to the positional feature of the pixel corresponding to the outer contour of the negative electrode cap of the dry cell battery in the abnormal connected region. The method for obtaining this feature was described in step 7.

[0125] When the position feature is the preset outermost feature, it indicates the presence of warping defects. In order to determine the specific type of warping defects, the pressure distribution image is obtained and compared with the standard pressure distribution image to obtain the pressure difference.

[0126] Step 110: When the pressure difference is greater than 0, output the preset upward warping defect signal.

[0127] The upward warping defect signal refers to a specific fault signal output when the negative terminal cap of a dry cell battery is determined to have an upward warping defect.

[0128] When the pressure difference is greater than 0, it indicates the presence of an upward warping defect, and an upward warping defect signal is output.

[0129] Step 111: When the pressure difference is less than 0, output the preset downward warping defect signal.

[0130] The under-warping defect signal refers to a specific fault signal output when the negative terminal cap of a dry cell battery is determined to have an under-warping defect.

[0131] When the pressure difference is less than 0, it indicates the presence of a downward warping defect, and a downward warping defect signal is output.

[0132] This also includes: Step 12: Compare the actual pressure on each pixel to obtain the highest pressure.

[0133] The maximum pressure refers to the largest actual pressure value among all pixels in the pressure distribution image. By comparing the actual pressure values ​​of all pixels, the actual pressure with the largest value is extracted as the maximum pressure.

[0134] Step 13: Find the pixel corresponding to the highest pressure and define it as the starting pixel.

[0135] The starting pixel is the point at which pressure gradient analysis begins. The starting pixel is obtained by retrieving the corresponding pixel from the pressure distribution image based on the highest pressure value.

[0136] Step 14: Perform a surrounding lookup based on the starting pixel to obtain the surrounding pixels.

[0137] Peripheral pixels refer to pixels obtained by analyzing the starting pixel according to preset rules. Peripheral pixels are obtained by analyzing the starting pixel using an 8-neighborhood connectivity algorithm.

[0138] Step 15: Find the surrounding pressure based on the surrounding pixels.

[0139] Peripheral pressure refers to the actual pressure value corresponding to the surrounding pixels. Based on the surrounding pixels, the pressure value of the corresponding pixel in the pressure distribution image is retrieved and regarded as the peripheral pressure.

[0140] Step 16: Perform gradient analysis based on the surrounding pressure to obtain the pressure gradient value.

[0141] The pressure gradient value refers to the rate of change of pressure between the starting pixel and its surrounding pixels. Pressure gradient value = (highest pressure - average surrounding pressure) ÷ pixel spacing. The average surrounding pressure is calculated as the average of the pressure values ​​corresponding to all surrounding pixels. Pixel spacing refers to the actual distance between adjacent pixels.

[0142] Step 160: When the pressure gradient value is greater than the preset high gradient threshold, output the preset foreign object defect signal.

[0143] The high gradient threshold refers to the benchmark used to distinguish between raised defects and foreign object defects based on the pressure gradient value. Researchers conducted pressure tests on dry cell negative electrode caps with both foreign object defects and raised defects to identify abnormal areas. The pressure difference between adjacent pixels in these abnormal areas was calculated, and the average pressure difference between the foreign object defects and raised defects was calculated and used as the high gradient threshold. The foreign object defect signal refers to a specific fault signal output when a raised area is identified as being caused by foreign matter (such as metal shavings or dust particles) adhering to the surface of the dry cell negative electrode cap.

[0144] When the pressure gradient value is greater than the preset high gradient threshold, it indicates that the abnormal area is caused by foreign matter attached to the surface of the dry cell negative electrode cover, and a foreign matter defect signal is output.

[0145] Step 161: When the pressure gradient value is less than the high gradient threshold, output the protrusion defect signal.

[0146] When the pressure gradient value is less than the high gradient threshold, it indicates that the abnormal area is caused by a raised defect on the surface of the negative electrode cover of the dry cell, and a raised defect signal is output.

[0147] This also includes: Step 710: Obtain the bulge area based on the abnormal area analysis.

[0148] The raised area refers to the actual physical area of ​​the abnormal region corresponding to the raised defect. This analysis was introduced in step 7510.

[0149] Step 711: Locate the functional area of ​​the dry cell negative terminal cover corresponding to the dry cell negative terminal cover model.

[0150] A functional area refers to the region on the negative terminal cover of a dry cell battery that performs core functions (such as conduction, sealing, and current collection). By inputting the battery negative terminal cover model into the functional area database, the corresponding functional areas can be retrieved. Researchers categorized the functions of different dry cell battery negative terminal covers during use (e.g., those that transmit current, those that do not transmit pressure) and entered these categories into the system to construct the functional area database.

[0151] Step 712: Determine the location of the protrusion based on the abnormal area and pressure distribution image analysis.

[0152] The protrusion location refers to the position of the abnormal area corresponding to the protruding defect on the negative terminal cover of the dry cell battery. The pixel coordinates within the abnormal area are converted into the actual physical coordinates of the defect on the negative terminal cover of the dry cell battery through a pixel coordinate-physical coordinate mapping relationship, thus obtaining the protrusion location. Researchers input the pixel coordinates of the pressure distribution image and the coordinates of the negative terminal cover of the dry cell battery into the system in a one-to-one correspondence to construct the pixel coordinate-physical coordinate mapping relationship.

[0153] Step 7120: When the following conditions are met simultaneously: the pressure gradient value is less than the preset qualified gradient threshold, the protrusion area is less than the preset qualified area threshold, and the protrusion position is not within the functional area, a qualified signal is output.

[0154] The acceptable gradient threshold refers to the benchmark for determining whether a slight bulge is an acceptable defect based on the pressure gradient value. Researchers test the impact of bulges with different pressure gradient values ​​on battery use, and the pressure gradient value of a bulge that just does not affect battery use is taken as the acceptable gradient threshold. The acceptable area threshold refers to the benchmark for determining whether a bulge is an acceptable defect based on its area. Researchers test the impact of bulges of different areas on battery use, and the area of ​​a bulge that just does not affect battery use is taken as the acceptable area threshold.

[0155] When the following conditions are met simultaneously: the pressure gradient value is less than the preset qualified gradient threshold, the protrusion area is less than the preset qualified area threshold, and the protrusion position is not within the functional area, it indicates that the protrusion defect is within the acceptable range, and a qualified signal is output.

[0156] Step 7121: When the following conditions are not met simultaneously: the pressure gradient value is less than the preset qualified gradient threshold, the protrusion area is less than the preset qualified area threshold, and the protrusion position is not within the functional area, an unqualified signal is output.

[0157] If the following conditions are not met simultaneously: the pressure gradient value is less than the preset acceptable gradient threshold, the protrusion area is less than the preset acceptable area threshold, and the protrusion position is not within the functional area, it indicates that the protrusion defect is not within the acceptable range, and an unacceptable signal is output.

[0158] This also includes: Step 17: Obtain historical abnormal area data.

[0159] Historical anomaly area data refers to relevant data for all anomaly areas during continuous detection, including location, area, pressure difference, and occurrence time. This data is retrieved from the historical anomaly area database. All anomaly areas generated during the detection process are stored to form the historical anomaly area database.

[0160] Step 18: Perform location analysis based on historical anomaly data to obtain overlapping anomaly regions.

[0161] Overlapping abnormal regions refer to areas formed by connecting consecutively abnormal pixels during the detection process. In regions where consecutive detections show abnormalities, the same pixel generating the same pressure difference is extracted, and these matching pixels are connected through an 8-neighborhood connection to obtain the overlapping abnormal regions.

[0162] Step 19: Find the occurrence count of the overlapping abnormal region in the preset abnormality database based on the overlapping abnormal region.

[0163] An anomaly count database stores the number of times overlapping anomaly regions occur. When a new overlapping anomaly region appears, it is recorded as 1 and input into the system. Subsequent occurrences of the same overlapping anomaly region increment the count, and the database is compiled by aggregating these overlapping anomaly regions. The occurrence count refers to the total number of times an overlapping anomaly region appears during continuous detection. The occurrence count is obtained by inputting the overlapping anomaly region into the anomaly count database.

[0164] Step 190: When the number of occurrences exceeds the preset suspected fault threshold, a preset self-test current is output to the pressure sensing module to obtain a self-test pressure distribution image.

[0165] The suspected fault threshold is a benchmark for judging whether a pressure sensing module is faulty based on the number of occurrences. Experimenters conduct pressure experiments based on the stability and failure probability of the pressure sensing module, setting a specific number of occurrences (e.g., 10 consecutive occurrences) as the suspected fault threshold. The self-test current refers to the standard current (e.g., 166mA) output by the pressure sensing module for self-testing. The parameters of the self-test current are pre-input into the system by the experimenters. The self-test pressure distribution image refers to the pressure distribution image generated by the pressure sensing module under the action of the self-test current, without contact with any object being detected. Under the action of the self-test current, the pressure sensing module collects non-contact pressure data from all sensing units to generate a pressure distribution image, which serves as the self-test pressure distribution image.

[0166] When the number of occurrences exceeds the preset suspected fault threshold, it indicates that the pressure sensing module may be malfunctioning and needs to perform a self-check. A preset self-check current is output to the pressure sensing module to obtain a self-check pressure distribution image.

[0167] Step 191: When the self-test pressure distribution image has preset fault characteristics, stop the detection and send a preset fault signal.

[0168] Fault characteristics refer to the specific abnormal features exhibited in the self-test pressure distribution image when a pressure sensing module malfunctions. Researchers calibrate the self-test pressure distribution image features corresponding to various faults in the pressure sensing module (such as sensor unit failure or damage to the detection surface) as fault characteristics. Fault signals refer to the specific alarm signals output by the detection system when it determines that the pressure sensing module is faulty.

[0169] When the self-test pressure distribution image has preset fault characteristics, it indicates that the pressure sensing module has malfunctioned and cannot guarantee the accuracy of detection. The detection will then stop and a preset fault signal will be sent.

[0170] Based on the same inventive concept, embodiments of the present invention provide a dry cell negative electrode cap defect detection system based on image processing.

[0171] A dry cell battery negative electrode cap defect detection system based on image processing includes: The acquisition module is used to acquire data on the model of the negative terminal cover of the dry cell battery, the pressure it can withstand, the real-time pressure, the abnormal coordinates, and the historical abnormal area data. A memory for storing a program for a control method of a dry cell negative electrode cap defect detection method based on image processing; The processor loads and executes programs from memory.

[0172] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0173] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for detecting defects in the negative electrode cap of a dry cell battery based on image processing, characterized in that, include: In response to the detection signal, obtain the model of the dry cell negative terminal cap; Find the corresponding pressure sensor module number and the corresponding standard moving endpoint based on the dry cell negative terminal cover model; The pressure sensing module corresponding to the control pressure sensing module number is installed on the electric slide and receives the installation completion signal fed back by the pressure sensing module. When the installation completion signal is received, the pressure sensing module corresponding to the pressure sensing module number is moved to the standard moving end point along the preset moving direction, and the pressure that all pressure sensing units in the pressure sensing module can withstand is obtained. The actual pressure on each pixel is obtained based on the pressure it bears. The pressure distribution image is obtained based on the actual pressure. The abnormal areas and corresponding pressure differences are obtained by comparing the pressure distribution image with the preset standard pressure distribution image. When the pressure difference is less than 0, a preset pit defect signal is output. When the pressure difference is greater than 0, a preset protrusion defect signal is output; When the abnormal region does not exist, a preset qualified signal is output.

2. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 1, characterized in that, It also includes a method for outputting a qualified signal even when the pressure difference is greater than 0 or the pressure distribution image is not available. This method includes: The pressure sensing module corresponding to the pressure sensing module number is moved along the moving direction until a real-time pressure distribution image is generated. The actual pressure at each pixel in the real-time pressure distribution image is greater than 0. When the real-time pressure distribution image is completely consistent with the standard pressure distribution image, the movement position of the pressure sensing module is obtained; When the moving location is not the standard moving endpoint, the moving difference is obtained by analyzing the moving location and the standard moving endpoint. When the movement difference is within the preset acceptable threshold range, an acceptable signal is output; When the movement difference is outside the acceptable threshold range, a preset unacceptable signal is output; When the real-time pressure distribution image is not completely consistent with the standard pressure distribution image, the real-time pressure difference is obtained by comparing the real-time pressure distribution image with the standard pressure distribution image. When the real-time pressure difference is less than 0, output a pit defect signal; When the real-time pressure difference is greater than 0, a bulge defect signal is output.

3. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 2, characterized in that, It also includes a method for outputting an unqualified signal when the real-time pressure is too high, the method comprising: Acquire the real-time pressure of all pressure sensing units as the device moves along the direction of movement; When the real-time pressure equals the preset damage threshold, the detection stops, the pressure sensing module is controlled to move along the preset return direction to the preset starting point, and an unqualified signal is output.

4. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 1, characterized in that, It also includes a verification method for outputting raised defect signals or pitted defect signals, the method comprising: When an abnormal area exists, obtain the historical pressure distribution image of the pressure sensing module corresponding to the pressure sensing module number during the movement along the moving direction; Historical pressure regions were identified based on historical pressure distribution images. The trend of regional range change is obtained based on the historical pressure area, which includes the trend of unchanged regional range and the trend of regional range expansion. When the trend of the area range change is that the area range is expanding, the current detection is completed and a preset detection completion signal is issued; Upon receiving the detection completion signal, the pressure sensing module is moved to the preset cleaning position to perform the preset cleaning task and receive the cleaning completion signal from the pressure sensing module. When a cleaning completion signal is received, the control pressure sensor module moves to the starting point and outputs a preset oil stain defect signal. When the trend of the area range change is the same as the trend of the area range remaining unchanged, output a pit defect signal or a raised defect signal.

5. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 4, characterized in that, The method for moving the pressure sensing module to the cleaning position to perform the cleaning task and receiving the cleaning completion signal from the pressure sensing module after receiving the detection completion signal includes: Once the pressure sensing module moves to the cleaning position, the abnormal area is obtained by analyzing the abnormal region. The pressure difference obtained by comparing the pressure distribution image with the standard pressure distribution image is used to search for the corresponding oil viscosity value in the preset viscosity database. The corresponding wiping path is obtained by performing path analysis based on the abnormal area; If the viscosity of the oil stain is greater than the preset viscosity threshold, the preset cleaning device is controlled to spray a special solvent onto the surface of the pressure sensing module, and the spraying completion signal is received from the pressure sensing module. After receiving the spraying completion signal, stop spraying the special solvent, control the cleaning device to wipe the surface of the pressure sensing module along the wiping path and receive the cleaning completion signal fed back by the pressure sensing module; If the viscosity value of the oil stain is less than the viscosity threshold and the abnormal area is greater than the preset wiping area threshold, the preset blowing device is turned on, and the cleaning device is controlled to wipe the surface of the pressure sensing module along the wiping path and receive the cleaning completion signal fed back by the pressure sensing module. After receiving the cleaning completion signal, turn off the blower. If the pressure difference is less than the viscosity threshold and the abnormal area is less than the wiping area threshold, the blowing device is turned on and the cleaning completion signal is received from the pressure sensing module. After receiving the cleaning completion signal, turn off the blower.

6. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 1, characterized in that, The raised defect signal includes an upward warping defect signal, and the pitted defect signal includes a downward warping defect signal. The document also includes a method for determining the upward and downward warping defect signals, which includes: Obtain the coordinates of pixels in the abnormal region and define these coordinates as abnormal coordinates; When all abnormal coordinates are within the preset edge region, the abnormal pixels are processed to obtain the abnormal connected region. Location features are obtained by analyzing abnormal connected regions; When the location feature is the preset outermost feature, the pressure distribution image is compared with the standard pressure distribution image to obtain the pressure difference; When the pressure difference is greater than 0, a preset upward warping defect signal is output. When the pressure difference is less than 0, a preset downward warping defect signal is output.

7. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 6, characterized in that, It also includes a method for outputting a protrusion defect signal when the pressure difference is greater than 0, the method comprising: The highest pressure is obtained by comparing the actual pressure at each pixel. Find the pixel corresponding to the highest pressure based on the highest pressure, and define that pixel as the starting pixel. The surrounding pixels are obtained by performing a surrounding lookup based on the starting pixel. The surrounding pressure is determined by finding the surrounding pixels; The pressure gradient value is obtained by gradient analysis based on the surrounding pressure. When the pressure gradient value is greater than the preset high gradient threshold, a preset foreign object defect signal is output. When the pressure gradient value is less than the high gradient threshold, a protrusion defect signal is output.

8. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 1, characterized in that, It also includes a method for outputting a qualified signal when the pressure difference is greater than 0, the method including: The area of ​​the protrusion is obtained from the analysis of the abnormal area; Find the functional area of ​​the dry cell negative terminal cover that corresponds to the dry cell negative terminal cover model. The location of the protrusion was determined by analyzing the abnormal area and pressure distribution images. When the following conditions are met simultaneously: the pressure gradient value is less than the preset qualified gradient threshold, the protrusion area is less than the preset qualified area threshold, and the protrusion position is not within the functional area, a qualified signal is output. When the following conditions are not met simultaneously: the pressure gradient value is less than the preset qualified gradient threshold, the protrusion area is less than the preset qualified area threshold, and the protrusion position is not within the functional area, an unqualified signal is output.

9. The method for detecting defects in the negative electrode cap of a dry cell battery based on image processing according to claim 1, characterized in that, Also includes: Obtain historical data on abnormal areas; Overlapping anomaly regions are identified through location analysis based on historical anomaly region data. The number of times the overlapping anomaly region appears is obtained by searching the preset anomaly count database. When the number of occurrences exceeds the preset suspected fault threshold, a preset self-test current is output to the pressure sensing module to obtain a self-test pressure distribution image; When the self-test pressure distribution image has preset fault characteristics, the detection stops and a preset fault signal is sent.

10. A dry cell battery negative electrode cap defect detection system based on image processing, characterized in that, include: The acquisition module is used to acquire data on the model of the negative terminal cover of the dry cell battery, the pressure it can withstand, the real-time pressure, the abnormal coordinates, and the historical abnormal area data. A memory for storing the program of the image processing-based dry cell negative electrode cap defect detection method as described in any one of claims 1 to 9; The processor loads and executes programs from memory.

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