Sealing performance detection method and system based on artificial intelligence

Abstract

The embodiment of the invention relates to the technical field of sealing detection, and discloses an artificial intelligence-based sealing detection method, which comprises the following steps of: determining corresponding control parameter information according to the volume of a detected object and a deformation parameter of the detected object of detected object information; according to the control parameter information, the working states of the air source assembly and the inflation proportional valve are controlled to inflate the tested object; if the pressure conversion information is smaller than a first set threshold value, it is determined that the airtightness of the corresponding measured object meets the requirement, and if the pressure conversion information is larger than the first set threshold value, the next step is executed; and obtaining an infrared detection image of the detected object shot by the infrared camera, inputting the infrared detection image into the air tightness detection model for identification so as to determine the air tightness state of the corresponding detected object, and outputting the corresponding air tightness state. According to the scheme of the embodiment of the invention, through accurate inflation control and an intelligent detection process, the method can improve the detection efficiency while ensuring the detection precision.

Description

Technical Field

[0001] The present invention relates to the technical field of sealing detection, and in particular to a sealing detection method and system based on artificial intelligence. Background Art

[0002] At present, the existing equipment has the following problems when performing sealing tests: first, it is impossible to accurately estimate the volume of the object being tested and thus cannot achieve fast and accurate sealing tests; second, it is impossible to achieve accurate leakage measurements when performing measurements. Therefore, designing a solution that can efficiently perform sealing tests has become a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0003] In response to the above defects, an embodiment of the present invention discloses a sealing detection method based on artificial intelligence, which can realize fast and accurate sealing detection.

[0004] The first aspect of the embodiment of the present invention discloses a sealing detection method based on artificial intelligence, comprising: Acquire information of the object to be measured, and determine corresponding control parameter information according to the volume of the object to be measured and the deformation parameters of the object to be measured; Controlling the working state of the gas source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard, wherein the inflation operation on the object to be measured includes inflating the cavity in which the object to be measured is placed or inflating the cavity of the object to be measured; After the inflation operation is completed, the pressure change information of the object under test within a period of time is obtained through the pressure sensor. If the pressure change information is less than the first set threshold, it is determined that the air tightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If the pressure change information is greater than the first set threshold, the next step is executed; An infrared detection image of the object to be tested taken by an infrared camera is obtained, and the infrared detection image is input into the airtightness detection model for identification to determine the airtightness status of the corresponding object to be tested, and the corresponding airtightness status is output.

[0005] As an optional implementation, in the first aspect of the embodiment of the present invention, the infrared detection image of the object to be measured taken by the infrared camera is obtained, and the infrared detection image is input into the airtightness detection model for identification to determine the airtightness state of the corresponding object to be measured, and the corresponding airtightness state is output, including: Acquire a detection image information set of the object under test captured by an infrared camera within a period of time, and input each detection image information in the detection image information set into a temperature detection model for detection to obtain the temperature state change at each time point; The temperature state change is input into the airtight timing detection model for identification to determine whether the object under test meets the airtightness requirements. If so, it is determined that the airtightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If not, it is determined that the corresponding object under test does not meet the airtightness requirements and is determined to be in an unqualified state, and the airtightness detection result of the corresponding leakage point is output, and the airtightness detection result includes the leakage point location information and the leakage point size information.

[0006] As an optional implementation, in the first aspect of the embodiment of the present invention, before acquiring a detection image information set of the object under test captured by the infrared camera within a period of time, it also includes: Acquire the image information of the object to be tested captured by the camera, and input the image information of the object to be tested into the detection and recognition model to determine the position information of each detection point in the image information of the object to be tested; or, transmit the image information of the object to be tested captured by the camera to the user end, so that the user can circle the corresponding detection area on the user end, and transmit the corresponding detection area to the detection end; The step of inputting each detection image information in the detection image information set into the airtightness detection model for detection to obtain leakage state changes at each time point includes: Determine the corresponding infrared detection area of ​​each detection image information in the detection image information set according to the position information or the detection area of ​​each detection point; The infrared detection area is input into the airtightness detection model for detection to determine the corresponding temperature change information.

[0007] As an optional implementation, in the first aspect of the embodiment of the present invention, the step of obtaining an infrared detection image of the object to be measured taken by an infrared camera, and inputting the infrared detection image into an airtightness detection model for identification to determine the airtightness state of the corresponding object to be measured further includes: Acquire an infrared detection image of the object to be detected captured by an infrared camera, and process the infrared detection image to obtain preprocessed infrared detection data; Determine the temperature parameter of each pixel in the infrared detection data, and determine the grayscale difference parameter and the gradient difference parameter of adjacent pixels according to the temperature parameter of each pixel; According to the temperature parameters of each pixel point, the grayscale difference parameter and the gradient difference parameter of the corresponding data point and the adjacent pixel point are determined to determine the corresponding change node information; Determine the change parameter information between each pixel and other pixel points according to the change node information of each pixel point in the obtained infrared detection data, and determine the association information between each pixel point and other pixel point areas according to the change parameter information; The infrared detection data is clustered according to the association information between each pixel point and other pixel point areas to obtain a clustered image result, and feature extraction is performed on the image result to obtain corresponding identification features, which are input into the airtightness detection model for identification to determine the airtightness status of the corresponding object under test.

[0008] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the obtaining of the measured object information and determining the corresponding control parameter information according to the measured object volume and the measured object deformation parameter of the measured object information includes: The point cloud basic data of the object to be measured is obtained through a 3D scanner, and the voxels occupied by the object to be measured are identified through image processing technology, and the number of voxels is calculated; Determine whether the object to be tested is a known test object according to the point cloud basic data. If so, directly retrieve the volume parameters and deformation state parameters associated with the corresponding object to be tested. If not, identify the number of voxels occupied by the object to be tested through image processing technology, determine its volume parameters according to the number of voxels, and determine the corresponding deformation state parameters according to the volume parameters and the point cloud basic data. The deformation state parameters refer to the influence of air pressure parameters on the deformation of the object to be tested. Corresponding control parameter information is determined according to the volume parameters and the deformation state parameters.

[0009] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the sealing detection method further includes: During the inflation process, the point cloud dynamic data collected by the 3D scanner is obtained, and a voxel correlation relationship between the point cloud basic data and the point cloud dynamic data is constructed; The similarity parameters between the point cloud basic data and the point cloud dynamic data are calculated according to the voxel association, and the similarity parameters are input into a pre-built deformation detection model to determine the corresponding deformation result, and the corresponding deformation result is output.

[0010] As an optional implementation, in the first aspect of the embodiment of the present invention, controlling the working state of the air source component and the inflation proportional valve according to the control parameter information to inflate the object under test according to the set inflation standard includes: Determine the target value of the inflation pressure according to the control parameter information, and measure the actual output pressure of the inflation system in real time through a pressure sensor; Comparing the target value of the inflation pressure with the actual output pressure detected by the pressure sensor to calculate a corresponding error parameter; Calculating a corresponding control amount according to the error parameter and a set of parameters of the PID controller, wherein the set of parameters includes a proportional coefficient, an integral coefficient and a differential coefficient; The calculated control amount is used to adjust the inflation parameters of the inflation system so that the actual measured value gradually approaches the target value and the inflation operation is performed on the object to be measured.

[0011] A second aspect of an embodiment of the present invention discloses a sealing detection system based on artificial intelligence, comprising: Acquisition module: used to acquire information of the object to be measured, and determine corresponding control parameter information according to the volume of the object to be measured and the deformation parameters of the object to be measured; Inflation module: used to control the working state of the air source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard, wherein the inflation operation on the object to be measured includes inflating the cavity in which the object to be measured is placed or inflating the cavity of the object to be measured; Pressure detection module: used to obtain the pressure change information of the object under test within a period of time through the pressure sensor after the inflation operation is completed. If the pressure change information is less than the first set threshold, it is determined that the air tightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If the pressure change information is greater than the first set threshold, the next step is executed; Infrared detection module: used to obtain the infrared detection image of the object under test taken by the infrared camera, and input the infrared detection image into the airtightness detection model for identification to determine the airtightness status of the corresponding object under test, and output the corresponding airtightness status.

[0012] The third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the artificial intelligence-based sealing detection method disclosed in the first aspect of the embodiment of the present invention.

[0013] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the artificial intelligence-based sealing detection method disclosed in the first aspect of an embodiment of the present invention.

[0014] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The artificial intelligence-based sealing detection method in the embodiment of the present invention obtains information about the object to be tested and automatically determines control parameters based on this information, thereby controlling the working state of the air source component and the inflation proportional valve. This process is highly automated, reduces reliance on manual operations, and improves detection efficiency. At the same time, the infrared detection image is recognized using an airtightness detection model, further improving the intelligent level of detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 It is a flow chart of a sealing detection method based on artificial intelligence disclosed in an embodiment of the present invention; Figure 2 This is a specific process of airtightness detection disclosed in an embodiment of the present invention; Figure 3 It is another specific process of airtightness detection disclosed in an embodiment of the present invention; Figure 4 is a schematic diagram of a deformation detection process disclosed in an embodiment of the present invention; Figure 5 is a structural schematic diagram of a sealing detection system based on artificial intelligence provided by an embodiment of the present invention; Figure 6 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0019] The existing equipment has the following problems when performing sealing tests: First, it is impossible to accurately estimate the volume of the object to be tested, and thus it is impossible to achieve fast and accurate sealing tests; second, it is impossible to achieve accurate leakage measurements when performing measurements. The embodiments of the present invention disclose a sealing test method, system, electronic device, and storage medium based on artificial intelligence, which obtains information about the object to be tested and automatically determines control parameters based on this information, thereby controlling the working state of the air source component and the inflation proportional valve. This process is highly automated, reducing reliance on manual operations and improving detection efficiency. At the same time, the infrared detection image is recognized using an airtightness detection model, further improving the intelligence level of the detection.

[0020] Embodiment 1 See also Figure 1 , Figure 1 It is a flow chart of the artificial intelligence-based sealing detection method disclosed in the embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the sealing detection method based on artificial intelligence includes the following steps: S101: Acquire information of the object to be measured, and determine corresponding control parameter information according to the volume of the object to be measured and the deformation parameter of the object to be measured; S102: controlling the working state of the gas source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard, wherein the inflation operation on the object to be measured includes inflating the cavity in which the object to be measured is placed or inflating the cavity of the object to be measured; S103: After the inflation operation is completed, the pressure change information of the object under test within a period of time is obtained through the pressure sensor. If the pressure change information is less than the first set threshold, it is determined that the air tightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If the pressure change information is greater than the first set threshold, the next step is executed; S104: Acquire an infrared detection image of the object to be measured taken by an infrared camera, input the infrared detection image into an airtightness detection model for identification to determine the airtightness status of the corresponding object to be measured, and output the corresponding airtightness status.

[0021] The embodiment of the present invention obtains the volume and deformation parameters of the object to be tested to determine the control parameter information, and then accurately controls the working state of the gas source component and the inflation proportional valve, so as to realize the inflation operation of the object to be tested according to the set inflation standard. This precise inflation method can ensure that during the detection process, the pressure and other conditions of the object to be tested meet the detection requirements, avoid the impact of insufficient or excessive inflation on the detection results, and improve the accuracy and reliability of the detection.

[0022] After inflation, the pressure sensor is used to obtain the pressure change information of the object under test within a period of time and compare it with the first set threshold. The air tightness of the object under test can be quickly judged. For objects under test with small pressure changes and meeting the requirements, it can be quickly determined to be qualified, and obviously qualified products can be quickly screened out, thereby improving the detection efficiency and reducing unnecessary subsequent detection processes.

[0023] When the pressure change information is greater than the first set threshold, an infrared camera is introduced to capture the infrared detection image of the object to be tested, and the airtightness detection model is used for identification. Combining the two methods of pressure detection and infrared image detection, multiple detections of the airtightness of the object to be tested are realized, which makes up for the limitations of a single detection method and makes the detection results more comprehensive and accurate.

[0024] The embodiment of the present invention inputs the infrared detection image into the airtightness detection model for identification, and utilizes the powerful image analysis and pattern recognition capabilities of artificial intelligence technology. It can automatically identify and analyze the characteristic information in the infrared image, accurately determine the airtightness status of the object being tested, and greatly improve the speed and accuracy of the detection compared to the traditional manual image analysis method, and reduce the interference and errors of human factors. The use of infrared cameras for detection is a non-contact detection method that will not cause physical damage or interference to the object being tested. It is suitable for objects of various shapes and materials, especially for some sensitive or precision parts that are not suitable for contact detection. It has good applicability and expands the application scope of the detection method.

[0025] The entire detection process, from obtaining information about the object being tested, controlling inflation, pressure detection to infrared image detection and result output, can be completed automatically by the system without the need for a large amount of manual intervention, thereby improving the level of automation in detection, reducing labor costs, and also improving the consistency and repeatability of detection, thus ensuring the stability of detection quality in large-scale detection.

[0026] In addition to the above reasons, during the specific implementation, a situation may occur in the battery pack measurement process. Although the overall leakage of the battery pack exceeds the set value, it meets the airtightness requirements. This is because the battery pack has multiple leakage points, but each of these multiple leakage points only leaks a little, which makes the overall leakage a little more, but each leaking a little will not have any effect on the overall airtightness. In order to achieve the above-mentioned precise detection, infrared precise detection is adopted.

[0027] As a complex system, the internal structure of the battery pack contains many components and connection parts, all of which may become potential leakage points. For example, the seals of the battery module, the connections of the electrical interface, and the splicing parts of the outer shell may cause tiny gaps due to the manufacturing process, material properties or long-term use, thereby causing leakage. Usually during the detection process, a threshold for overall leakage is pre-set as a standard for determining whether the airtightness is qualified. However, this set value is determined based on general conditions and experience, and cannot fully cover the complex leakage situations of the battery pack. In actual situations, when multiple leakage points exist at the same time and the leakage at each leakage point is small, the overall leakage may exceed the set value, but the leakage at each leakage point does not affect the overall airtightness.

[0028] When there are multiple leakage points in a battery pack, each leakage point only leaks a small amount of gas, but as time accumulates, these tiny leakages will gradually accumulate, eventually causing the overall leakage to exceed the set value. For example, there are 10 leakage points in a battery pack, and each leakage point leaks 0.01 ml of gas per second. After 100 seconds, the overall leakage reaches 10 × 0.01 × 100 = 10 ml. If the overall leakage threshold is set to 5 ml, the overall leakage has exceeded the standard. Although the overall leakage exceeds the set value, the impact on air tightness cannot be judged solely based on the leakage amount. Air tightness mainly focuses on whether gas leakage will have a substantial impact on the environment inside the battery pack, such as whether it will cause an increase in humidity to affect battery performance, whether it will allow external gas to enter and cause safety hazards, etc. If the amount of gas leaked from each leakage point is extremely small, not enough to change the key environmental parameters inside the battery pack, and will not threaten the normal operation and safety of the battery pack, then from the perspective of actual function, the battery pack can still be considered to meet the air tightness requirements.

[0029] In the sealing detection method based on artificial intelligence, the pressure change information of the object under test over a period of time is first obtained through a pressure sensor to preliminarily judge the air tightness. However, this method can only detect the overall pressure change and cannot accurately identify the existence of multiple tiny leaks. When multiple small leaks work together to cause the overall pressure change to exceed the first set threshold, pressure detection alone may misjudge the battery pack as unqualified for air tightness. Introducing an infrared camera to capture infrared detection images and using an air tightness detection model for identification can more accurately locate and analyze the leak. By analyzing the infrared image, not only can the location of the leak point be determined, but also the leakage degree of each leak point can be evaluated based on the image features. For example, in an infrared image, the leak point will show temperature distribution characteristics that are different from those of the surrounding area. By identifying and analyzing these characteristics, the leakage of each leak point can be determined. Even if the overall leakage exceeds the set value, if the infrared image detection shows that the leakage degree of each leak point is slight and has no obvious effect on the overall performance of the battery pack, then it can be comprehensively judged that the battery pack meets the air tightness requirements. This method of combining multiple detection methods can more comprehensively and accurately evaluate the airtightness of the battery pack and avoid misjudgment due to the limitations of a single detection method.

[0030] More preferably, Figure 2 The infrared detection image of the object to be tested taken by the infrared camera is obtained, and the infrared detection image is input into the airtightness detection model for identification to determine the airtightness state of the corresponding object to be tested, and the corresponding airtightness state is output, including: S1041: Acquire a detection image information set of the object under test captured by an infrared camera within a period of time, and input each detection image information in the detection image information set into a temperature detection model for detection to obtain a temperature state change at each time point; S1042: Input the temperature state change into the airtight timing detection model for identification to determine whether the object under test meets the airtightness requirements. If so, determine that the airtightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If not, determine that the corresponding object under test does not meet the airtightness requirements and is determined to be in an unqualified state, and output the airtightness detection result of the corresponding leakage point, wherein the airtightness detection result includes the leakage point location information and the leakage point size information.

[0031] The embodiment of the present invention can accurately obtain the temperature state changes at each time point by acquiring the detection image information set of the object under test within a period of time and inputting each detection image information into the temperature detection model. When the battery pack leaks, temperature differences will occur at the leakage point due to gas flow and other reasons. Such an operation can capture these subtle temperature changes and provide detailed data support for more accurate subsequent judgment of airtightness.

[0032] The temperature state changes are input into the airtight time series detection model, which can use time series information for analysis. Since airtightness problems often show specific change patterns over time, this model can better judge whether the object under test meets the airtightness requirements by analyzing the time series of temperature state changes, thereby improving the accuracy and reliability of the detection.

[0033] If the object under test is found to not meet the airtightness requirements, the airtightness test results including the location and size of the leak point can be output. This is crucial for subsequent repair and improvement work. Based on this precise information, the staff can quickly locate the problem and understand the severity of the leak point, and carry out targeted repairs or optimized designs, saving time and costs.

[0034] This testing process builds a comprehensive and detailed airtightness evaluation system from capturing temperature changes to analyzing the timing model and then outputting specific leakage point information. Compared with a single testing method, it can provide a deeper understanding of the airtightness of the battery pack, effectively avoiding overall performance problems caused by neglecting local small leakage points, and ensuring the quality and safety of the battery pack.

[0035] The airtight timing detection model of the embodiment of the present invention is constructed by the following steps: Data collection: Collect a large number of infrared detection image information sets of battery packs in different states, including battery packs with good airtightness and battery packs with different degrees of leakage problems. These data need to cover different battery pack models, different usage environments (such as temperature, humidity, pressure, etc.) and different usage time stages to ensure data diversity and comprehensiveness. For each sample, ensure that a continuous infrared detection image sequence is collected over a period of time so that the time series information of the temperature state can be extracted.

[0036] Image preprocessing: Standardize the infrared detection images, such as cropping, normalization, and denoising, to improve the quality and consistency of the images. For images of different sizes, they can be scaled to a uniform size to ensure the consistency of subsequent model inputs.

[0037] Time series generation: The image information of each sample at different time points is input into the temperature detection model to obtain the corresponding temperature state, and then these temperature states are arranged in chronological order to form the time series data of the temperature state.

[0038] Basic statistical features: Extract basic statistical features from the time series of temperature states, such as mean, variance, maximum, minimum, median, quartiles, etc. These features can reflect the overall distribution and dispersion of the temperature state.

[0039] Trend characteristics: Calculate the trend characteristics of the time series, such as linear trend, seasonal trend, etc. Linear regression, moving average and other methods can be used to analyze the temperature change trend over time to help the model understand the long-term trend of temperature status.

[0040] Frequency domain features: Perform Fourier transform on the time series to extract frequency domain features, including the amplitude and phase of different frequency components, to identify periodic temperature variation patterns, which is very useful for analyzing periodic phenomena (such as intermittent leakage) that may exist in the leakage process.

[0041] Dynamic characteristics: Calculate the dynamic characteristics of the time series, such as autocorrelation coefficient, partial autocorrelation coefficient, etc. These characteristics can reflect the correlation of temperature states at different time lags and provide the model with information about the dynamic characteristics of the time series.

[0042] Deep learning models such as long short-term memory networks (LSTM) or gated recurrent units (GRU) can be used, which have excellent performance in processing time series data. These models can automatically learn long-term dependencies in time series and are very effective in capturing complex changes in temperature status over time.

[0043] The dataset after preprocessing and feature extraction is divided into training set, validation set and test set. Generally, the division is carried out in a certain ratio (such as 7:1:2) to ensure the effectiveness of model training, validation and evaluation.

[0044] For deep learning models, training is performed using training set data, and the model parameters are updated through the back propagation algorithm to minimize the loss function (such as cross entropy loss, mean square error, etc.). At the same time, the performance of the model is monitored using the validation set to prevent overfitting, and the model hyperparameters (such as learning rate, number of hidden layers, number of neurons, etc.) are adjusted based on the performance of the validation set. For traditional time series prediction models and machine learning models, model parameters are adjusted through optimization algorithms (such as gradient descent, stochastic gradient descent, etc.) to achieve optimal performance on the training set, and then verified on the validation set.

[0045] More preferably, before acquiring the detection image information set of the object to be detected captured by the infrared camera within a period of time, the method further includes: Acquire the image information of the object to be tested captured by the camera, and input the image information of the object to be tested into the detection and recognition model to determine the position information of each detection point in the image information of the object to be tested; or, transmit the image information of the object to be tested captured by the camera to the user end, so that the user can circle the corresponding detection area on the user end, and transmit the corresponding detection area to the detection end; The step of inputting each detection image information in the detection image information set into the airtightness detection model for detection to obtain leakage state changes at each time point includes: Determine the corresponding infrared detection area of ​​each detection image information in the detection image information set according to the position information or the detection area of ​​each detection point; The infrared detection area is input into the airtightness detection model for detection to determine the corresponding temperature change information.

[0046] The embodiment of the present invention can automatically determine the position information of each detection point in the image information of the object being tested by inputting the image information of the object being tested into the detection and recognition model. This automated positioning method helps to improve the accuracy and consistency of detection and avoids the errors that may be caused by manual marking. For objects with complex structures, the detection and recognition model can accurately find key detection points based on its training data and algorithms, provide accurate position references for subsequent airtightness testing, and ensure the scientificity and reliability of the detection process.

[0047] In addition, precise detection point positioning can guide subsequent infrared inspections, ensuring that infrared cameras can focus on key areas, thereby improving detection efficiency and avoiding wasting time and resources in irrelevant areas.

[0048] The image information of the object being tested captured by the camera is transmitted to the user end, so that the user can circle the corresponding detection area, realizing the interaction between the user and the detection system. This method provides users with a customized detection experience. Users can select the area they think is most critical or needs the most attention as the detection area based on their professional knowledge, experience or specific needs, which enhances the pertinence and flexibility of the detection. User participation makes the detection system closer to the actual application scenario, meets the special requirements of different users for the detection area in different situations, and improves the applicability and practicality of the detection system.

[0049] According to the automatic or user-specified detection point location information or detection area, the corresponding infrared detection area can be accurately determined from the detection image information set. This avoids indiscriminate analysis of the entire image, concentrates detection resources on key areas, and improves the efficiency of infrared detection. At the same time, this targeted area determination method can reduce interference factors and improve detection accuracy, because different areas may have different background and environmental noise, and focusing only on key areas helps to eliminate interference from irrelevant information.

[0050] For some objects with larger sizes or complex structures, only infrared detection of key areas can speed up the detection process, enable the detection system to obtain valuable information more quickly, shorten the detection cycle, and improve the overall efficiency of production or detection.

[0051] Inputting the infrared detection area into the airtightness detection model for detection can accurately determine the corresponding temperature change information. Since the detection is performed on the pre-determined key area, the obtained temperature change information can better reflect the characteristics related to airtightness, reducing the impact of temperature fluctuations in other areas on the detection results, making the final temperature change information more representative and reliable.

[0052] Such operation can improve the accuracy of subsequent judgment of air tightness status based on temperature changes, more accurately discover potential leaks and leaks, help improve the performance of the entire air tightness detection system, and provide better data support for product quality assessment and fault diagnosis. Through these steps, the entire air tightness detection system is not only more flexible and accurate in locating detection points and determining detection areas, but also more accurate and effective in obtaining the final temperature change information, which can better meet the needs of different users and the characteristics of different objects being tested, and provide better technical support for product air tightness detection.

[0053] More preferably, the step of acquiring an infrared detection image of the object to be tested taken by an infrared camera and inputting the infrared detection image into an airtightness detection model for identification to determine the airtightness state of the corresponding object to be tested further includes: S104a: Acquire an infrared detection image of the object to be detected captured by an infrared camera, and process the infrared detection image to obtain pre-processed infrared detection data; S104b: Determine the temperature parameter of each pixel in the infrared detection data, and determine the grayscale difference parameter and the gradient difference parameter of adjacent pixels according to the temperature parameter of each pixel; S104c: Determine the grayscale difference parameter and the gradient difference parameter between the corresponding data point and the adjacent pixel points according to the temperature parameter of each pixel point to determine the corresponding change node information; S104d: determining change parameter information between each pixel and other pixel points according to the change node information of each pixel point in the obtained infrared detection data, and determining association information between each pixel point and other pixel point areas according to the change parameter information; S104e: Clustering the infrared detection data according to the association information between each pixel point and other pixel point areas to obtain a clustered image result, and extracting features from the image result to obtain corresponding identification features, and inputting the identification features into the airtightness detection model for identification to determine the airtightness status of the corresponding object under test.

[0054] The embodiment of the present invention processes the infrared detection image to obtain preprocessed infrared detection data, which helps to eliminate noise, distortion and other interference factors in the original image. Through image preprocessing, the contrast and clarity of the image can be enhanced, making the subsequent feature extraction and analysis process more accurate and reliable, and providing a better data basis for subsequent airtightness detection.

[0055] Determine the temperature parameters of each pixel in the infrared detection data, and calculate the grayscale difference parameters and gradient difference parameters of adjacent pixels, providing multi-dimensional information for subsequent airtightness analysis. The temperature parameters can directly reflect the heat distribution on the surface of the object, while the grayscale difference and gradient difference parameters can reveal the changes in the temperature field more finely, which helps to capture possible leakage areas more keenly, because there is usually a sudden change in the temperature gradient around the leakage point.

[0056] By determining the change node information based on the temperature, grayscale difference and gradient difference parameters of the pixel points, it is possible to accurately locate the locations in the image where abnormal temperature changes may exist. These change nodes may be key clues to potential leaks, which help narrow the detection range and improve the accuracy and efficiency of detection. By determining the change parameter information and association information between each pixel and other pixels, a more in-depth analysis of the spatial structure of the entire infrared detection data can be performed. The determination of this association information can grasp the distribution and change law of the temperature field from a global perspective, help identify the diffusion and connection of abnormal temperature areas that may be caused by leaks, avoid viewing each pixel in isolation, and provide more relevant and systematic data support for subsequent clustering operations.

[0057] Clustering the infrared detection data and grouping similar pixel areas into one category helps to simplify and integrate complex image information. The clustered image results are more regular, which is convenient for subsequent feature extraction. By extracting features from the clustered image results, the obtained identification features can more prominently reflect the information that may be related to airtightness, improving the representativeness and effectiveness of the features input into the airtightness detection model.

[0058] The extracted identification features are input into the airtightness detection model for identification, and finally the airtightness status of the object under test is determined. Due to the above series of processing and feature extraction, the model can make judgments based on more representative and systematic features, thereby improving the accuracy and reliability of airtightness detection, reducing the possibility of misjudgment, and providing a more scientific and accurate basis for airtightness assessment.

[0059] More preferably, the obtaining of the measured object information and determining the corresponding control parameter information according to the measured object volume and the measured object deformation parameter of the measured object information includes: S1011: Obtaining basic point cloud data of the object under test through a 3D scanner, identifying the voxels occupied by the object under test through image processing technology, and calculating the number of voxels; S1012: judging whether the object to be measured is a known detection object according to the point cloud basic data; if so, directly retrieving the volume parameter and deformation state parameter associated with the corresponding object to be measured; if not, identifying the number of voxels occupied by the object to be measured by image processing technology, determining its volume parameter according to the number of voxels, and determining the corresponding deformation state parameter according to the volume parameter and the point cloud basic data, wherein the deformation state parameter refers to the influence of the air pressure parameter on the deformation of the object to be measured; S1013: Determine corresponding control parameter information according to the volume parameter and the deformation state parameter.

[0060] The embodiment of the present invention obtains the basic point cloud data of the object to be measured through a three-dimensional scanner, and uses image processing technology to identify the voxels occupied by the object to be measured and calculate the number of voxels. This method can provide very accurate geometric information of the object to be measured. Compared with traditional measurement methods, it can fully and carefully describe the shape and size of the object to be measured from a three-dimensional perspective, avoiding inaccurate data caused by the limitations of manual measurement or two-dimensional measurement, and providing more accurate basic data for subsequent detection, especially for objects with complex shapes. It has significant advantages.

[0061] Judging whether the object to be tested is a known test object based on the basic data of the point cloud, and directly calling the corresponding volume parameters and deformation state parameters for known objects can save computing time and resources. Because for some common objects to be tested, the system can pre-store their relevant parameters, without repeated calculations, and can quickly determine the required information, thus improving the efficiency of the entire detection process.

[0062] For unknown objects, the volume parameters are determined according to the number of voxels through image processing technology, and the deformation state parameters are determined in combination with the basic point cloud data, ensuring the adaptability of the system to different types of objects. Even when facing new types of objects that have never been tested, their volume and deformation state parameters can be accurately calculated, expanding the scope of application of the detection system.

[0063] According to the volume parameters and deformation state parameters, the corresponding control parameter information can be determined to achieve precise control of the inflation process. The volume parameters and deformation state parameters can help determine the control parameters such as air pressure, inflation volume, inflation time, etc. suitable for the object to be tested, ensuring that the inflation operation not only meets the test requirements but also does not damage the object to be tested or affect the test results. This precise control helps to ensure the safety of the inflation process and the accuracy of the test, and also provides more stable experimental conditions for subsequent airtightness testing.

[0064] More preferably, the sealing detection method further includes: During the inflation process, the point cloud dynamic data collected by the 3D scanner is obtained, and a voxel correlation relationship between the point cloud basic data and the point cloud dynamic data is constructed; The similarity parameters between the point cloud basic data and the point cloud dynamic data are calculated according to the voxel association, and the similarity parameters are input into a pre-built deformation detection model to determine the corresponding deformation result, and the corresponding deformation result is output.

[0065] During the inflation process, point cloud dynamic data is acquired, and the voxel correlation between the point cloud basic data and the point cloud dynamic data is constructed, so that the geometric shape changes of the measured object during inflation can be grasped in real time. By calculating the similarity parameters between the two, the deformation degree and trend of the measured object can be quantitatively described, providing accurate data basis for subsequent deformation result analysis. This real-time monitoring helps to discover some subtle and imperceptible deformations and improve the ability to perceive the deformation of the measured object.

[0066] By inputting the similarity parameters into the pre-built deformation detection model, the deformation of the object under test can be judged by the powerful analytical ability of the model. Since the model is pre-built, it can be trained based on a large amount of data, and can learn the different deformation states corresponding to different similarity parameters, so as to accurately output the corresponding deformation results. This avoids the subjectivity and inaccuracy of traditional manual judgment, making the judgment of deformation results more objective and scientific. Through accurate judgment of deformation results, abnormal deformation that may be caused by overinflation or structural problems of the object under test can be discovered in advance. For some objects under test that are sensitive to deformation, timely measures can be taken to avoid damage or safety accidents during the detection process, thereby improving the safety and reliability of the entire sealing detection process.

[0067] This technology adds a new dimension to the sealing test, incorporating deformation results into the evaluation system. Combined with the previous pressure test and infrared image test, it can comprehensively evaluate the object under test from multiple angles, providing more abundant information for judging the airtightness of the object under test. For example, abnormal deformation may indicate potential leakage locations or structural defects of the object under test, thus further assisting in judging whether the object under test meets the airtightness requirements.

[0068] More preferably, the controlling the working state of the air source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard includes: Determine the target value of the inflation pressure according to the control parameter information, and measure the actual output pressure of the inflation system in real time through a pressure sensor; Comparing the target value of the inflation pressure with the actual output pressure detected by the pressure sensor to calculate a corresponding error parameter; Calculating a corresponding control amount according to the error parameter and a set of parameters of the PID controller, wherein the set of parameters includes a proportional coefficient, an integral coefficient and a differential coefficient; The calculated control amount is used to adjust the inflation parameters of the inflation system so that the actual measured value gradually approaches the target value and the inflation operation is performed on the object to be measured.

[0069] The embodiment of the present invention measures the actual output pressure of the inflation system in real time through a pressure sensor, and compares it with the target value of the inflation pressure, thereby realizing real-time monitoring of the inflation process. This real-time monitoring can ensure that during the inflation operation, the system always grasps the actual situation of the inflation pressure, avoids the errors that may occur when only relying on the set value for inflation, and provides an accurate data basis for subsequent pressure adjustments. For example, when inflating some pressure-sensitive objects, accurate pressure monitoring can prevent the pressure from being too high to cause damage to the object being tested or the pressure from being too low to meet the test requirements.

[0070] Calculating the error parameter between the target value of the inflation pressure and the actual output pressure can quantify the pressure deviation. This provides a clear basis for subsequent pressure control adjustments, allowing the system to clearly understand the gap between the current inflation pressure and the target pressure, which helps to adopt targeted control strategies in the future.

[0071] The control quantity is calculated based on the error parameter and the parameter set of the PID controller (including the proportional coefficient, integral coefficient and differential coefficient), which can achieve precise control of the inflation system. Proportional (P) control: The proportional coefficient can adjust the inflation system according to the current error, so that the inflation system responds quickly. The larger the error, the larger the adjustment range, which helps to quickly reduce the deviation. Integral (I) control: The integral coefficient takes into account the cumulative effect of the error and can eliminate the steady-state error of the system. For small errors that exist for a long time, integral control can continuously adjust the output of the system, and eventually make the actual pressure reach the target pressure, avoiding long-term deviations in the system. Differential (D) control: The differential coefficient is adjusted according to the rate of change of the error, which helps to predict the trend of the error, adjust the inflation system in advance, prevent overshoot, and make the system more stable.

[0072] The calculated control quantity is used to adjust the inflation parameters of the inflation system, so that the actual measured value gradually approaches the target value and the measured object is inflated, realizing a dynamic and continuously optimized inflation process. This dynamic adjustment can make the inflation operation more stable and accurate. No matter what kind of interference or system fluctuation occurs during the inflation process, it can be continuously adjusted through the PID controller to ensure the stability and accuracy of the inflation process. For measured objects of different volumes and materials, efficient, stable and accurate inflation operations can be achieved according to the set control parameter information and the adjustment of the PID controller, which improves the adaptability and reliability of the inflation process.

[0073] In the embodiment of the invention, fuzzy calculation is also combined to realize the input optimization of the PID controller. Input variable: The error of gas pressure and the error change rate are selected as the input of the fuzzy controller. Output variable: The output of the fuzzy controller is the increment Δu of the inflation valve flow correction amount, which is used to adjust the input parameters of the PID controller. Fuzzification process: The input variables are fuzzified to determine the fuzzy level and membership function. Fuzzy rules: A fuzzy rule base is established, and the fuzzy value of the output variable is obtained through fuzzy reasoning based on the fuzzy value of the input variable. Defuzzification: The fuzzy value of the output variable is defuzzified using the center of gravity method or the maximum membership function method to obtain accurate correction amounts ΔK_p, ΔK_i, and ΔK_d.

[0074] The artificial intelligence-based sealing detection method in the embodiment of the present invention obtains information about the object to be tested and automatically determines control parameters based on this information, thereby controlling the working state of the air source component and the inflation proportional valve. This process is highly automated, reduces reliance on manual operations, and improves detection efficiency. At the same time, the infrared detection image is recognized using an airtightness detection model, further improving the intelligent level of detection.

[0075] Embodiment 2 See also Figure 5 , Figure 5 Schematic diagram of the structure of the sealing detection system based on artificial intelligence disclosed in the embodiment of the present invention. Figure 5 As shown, the sealing detection system based on artificial intelligence may include: Acquisition module 21: used to acquire information of the object to be measured, and determine corresponding control parameter information according to the volume of the object to be measured and the deformation parameters of the object to be measured; Inflating module 22: used to control the working state of the air source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard, wherein the inflating operation on the object to be measured includes inflating the cavity in which the object to be measured is placed or inflating the cavity of the object to be measured; The pressure detection module 23 is used to obtain the pressure change information of the object under test within a period of time through the pressure sensor after the inflation operation is completed. If the pressure change information is less than the first set threshold, it is determined that the air tightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If the pressure change information is greater than the first set threshold, the next step is executed; Infrared detection module 24: used to obtain the infrared detection image of the object to be tested taken by the infrared camera, and input the infrared detection image into the airtightness detection model for identification to determine the airtightness status of the corresponding object to be tested, and output the corresponding airtightness status.

[0076] The artificial intelligence-based sealing detection method in the embodiment of the present invention obtains information about the object to be tested and automatically determines control parameters based on this information, thereby controlling the working state of the air source component and the inflation proportional valve. This process is highly automated, reduces reliance on manual operations, and improves detection efficiency. At the same time, the infrared detection image is recognized using an airtightness detection model, further improving the intelligent level of detection.

[0077] Embodiment 3 See also Figure 6 , Figure 6 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Figure 6 As shown, the electronic device may include: A memory 510 storing executable program codes; a processor 520 coupled to the memory 510; The processor 520 calls the executable program code stored in the memory 510 to execute part or all of the steps in the artificial intelligence-based sealing detection method in the first embodiment.

[0078] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the artificial intelligence-based sealing detection method in Embodiment 1.

[0079] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the artificial intelligence-based sealing detection method in the first embodiment.

[0080] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes part or all of the steps in the artificial intelligence-based sealing detection method in embodiment one.

[0081] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0082] 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, i.e., 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 present embodiment.

[0083] 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. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0084] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, server or network device, etc., specifically a processor in a computer device) to perform some or all steps of the method described in each embodiment of the present invention.

[0085] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0086] A person of ordinary skill in the art may understand that part or all of the steps in the various methods of the embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, the storage medium including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically-erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0087] The above is a detailed introduction to the artificial intelligence-based sealing detection method, system, electronic device and storage medium disclosed in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A sealing detection method based on artificial intelligence, characterized in that: include: Acquire information of the object to be measured, and determine corresponding control parameter information according to the volume of the object to be measured and the deformation parameters of the object to be measured; Controlling the working state of the gas source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard, wherein the inflation operation on the object to be measured includes inflating the cavity in which the object to be measured is placed or inflating the cavity of the object to be measured; After the inflation operation is completed, the pressure change information of the object under test within a period of time is obtained through the pressure sensor. If the pressure change information is less than the first set threshold, it is determined that the air tightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If the pressure change information is greater than the first set threshold, the next step is executed; An infrared detection image of the object to be tested taken by an infrared camera is obtained, and the infrared detection image is input into the airtightness detection model for identification to determine the airtightness status of the corresponding object to be tested, and the corresponding airtightness status is output.

2. The artificial intelligence-based sealing detection method according to claim 1, characterized in that: The method of obtaining an infrared detection image of the object to be tested taken by an infrared camera, inputting the infrared detection image into an airtightness detection model for identification to determine the airtightness state of the corresponding object to be tested, and outputting the corresponding airtightness state includes: Acquire a detection image information set of the object under test captured by an infrared camera within a period of time, and input each detection image information in the detection image information set into a temperature detection model for detection to obtain the temperature state change at each time point; The temperature state change is input into the airtight timing detection model for identification to determine whether the object under test meets the airtightness requirements. If so, it is determined that the airtightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If not, it is determined that the corresponding object under test does not meet the airtightness requirements and is determined to be in an unqualified state, and the airtightness detection result of the corresponding leakage point is output, and the airtightness detection result includes the leakage point location information and the leakage point size information.

3. The sealing detection method based on artificial intelligence as claimed in claim 2, characterized in that: Before acquiring the detection image information set of the object to be detected captured by the infrared camera within a period of time, the method further includes: Acquire the image information of the object to be tested captured by the camera, and input the image information of the object to be tested into the detection and recognition model to determine the position information of each detection point in the image information of the object to be tested; or, transmit the image information of the object to be tested captured by the camera to the user end, so that the user can circle the corresponding detection area on the user end, and transmit the corresponding detection area to the detection end; The step of inputting each detection image information in the detection image information set into the airtightness detection model for detection to obtain leakage state changes at each time point includes: Determine the corresponding infrared detection area of ​​each detection image information in the detection image information set according to the position information or the detection area of ​​each detection point; The infrared detection area is input into the airtightness detection model for detection to determine the corresponding temperature change information.

4. The sealing detection method based on artificial intelligence according to claim 1, characterized in that: The step of acquiring an infrared detection image of the object to be tested taken by an infrared camera and inputting the infrared detection image into an airtightness detection model for identification to determine the airtightness state of the corresponding object to be tested also includes: Acquire an infrared detection image of the object to be detected captured by an infrared camera, and process the infrared detection image to obtain preprocessed infrared detection data; Determine the temperature parameter of each pixel in the infrared detection data, and determine the grayscale difference parameter and the gradient difference parameter of adjacent pixels according to the temperature parameter of each pixel; According to the temperature parameters of each pixel point, the grayscale difference parameter and the gradient difference parameter of the corresponding data point and the adjacent pixel point are determined to determine the corresponding change node information; Determine the change parameter information between each pixel and other pixel points according to the change node information of each pixel point in the obtained infrared detection data, and determine the association information between each pixel point and other pixel point areas according to the change parameter information; The infrared detection data is clustered according to the association information between each pixel point and other pixel point areas to obtain a clustered image result, and feature extraction is performed on the image result to obtain corresponding identification features, which are input into the airtightness detection model for identification to determine the airtightness status of the corresponding object under test.

5. The sealing detection method based on artificial intelligence according to claim 1, characterized in that: The step of obtaining the measured object information and determining corresponding control parameter information according to the measured object volume and the measured object deformation parameters of the measured object information includes: The point cloud basic data of the object to be measured is obtained through a 3D scanner, and the voxels occupied by the object to be measured are identified through image processing technology, and the number of voxels is calculated; Determine whether the object to be tested is a known test object according to the point cloud basic data. If so, directly retrieve the volume parameters and deformation state parameters associated with the corresponding object to be tested. If not, identify the number of voxels occupied by the object to be tested through image processing technology, determine its volume parameters according to the number of voxels, and determine the corresponding deformation state parameters according to the volume parameters and the point cloud basic data. The deformation state parameters refer to the influence of air pressure parameters on the deformation of the object to be tested. Corresponding control parameter information is determined according to the volume parameters and the deformation state parameters.

6. The artificial intelligence-based sealing detection method according to claim 5, characterized in that: The sealing detection method further comprises: During the inflation process, the point cloud dynamic data collected by the 3D scanner is obtained, and a voxel correlation relationship between the point cloud basic data and the point cloud dynamic data is constructed; The similarity parameters between the point cloud basic data and the point cloud dynamic data are calculated according to the voxel association, and the similarity parameters are input into a pre-built deformation detection model to determine the corresponding deformation result, and the corresponding deformation result is output.

7. The sealing detection method based on artificial intelligence according to claim 1, characterized in that: The controlling the working state of the gas source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard includes: Determine the target value of the inflation pressure according to the control parameter information, and measure the actual output pressure of the inflation system in real time through a pressure sensor; Comparing the target value of the inflation pressure with the actual output pressure detected by the pressure sensor to calculate a corresponding error parameter; Calculating a corresponding control amount according to the error parameter and a set of parameters of the PID controller, wherein the set of parameters includes a proportional coefficient, an integral coefficient and a differential coefficient; The calculated control amount is used to adjust the inflation parameters of the inflation system so that the actual measured value gradually approaches the target value and the inflation operation is performed on the object to be measured.

8. A sealing detection system based on artificial intelligence, characterized in that: include: Acquisition module: used to acquire information of the object to be measured, and determine corresponding control parameter information according to the volume of the object to be measured and the deformation parameters of the object to be measured; Inflation module: used to control the working state of the air source component and the inflation proportional valve according to the control parameter information to inflate the object to be measured according to the set inflation standard, wherein the inflation operation on the object to be measured includes inflating the cavity in which the object to be measured is placed or inflating the cavity of the object to be measured; Pressure detection module: used to obtain the pressure change information of the object under test within a period of time through the pressure sensor after the inflation operation is completed. If the pressure change information is less than the first set threshold, it is determined that the air tightness of the corresponding object under test meets the requirements and is determined to be in a qualified state. If the pressure change information is greater than the first set threshold, the next step is executed; Infrared detection module: used to obtain the infrared detection image of the object under test taken by the infrared camera, and input the infrared detection image into the airtightness detection model for identification to determine the airtightness status of the corresponding object under test, and output the corresponding airtightness status.

9. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the artificial intelligence-based sealing detection method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the artificial intelligence-based sealing detection method according to any one of claims 1 to 7.

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