Inner container detection method, device, system and equipment, storage medium and program product

Abstract

The embodiment of the invention provides an inner container detection method, device, system and equipment, a storage medium and a program product. The method comprises the following steps: acquiring a liner size parameter and a liner image of to-be-detected equipment; comparing at least part of the region in the liner image with a pre-stored standard image to obtain a flaw evaluation index corresponding to the region to be identified; comparing the inner container size parameter with a pre-stored standard size parameter to obtain a size evaluation index of the to-be-detected equipment; and determining a liner detection result of the to-be-detected equipment based on the flaw evaluation index and the size evaluation index. According to the method, the detection efficiency can be improved, and the accuracy of liner detection is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent detection technology, and in particular to an inner liner detection method, device, system, equipment, storage medium and program product. Background Art

[0002] With the rapid development of modern kitchen appliance technology, multifunctional cooking equipment is increasingly favored by consumers. As one of the core components of such products, the quality of the inner pot of multifunctional cooking equipment is directly related to food safety, cooking efficiency and the service life of the equipment.

[0003] At present, the inner pot of steam ovens is mostly made of high temperature resistant and easy to clean materials such as stainless steel or enamel. During the production process, various defects may appear in the inner pot, such as scratches, dents, bubbles, coating shedding, etc. These defects not only affect the appearance of the product, but more importantly, may cause food safety problems and functional failure. Therefore, it is particularly important to conduct effective defect detection on the inner pot of steam ovens.

[0004] The traditional method of detecting liner defects mainly relies on manual visual inspection, which is highly subjective, inefficient and prone to missing minor defects. Although the automated detection method of liner defects using machine vision technology is becoming more and more popular, there is still the problem of low defect recognition accuracy. Summary of the invention

[0005] The embodiments of the present application provide a liner detection method, device, system, equipment, storage medium and program product to achieve the effect of improving the accuracy of liner defect identification.

[0006] In a first aspect, an embodiment of the present application provides a liner detection method, comprising:

[0007] Obtain the inner liner size parameters and inner liner image of the device to be tested;

[0008] Compare at least a portion of the inner liner image with a pre-stored standard image to obtain a defect evaluation index corresponding to the area to be identified; compare the inner liner size parameter with a pre-stored standard size parameter to obtain a size evaluation index of the device to be detected;

[0009] Based on the defect evaluation index and the size evaluation index, an inner liner inspection result of the device to be inspected is determined.

[0010] In a possible implementation manner, before obtaining the inner liner size parameters and the inner liner image of the device to be detected, the method further includes:

[0011] A sensing device disposed at a first preset position is used to obtain position information of the device to be detected; the device to be detected is disposed on a conveying device and is moved by the conveying device;

[0012] When the posture information meets the first preset requirement, a stop instruction is generated and sent to the conveying device to control the conveying device to stop moving the device to be detected.

[0013] In a possible implementation manner, obtaining the inner liner size parameters and the inner liner image of the device to be detected includes:

[0014] Move the laser scanning device set at the initial position to a second preset position, and control the laser scanning device to scan the device to be detected to obtain the liner size parameters;

[0015] An image acquisition device arranged at a third preset position is used to acquire an image of the device to be detected to obtain the liner image.

[0016] In a possible implementation, the laser scanning device includes a mechanical arm and a laser emitter disposed on the mechanical arm;

[0017] The step of moving the laser scanning device set at the initial position to the second preset position and controlling the laser scanning device to scan the device to be detected to obtain the inner liner size parameters includes:

[0018] Generate a movement instruction and send it to the robotic arm to control the robotic arm to move the laser transmitter from the initial position to the second preset position according to a preset degree of freedom change process;

[0019] A scanning instruction is generated and sent to the laser transmitter to control the laser transmitter to perform laser measurement on the inner liner of the device to be detected to obtain the size parameters of the inner liner.

[0020] In a possible implementation, when the posture information meets the first preset requirement, before generating a stop instruction and sending it to the transmission device to control the transmission device to stop moving the device to be detected, the method further includes:

[0021] When the posture information meets the second preset requirement, the first size information is acquired using a laser transmitter disposed at a fourth preset position;

[0022] The step of obtaining the inner liner size parameters and the inner liner image of the device to be detected includes:

[0023] The second size information is acquired by using a laser transmitter disposed at the fourth preset position; and the image acquisition device disposed at the fifth preset position is acquired by using an image acquisition device;

[0024] The inner liner size parameter is determined based on the first size information and the second size information.

[0025] In a possible implementation manner, comparing at least a portion of the liner image with a pre-stored standard image to obtain a defect assessment index corresponding to the to-be-identified area includes:

[0026] Performing part recognition on the inner liner image to obtain a first region corresponding to a coating region in the inner liner image and a second region corresponding to an adhesive region in the inner liner image; the standard image corresponds to the coating region and the adhesive region, respectively;

[0027] Comparing the inner container image in the first area with the corresponding standard image, and calculating the coating defect evaluation index based on the pixel points where there are differences between the inner container image and the standard image;

[0028] Comparing the liner image in the second area with the corresponding standard image, and calculating the adhesive defect evaluation index based on the pixel points where there are differences between the liner image and the standard image;

[0029] The coating defect evaluation index and the adhesive defect evaluation index are combined to obtain the defect evaluation index.

[0030] In a possible implementation, determining the inner liner inspection result of the device to be inspected based on the defect assessment index and the size assessment index includes:

[0031] Based on a preset defect coefficient, the defect evaluation index and the size evaluation index are integrated to obtain an inner liner quality evaluation index;

[0032] Based on the preset standard quality index and the liner quality evaluation index, the liner detection result of the device to be detected is determined.

[0033] In a second aspect, an embodiment of the present application provides an inner liner detection device, including:

[0034] An acquisition module is used to acquire the inner liner size parameters and inner liner image of the device to be detected;

[0035] A comparison module, used to compare at least a part of the inner tank image with a pre-stored standard image to obtain a defect evaluation index corresponding to the area to be identified; compare the inner tank size parameter with a pre-stored standard size parameter to obtain a size evaluation index of the device to be detected;

[0036] A determination module is used to determine the inner liner detection result of the device to be detected based on the defect evaluation index and the size evaluation index.

[0037] In a third aspect, an embodiment of the present application provides a liner detection system, comprising the liner detection device in the second aspect and / or various possible embodiments of the second aspect, and a transmission device, a sensing device, a laser sensor, and an image acquisition device respectively connected to the liner detection device;

[0038] The transmission device is used to transmit the device to be detected;

[0039] The sensing device is used to obtain the position information of the device to be detected;

[0040] The laser sensor is used to obtain the inner container size parameters of the device to be detected;

[0041] The image acquisition device is used to obtain the inner tank image of the device to be detected.

[0042] In a fourth aspect, an embodiment of the present application provides a computer device, including: a memory, a processor;

[0043] The memory stores computer-executable instructions;

[0044] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0045] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementations of the first aspect.

[0046] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0047] The liner detection method, apparatus, system, equipment, storage medium and program product provided in the embodiments of the present application can, after acquiring the liner size parameters and liner image of the device to be detected, use numerical comparison and image recognition to perform automated quality inspection on the appearance and size of the liner, thereby improving the detection efficiency and also improving the accuracy of liner detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0049] Figure 1 A schematic diagram of a scenario of the liner detection method provided in this application;

[0050] Figure 2 Schematic diagram of the process of the inner liner detection method provided in this application Figure 1 ;

[0051] Figure 3 Schematic diagram of the process of the inner liner detection method provided in this application Figure 2 ;

[0052] Figure 4 Schematic diagram of the process of the inner liner detection method provided in this application Figure 3 ;

[0053] Figure 5 Schematic diagram of the process of the inner liner detection method provided in this application Figure 4 ;

[0054] Figure 6 A schematic diagram of the structure of the liner detection device provided in this application;

[0055] Figure 7 A schematic diagram of the structure of the liner detection system provided in this application;

[0056] Figure 8 A schematic diagram of the structure of the computer device provided for this application.

[0057] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0058] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0059] The liner detection method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown.

[0060] For example, the liner detection method is applied to the terminal 102, and the terminal 102 can obtain the liner size parameters and liner image of the device to be detected; and obtain the pre-stored standard image and standard size parameters from the data storage system of the server 104, and then compare at least part of the area in the liner image with the standard image to obtain the defect evaluation index corresponding to the area to be identified; compare the liner size parameters with the standard size parameters to obtain the size evaluation index of the device to be detected; finally, based on the defect evaluation index and the size evaluation index, determine the liner detection result of the device to be detected. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices, and the Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster consisting of multiple servers. The terminal 102 and the server 104 can be directly or indirectly connected by wired or wireless communication, such as through a network connection.

[0061] For another example, the liner detection method is applied to the server 104. After being triggered by the terminal 102, the server 104 can obtain the liner size parameters and liner image of the device to be detected; and obtain the pre-stored standard image and standard size parameters from the data storage system, and then compare at least part of the area in the liner image with the standard image to obtain the defect evaluation index corresponding to the area to be identified; compare the liner size parameters with the standard size parameters to obtain the size evaluation index of the device to be detected; finally, based on the defect evaluation index and the size evaluation index, determine the liner detection result of the device to be detected. It can be understood that the data storage system can be an independent storage device, or the data storage system is located on the server 104, or the data storage system is located on another terminal.

[0062] In one embodiment, a liner detection method is provided. This embodiment uses the liner detection method applied to a terminal as an example. It is understandable that the liner detection method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. Figure 2 As shown, the liner detection method includes:

[0063] Step 202: Obtain the inner liner size parameters and inner liner image of the device to be detected.

[0064] The device to be tested may refer to a multifunctional cooking device provided with an inner pot, for example, it may be a rice cooker, an electric oven, an electric steamer, a steam oven, or the like, which are provided with inner pots of different materials.

[0065] The inner liner size parameter is used to indicate the size of the inner liner of the device to be detected, for example, it may include parameters such as the length, width, and height of the inner liner of the device to be detected.

[0066] The liner image is used to display the actual status of the liner in the device to be tested. The liner image is usually used to intuitively display the actual status of the liner, such as shape, material, spraying condition, and adhesive condition.

[0067] As an example, the terminal may obtain the inner liner size parameters and the inner liner image of the device to be detected in response to the received detection instruction.

[0068] Among them, the detection instruction can be issued by the staff through the human-computer interaction interface of the terminal. The human-computer interaction interface of the terminal can specifically display a pre-designated platform interface for performing equipment liner detection. The staff issues the detection instruction by clicking on the area used to represent the trigger detection on the designated platform interface; or, the detection instruction can be automatically generated by the terminal when it detects that there is a device to be detected at a preset position; specifically, the terminal can be connected to a sensing device, which is used to detect whether there is a device to be detected at a first preset position. When the sensing device detects that there is a device to be detected at the first preset position, it can generate a first trigger instruction and send it to the terminal. The terminal automatically generates a detection instruction after receiving the first trigger instruction; or, when the sensing device detects that there are a preset number of devices to be detected at at least one second preset position, it can generate a second trigger instruction and send it to the terminal. The terminal automatically generates a detection instruction after receiving the second trigger instruction.

[0069] Specifically, the terminal can generate a trigger instruction in response to the received detection instruction and send it to the camera and laser emitter respectively. After receiving the trigger instruction, the camera can collect the image in real time to obtain the inner liner image. After receiving the trigger instruction, the laser emitter can perform laser scanning to obtain the inner liner size parameters.

[0070] Step 204: compare at least a portion of the inner liner image with a pre-stored standard image to obtain a defect evaluation index corresponding to the area to be identified; compare the inner liner size parameters with the pre-stored standard size parameters to obtain a size evaluation index of the device to be detected.

[0071] The process of comparing the inner liner image with the standard image is intended to accurately evaluate the similarity or difference between the quality of the inner liner of the device to be tested and the quality of the standard inner liner, for example, it may include pixel value comparison, color distribution comparison, texture and shape feature comparison, etc. It should be noted that the quality inspection of the inner liner based on image comparison involves visual inspection content, for example, it may include inspection of whether the surface is flat, whether the spraying is uniform, whether the coverage is complete, and whether the glass adhesive has overflowed.

[0072] As an example, before comparing the liner image with the standard image, the liner image can be divided into regions to obtain at least one region to be identified, and then the liner image within each region to be identified is compared with the corresponding standard image. The region to be identified is used to indicate the specific parts corresponding to the liner of the device to be tested. The quality of these specific parts can intuitively reflect the overall quality of the liner. The specific parts include, for example, the spraying area of ​​the liner, the adhesive area of ​​the glass adhesive, etc.

[0073] As an example, the terminal may pre-store a standard image of at least one specific part corresponding to at least one device. After the terminal obtains the liner image of the device to be detected, the liner image can be input into a pre-trained image segmentation model. The image segmentation model is used to divide the liner image into the area to be identified corresponding to the preset specific part on the liner image. Then, based on the device to be detected, the standard image of at least one specific part corresponding to the device to be detected is matched. Then, based on the specific part that each area to be identified matches, the liner image within each area to be identified is compared with the standard image of the corresponding specific part to obtain the defect assessment index corresponding to each area to be identified. Among them, the image segmentation model can be trained using multiple sample images, and the sample image carries multiple area labels, and the area labels are used to represent the pixel points corresponding to different specific parts. The image segmentation model can use a deep neural network, a long short-term memory network, etc. This application does not limit the model structure used by the image segmentation model, and it only needs to be able to be trained based on the sample image and divide the liner image into areas to be identified.

[0074] The defect assessment index is used to indicate whether the inner tank surface of the equipment to be inspected is flat, whether the spraying is uniform, whether the coverage is complete, and whether the glass adhesive has overflowed.

[0075] The process of comparing the liner size parameters with the standard size parameters is intended to accurately evaluate the similarity or difference in size between the liner of the device to be tested and the standard liner.

[0076] The size assessment index is used to indicate the degree of conformity between the inner liner size of the device to be tested and the standard inner liner size.

[0077] Step 206: Determine the inner tank inspection result of the device to be inspected based on the defect assessment index and the size assessment index.

[0078] In this embodiment, the terminal can fuse the defect assessment index corresponding to the inner liner appearance and the size assessment index corresponding to the size to obtain the final inner liner inspection result. The inner liner inspection result is used to indicate the overall evaluation of the size and appearance of the inner liner of the device to be inspected.

[0079] It should be noted that, for example, the terminal can take the average of the defect assessment index and the size assessment index to obtain the inner liner quality assessment index corresponding to the device to be inspected, and can compare the inner liner inspection result with a preset detection threshold. The detection threshold is used to indicate the minimum quality standard of the inner liner. When the inner liner quality assessment index exceeds the detection threshold, the terminal can confirm that the current device to be inspected does not meet the minimum quality standard and fails the inspection. At this time, the terminal will use the failed inspection as the inner liner inspection result. When the inner liner quality assessment index does not exceed the detection threshold, the terminal can confirm that the quality inspection of the current device to be inspected has passed. At this time, the terminal will use the passed inspection as the inner liner inspection result.

[0080] The quality inspection method provided in the embodiment of the present application can, after acquiring the inner liner size parameters and inner liner image of the device to be inspected, use numerical comparison and image recognition to perform automated quality inspection on the inner liner appearance and size, thereby improving the inspection efficiency and also improving the accuracy of inner liner inspection.

[0081] like Figure 3 As shown, in some optional embodiments, before step 202, the following steps are included:

[0082] Step 212: using a sensing device disposed at a first preset position to obtain position information of the device to be detected; the device to be detected is disposed on a conveying device and is moved by the conveying device;

[0083] Step 2014: When the posture information meets the first preset requirement, a stop instruction is generated and sent to the transmission device to control the transmission device to stop moving the device to be detected.

[0084] The position and posture information of the device to be detected includes information on the position and posture of the device to be detected in space, and can be used to indicate the actual position, specific orientation, tilt degree, etc. of the device to be detected.

[0085] The sensing device may be, for example, a posture sensor, which is usually composed of sensors such as an accelerometer, a gyroscope and a magnetometer. The accelerometer can determine the posture of the device to be detected by measuring its acceleration. When the acceleration of the device to be detected changes, the tiny mass and spring system inside the accelerometer will move due to the force, and the acceleration of the device to be detected can be determined by measuring this movement; the gyroscope determines the posture of the device to be detected by measuring the angular velocity of the device to be detected. When the device to be detected rotates, the rotor or oscillator inside the gyroscope will be affected by the force and cause corresponding displacement, and the angular velocity of the device to be detected can be determined by measuring this displacement; the magnetometer is used to measure the direction and strength of the earth's magnetic field, thereby helping to determine the absolute direction of the device to be detected.

[0086] The attitude sensor can obtain the direction, rotation angle and position information of the device to be detected in space in real time through the above-mentioned sensors such as accelerometers, gyroscopes and magnetometers, so as to calculate the position and attitude of the device to be detected in space.

[0087] The transmission device may refer to a conveyor belt, and the transmission device may realize the position movement of the device to be detected by means of gears, chains, belts, etc. The first preset position may be located at one side of the transmission device.

[0088] The first preset requirement is used to indicate a specific position in space and a specific posture of the device to be detected.

[0089] It should be noted that when the posture information does not meet the first preset requirement, the terminal can also generate a stop command to control the conveying equipment to stop moving the equipment to be detected, and further generate an alarm signal to prompt the staff to adjust the position of the equipment to be detected, or generate an adjustment command and send it to the adjustment device to automatically adjust the position of the equipment to be detected through the adjustment device.

[0090] The adjustment device may include, for example, a gripper and a mobile mechanical arm, and the gripper may be arranged on the mobile mechanical arm. The process of generating an adjustment instruction and sending it to the adjustment device so as to automatically adjust the position of the device to be detected through the adjustment device may include: based on the posture information of the device to be detected and the specific position and specific posture indicated by the first preset requirement, calculating the difference between the current position of the device to be detected and the specific position, and the difference between the current posture and the specific posture, and obtaining the deviation posture information.

[0091] Furthermore, a pre-established first conversion model is used to determine the rotation angle adjustment process of each joint of the gripper corresponding to the deviation posture information, wherein the first conversion model includes the correspondence between the rotation angle of each joint of the gripper and the posture information of the object.

[0092] Furthermore, based on the starting position of the mobile robotic arm and the posture information of the device to be detected, the moving trajectory of the mobile robotic arm is calculated, and the moving trajectory is used to indicate the position change process of the end of the mobile robotic arm in space.

[0093] Subsequently, a pre-established second transformation model is used to determine the degree of freedom change process of each joint in the mobile robotic arm corresponding to the moving trajectory, wherein the second transformation model includes the correspondence between the degree of freedom of each joint of the mobile robotic arm and the position of the end of the mobile robotic arm.

[0094] Based on the rotation angle adjustment process of the gripper and the moving trajectory of the mobile robotic arm, the terminal can generate an adjustment instruction and send it to the adjustment device. After receiving the adjustment instruction, the mobile robotic arm can move the gripper to the position of the device to be detected according to the above-mentioned degree of freedom change process. After the movement is completed, the gripper grasps the device to be detected and adjusts its position according to the above-mentioned rotation angle adjustment process until the device to be detected is in a specific position and meets a specific posture.

[0095] In this embodiment, the terminal can use a sensing device to monitor the actual position and actual posture of the device to be detected, and when the actual position and actual posture of the device to be detected meet the first preset requirement, the conveying device is controlled to stop moving, so that the device to be detected can be fixed at a specific position corresponding to the first preset requirement and meet the specific posture corresponding to the first preset requirement, thereby ensuring that the collected liner size parameters and liner image will not produce errors due to the positional offset of the device to be detected, thereby ensuring the accuracy of liner detection.

[0096] like Figure 4 As shown, in some optional embodiments, step 202 includes:

[0097] Step 2022: Move the laser scanning device set at the initial position to a second preset position, and control the laser scanning device to scan the device to be detected to obtain the inner liner size parameters;

[0098] Step 2024: Use an image acquisition device set at a third preset position to acquire an image of the device to be detected to obtain an image of the inner tank.

[0099] The second preset position may be located, for example, in the inner space of the inner container of the device to be detected.

[0100] In this embodiment, when the terminal determines that the device to be detected is located at a specific position indicated by the first preset requirement and meets the specific posture indicated by the first preset requirement, it can control the laser scanning device to move from the initial position to the internal space of the inner liner of the device to be detected, and obtain the inner size parameters of the device to be detected through laser scanning.

[0101] The third preset position can be fixedly arranged on one side of the conveying device.

[0102] Specifically, the laser scanning device may include a mechanical arm and a laser emitter disposed on the mechanical arm.

[0103] The process of moving the laser scanning device set at the initial position to the second preset position, and controlling the laser scanning device to scan the equipment to be inspected to obtain the inner liner size parameters may include: generating a movement instruction and sending it to the robotic arm to control the robotic arm to move the laser emitter from the initial position to the second preset position according to the preset degree of freedom change process; generating a scanning instruction and sending it to the laser emitter to control the laser emitter to perform laser measurement on the inner liner of the equipment to be inspected to obtain the inner liner size parameters.

[0104] The degree of freedom change process corresponding to the robotic arm is used to instruct the various joints of the robotic arm to undergo a degree of freedom change, thereby moving the end of the robotic arm from the initial position to the second preset position through the opening surface of the device to be detected.

[0105] It should be noted that in this embodiment, after obtaining the inner liner size parameters, the terminal can also generate a reset instruction and send it to the robotic arm to control the robotic arm to reverse the preset degree of freedom change process and move the laser emitter from the second preset position to the initial position.

[0106] In this embodiment, the terminal can control the laser transmitter to move to the inside of the inner liner of the device to be detected to collect size parameters, thereby making the collected inner liner size parameters more accurate, making the automated detection of the size of the device to be detected more realistic, and the detection results more accurate.

[0107] like Figure 5 As shown, in some optional embodiments, before step 2014, the method further includes:

[0108] Step 2013: when the position information meets the second preset requirement, the first size information is acquired by using a laser transmitter arranged at a fourth preset position;

[0109] Step 202 includes:

[0110] Step 2026: using a laser transmitter disposed at a fourth preset position to obtain second size information; and using an image acquisition device disposed at a fifth preset position to obtain an image of the inner container;

[0111] Step 2028: Determine the inner liner size parameters based on the first size information and the second size information.

[0112] The second preset requirement is used to indicate another specific position in the space and another specific posture of the device to be detected. The other specific posture of the device to be detected indicated by the second preset requirement may be the same as the specific posture indicated by the first preset requirement.

[0113] It should be noted that the other specific position indicated by the second preset requirement and the specific position indicated by the first preset requirement can be arranged in a sequential order along the moving direction of the conveying device.

[0114] The first size information refers to the relevant size data of the inner liner of the device to be detected obtained by the laser transmitter performing laser scanning when the device to be detected is located at another specific position indicated by the second preset requirement. At this time, the first size information obtained is deformed or deviated from the actual size data of the inner liner of the device to be detected.

[0115] The second size information refers to the relevant size data of the inner liner of the device to be detected obtained when the laser emitter performs laser scanning when the device to be detected is located at the specific position indicated by the first preset requirement. The second size information obtained at this time also has deformation or deviation from the actual size data of the inner liner of the device to be detected.

[0116] In this embodiment, the terminal may, for example, input the first size information and the second size information into a pre-trained size prediction model, and the size prediction model is used to predict the actual size data of the liner based on the size data that may be detected when the device to be detected is in two specific positions, and obtain the size parameters of the liner. Among them, the size prediction model can be trained using multiple sample groups of multiple devices, and the sample group of each device contains the size data that may be detected when the current device is in two specific positions, and the two size data contained in the sample group both carry size labels, and the size labels are used to identify the actual size data of the current device. The size prediction model can use a deep neural network, a long short-term memory network, etc. This application does not limit the model structure used by the size prediction model, and it only needs to be able to be trained based on multiple sample groups of multiple devices, and predict the size parameters of the liner based on the first size information and the second size information.

[0117] In this embodiment, the inner liner size parameters and the inner liner image can be obtained through a fixedly set laser emitter and an image acquisition device. The laser emitter can scan the device to be detected twice, and based on the size data obtained from the two scans, predict the actual size of the inner liner of the device to be detected, thereby obtaining accurate inner liner size parameters and improving detection accuracy while reducing the movement control of the laser emitter.

[0118] In some optional embodiments, step 204 includes:

[0119] Performing part recognition on the liner image to obtain a first region corresponding to the coating region in the liner image and a second region corresponding to the adhesive region in the liner image; the standard image corresponds to the coating region and the adhesive region respectively;

[0120] Compare the inner container image in the first area with the corresponding standard image, and calculate the coating defect evaluation index based on the pixel points where there are differences between the inner container image and the standard image;

[0121] Comparing the liner image in the second area with the corresponding standard image, and calculating the adhesive defect evaluation index based on the pixel points where there are differences between the liner image and the standard image;

[0122] The coating defect assessment index and the adhesive defect assessment index are combined to obtain the defect assessment index.

[0123] As an example, in this embodiment, the above-mentioned image segmentation model can be used to perform part recognition on the liner image to obtain a first area corresponding to the coating area in the liner image and a second area corresponding to the glue area in the liner image. The first area and the second area can be understood as the above-mentioned areas to be recognized. The specific process of part recognition can refer to the process of dividing the areas to be recognized on the liner image using the above-mentioned image segmentation model, which will not be repeated here.

[0124] Based on the pixel points where the inner liner image differs from the standard image, the coating defect evaluation index is calculated, for example, using the following formula:

[0125] Represents the coating defect assessment index, The percentage of the number of pixels in the liner image in the first region that are different from the corresponding standard image accounts for the total number of pixels in the liner image in the first region. The process of calculating the adhesive defect evaluation index based on the number of pixels that are different between the liner image and the standard image may, for example, be calculated using the following formula:

[0126] Represents the adhesive defect evaluation index, It indicates the percentage of the pixel points in the inner liner image in the second area that are different from the corresponding standard image to the total number of pixel points in the inner liner image in the second area.

[0127] The process of combining the coating defect assessment index and the adhesive defect assessment index to obtain the defect assessment index may be, for example, formulated as follows:

[0128] in, represents the defect evaluation index, δ1 represents the pre-set influence coefficient corresponding to the coating defect, δ2

[0129] Indicates the preset influence coefficient corresponding to the adhesive defect.

[0130] In this embodiment, the terminal can extract areas where adhesive defects may exist and areas where coating defects may exist from the liner image by dividing the areas, thereby limiting the defect detection content that needs to be performed in different areas of the liner image. Subsequently, by comparing the liner images in different areas with the corresponding standard images at the pixel level, the defect detection is converted into an image recognition problem, which simplifies the defect detection process and improves the defect detection speed.

[0131] In some optional embodiments, step 206 includes:

[0132] Based on the preset defect coefficient, the defect evaluation index and the size evaluation index are integrated to obtain the liner quality evaluation index;

[0133] Based on the preset standard quality index and the inner liner quality evaluation index, the inner liner test result of the device to be tested is determined.

[0134] As an example, in step 204, the step of comparing the inner liner size parameters with the pre-stored standard size parameters to obtain the size evaluation index of the device to be tested may adopt the following formula:

[0135]

[0136] Where ω represents the size evaluation index, Indicates the inner liner size parameter of the device to be tested in the nth dimension. It represents the standard size parameters of the inner liner of the device to be tested in the nth dimension. The n dimensions may include, for example, the length, width, height, the connecting curvature of each surface, the diameter of the pores, the spacing distance, etc. of the inner liner of the device to be tested.

[0137] Based on the preset defect coefficient, the defect evaluation index and the size evaluation index are integrated to obtain the liner quality evaluation index. The following formula can be used:

[0138] Among them, θ represents the inner liner quality evaluation index, μ1 represents the defect coefficient corresponding to the size evaluation parameter, and μ2 represents the defect coefficient corresponding to the defect evaluation index.

[0139] Furthermore, based on the preset standard quality index and the liner quality evaluation index, the liner test result of the device to be tested can be determined using the following formula:

[0140] |θ-θ′|≥τ, where θ′ represents the standard quality index and τ can represent the above-mentioned detection threshold.

[0141] In this embodiment, the terminal can obtain the inner liner detection result of the device to be detected based on the difference between the standard quality index and the inner liner quality evaluation index. When the difference reaches the detection threshold, it can be considered that the inner liner quality evaluation index of the device to be detected is too different from the standard quality index. The terminal can confirm that the current device to be detected does not meet the minimum quality standard and fails the test as the final inner liner detection result. When the inner liner detection result does not exceed the detection threshold, the terminal can confirm that the inner liner quality evaluation index of the current device to be detected is close to the standard quality index, and the current device to be detected has passed the detection quality.

[0142] The above-mentioned liner detection method collects the liner size parameters of the equipment to be detected through a laser transmitter, uses an image acquisition device to collect the liner image of the equipment to be detected, and processes the liner image at the image level to realize the conversion from appearance defect recognition to image recognition, thereby realizing the automated processing of quality inspection of the equipment to be detected, thereby saving manpower, improving the liner detection speed, and ensuring the accuracy of liner detection.

[0143] It should be understood that, although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0144] Based on the same inventive concept, the embodiment of the present application also provides an inner liner detection device for implementing the inner liner detection method involved above. The implementation scheme for solving the problem provided by the inner liner detection device is similar to the implementation scheme recorded in the inner liner detection method above, so the specific limitations in one or more device embodiments provided below can refer to the limitations of the inner liner detection method above, and will not be repeated here.

[0145] In one embodiment, Figure 6 As shown, a liner detection device 600 is provided, comprising:

[0146] An acquisition module 602 is used to acquire the inner liner size parameters and inner liner image of the device to be detected;

[0147] The comparison module 604 is used to compare at least a part of the inner tank image with a pre-stored standard image to obtain a defect evaluation index corresponding to the area to be identified; compare the inner tank size parameter with the pre-stored standard size parameter to obtain a size evaluation index of the device to be detected;

[0148] The determination module 606 is used to determine the inner liner detection result of the device to be detected based on the defect evaluation index and the size evaluation index.

[0149] In some optional embodiments, the acquisition module 602 is further configured to:

[0150] A sensing device disposed at a first preset position is used to obtain position information of the device to be detected; the device to be detected is disposed on a conveying device and is moved by the conveying device;

[0151] When the posture information meets the first preset requirement, a stop instruction is generated and sent to the conveying device to control the conveying device to stop moving the device to be detected.

[0152] In some optional embodiments, the acquisition module 602 is further configured to:

[0153] Move the laser scanning device set at the initial position to a second preset position, and control the laser scanning device to scan the device to be detected to obtain the inner liner size parameters;

[0154] An image acquisition device arranged at a third preset position is used to acquire an image of the device to be detected to obtain an image of the inner tank.

[0155] In some optional embodiments, the laser scanning device includes a mechanical arm, a laser emitter disposed on the mechanical arm;

[0156] The acquisition module 602 is further configured to:

[0157] Generate a movement instruction and send it to the robot arm to control the robot arm to move the laser transmitter from the initial position to a second preset position according to a preset degree of freedom change process;

[0158] Generate a scanning instruction and send it to the laser transmitter to control the laser transmitter to perform laser measurement on the inner liner of the device to be detected to obtain the inner liner size parameters.

[0159] In some optional embodiments, the acquisition module 602 is further configured to:

[0160] When the position information meets the second preset requirement, the first size information is acquired using a laser transmitter disposed at a fourth preset position;

[0161] A laser transmitter disposed at a fourth preset position is used to acquire second size information; and an image acquisition device disposed at a fifth preset position is used to acquire an image of the inner container;

[0162] Based on the first size information and the second size information, determine the inner liner size parameters.

[0163] In some optional embodiments, the comparison module 604 is further configured to:

[0164] Performing part recognition on the liner image to obtain a first region corresponding to the coating region in the liner image and a second region corresponding to the adhesive region in the liner image; the standard image corresponds to the coating region and the adhesive region respectively;

[0165] Compare the inner container image in the first area with the corresponding standard image, and calculate the coating defect evaluation index based on the pixel points where there are differences between the inner container image and the standard image;

[0166] Comparing the liner image in the second area with the corresponding standard image, and calculating the adhesive defect evaluation index based on the pixel points where there are differences between the liner image and the standard image;

[0167] The coating defect assessment index and the adhesive defect assessment index are combined to obtain the defect assessment index.

[0168] In some optional embodiments, the determination module 606 is further configured to:

[0169] Based on the preset defect coefficient, the defect evaluation index and the size evaluation index are integrated to obtain the liner quality evaluation index;

[0170] Based on the preset standard quality index and the inner liner quality evaluation index, the inner liner test result of the device to be tested is determined.

[0171] Each module in the above device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module above.

[0172] In one embodiment, Figure 7 As shown, a liner detection system 700 is provided, including a liner detection device 600, and a transmission device 702, a sensing device 704, a laser sensor 706 and an image acquisition device 707 respectively connected to the liner detection device 600;

[0173] The transmission device 702 is used to transmit the device to be detected;

[0174] The sensing device 704 is used to obtain the position information of the device to be detected;

[0175] The laser sensor 706 is used to obtain the inner container size parameters of the device to be detected;

[0176] The image acquisition device 708 is used to obtain the inner tank image of the device to be inspected.

[0177] like Figure 7 As shown, in some optional embodiments, the liner detection system 700 further includes a mechanical arm 705, and the laser sensor 706 is disposed on the mechanical arm 705;

[0178] The robot arm 705 is used to move the laser emitter 706 according to a preset degree of freedom change process.

[0179] The (device subject) provided in this embodiment can execute the method provided in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be described in detail here.

[0180] Figure 8 The schematic diagram of the computer structure provided in this application. Figure 8 As shown, the computer device 800 provided in this embodiment includes: at least one processor 801 and a memory 802. Optionally, the computer device 800 also includes a communication component 803. The processor 801, the memory 802 and the communication component 803 are connected via a bus 804.

[0181] In a specific implementation process, at least one processor 801 executes the computer-executable instructions stored in the memory 802, so that at least one processor 801 executes the above method.

[0182] The specific implementation process of the processor 801 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0183] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0184] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0185] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0186] The present application also provides a computer program product, including a computer program, which implements the above-mentioned liner detection method when executed by a processor.

[0187] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above-mentioned liner detection method is implemented.

[0188] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special-purpose computer.

[0189] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0190] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0191] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0192] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0193] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0194] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.

[0195] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A liner detection method, characterized in that: include: Obtain the inner liner size parameters and inner liner image of the device to be tested; Compare at least a portion of the inner liner image with a pre-stored standard image to obtain a defect evaluation index corresponding to the area to be identified; compare the inner liner size parameter with a pre-stored standard size parameter to obtain a size evaluation index of the device to be detected; Based on the defect evaluation index and the size evaluation index, an inner liner inspection result of the device to be inspected is determined.

2. The method according to claim 1, characterized in that: Before obtaining the inner liner size parameters and inner liner image of the device to be detected, the method further includes: A sensing device disposed at a first preset position is used to obtain position information of the device to be detected; the device to be detected is disposed on a conveying device and is moved by the conveying device; When the posture information meets the first preset requirement, a stop instruction is generated and sent to the conveying device to control the conveying device to stop moving the device to be detected.

3. The method according to claim 1, characterized in that The step of obtaining the inner liner size parameters and the inner liner image of the device to be detected includes: Move the laser scanning device set at the initial position to a second preset position, and control the laser scanning device to scan the device to be detected to obtain the liner size parameters; An image acquisition device arranged at a third preset position is used to acquire an image of the device to be detected to obtain the liner image.

4. The method according to claim 3, characterized in that The laser scanning device comprises a mechanical arm and a laser emitter arranged on the mechanical arm; The step of moving the laser scanning device set at the initial position to the second preset position and controlling the laser scanning device to scan the device to be detected to obtain the inner liner size parameters includes: Generate a movement instruction and send it to the robotic arm to control the robotic arm to move the laser transmitter from the initial position to the second preset position according to a preset degree of freedom change process; A scanning instruction is generated and sent to the laser transmitter to control the laser transmitter to perform laser measurement on the inner liner of the device to be detected to obtain the size parameters of the inner liner.

5. The method according to claim 2, characterized in that: When the posture information meets the first preset requirement, before generating a stop instruction and sending it to the transmission device to control the transmission device to stop moving the device to be detected, the method further includes: When the posture information meets the second preset requirement, the first size information is acquired using a laser transmitter disposed at a fourth preset position; The step of obtaining the inner liner size parameters and the inner liner image of the device to be detected includes: The second size information is acquired by using a laser transmitter disposed at the fourth preset position; and the image acquisition device disposed at the fifth preset position is acquired by using an image acquisition device; The inner liner size parameter is determined based on the first size information and the second size information.

6. The method according to claim 1, characterized in that The step of comparing at least a portion of the inner tank image with a pre-stored standard image to obtain a defect assessment index corresponding to the to-be-identified area includes: Performing part recognition on the inner liner image to obtain a first area corresponding to a coating area in the inner liner image and a second area corresponding to an adhesive area in the inner liner image; the standard image corresponds to the coating area and the adhesive area, respectively; Comparing the inner container image in the first area with the corresponding standard image, and calculating the coating defect evaluation index based on the pixel points where there are differences between the inner container image and the standard image; Comparing the liner image in the second area with the corresponding standard image, and calculating the adhesive defect evaluation index based on the pixel points where there are differences between the liner image and the standard image; The coating defect evaluation index and the adhesive defect evaluation index are combined to obtain the defect evaluation index.

7. The method according to claim 1, characterized in that The step of determining the inner liner inspection result of the device to be inspected based on the defect assessment index and the size assessment index includes: Based on a preset defect coefficient, the defect evaluation index and the size evaluation index are integrated to obtain an inner liner quality evaluation index; Based on the preset standard quality index and the liner quality evaluation index, the liner detection result of the device to be detected is determined.

8. A liner detection device, characterized in that: include: An acquisition module is used to acquire the inner liner size parameters and inner liner image of the device to be detected; A comparison module, used to compare at least a part of the inner tank image with a pre-stored standard image to obtain a defect evaluation index corresponding to the area to be identified; compare the inner tank size parameter with a pre-stored standard size parameter to obtain a size evaluation index of the device to be detected; A determination module is used to determine the inner liner detection result of the device to be detected based on the defect evaluation index and the size evaluation index.

9. A liner detection system, characterized in that: It comprises the inner liner detection device as claimed in claim 8, and a transmission device, a sensing device, a laser sensor and an image acquisition device respectively connected to the inner liner detection device; The transmission device is used to transmit the device to be detected; The sensing device is used to obtain the position information of the device to be detected; The laser sensor is used to obtain the inner container size parameters of the device to be detected; The image acquisition device is used to obtain the inner tank image of the device to be detected.

10. The system according to claim 9, characterized in that The liner detection system further includes a mechanical arm, and the laser sensor is arranged on the mechanical arm; The mechanical arm is used to move the laser emitter according to a preset degree of freedom change process.

11. A computer device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor executes the liner detection method as described in any one of claims 1-7.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the liner detection method as described in any one of claims 1-7.

13. A computer program product, characterized in that It includes a computer program, which, when executed by a processor, implements the liner detection method described in any one of claims 1 to 7.

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