Waterproof Performance Detection Method and System for Type-C Interface

Abstract

The present invention discloses a waterproof performance detection method and system for a Type-C interface, which relates to the technical field of interface adapters. The method includes: obtaining a waterproof performance detection device; constructing an interface attribute classifier to obtain a set of interface attribute parameter to be detected; obtaining a dual-channel interface detection according to a control processor; performing matching and parsing on the set of interface attribute parameter to be detected; placing the Type-C interface to be detected in an airtightness test chamber, and performing airtightness detection control according to a waterproof performance detection parameter curve to obtain a pressure change test data stream; migrating to obtain a waterproof performance evaluator for the Type-C interface, and evaluating the waterproof performance of the pressure change test data stream to obtain a waterproof performance detection result. It solves the technical problem in the prior art that it is difficult to match detection parameters efficiently and accurately, resulting in low accuracy and poor efficiency of waterproof performance detection, and achieves the technical effect of improving the detection efficiency, accuracy and reliability of the detection result of the interface waterproof performance.

Description

Technical Field

[0001] This application relates to the technical field of interface adapters, and specifically relates to a waterproof performance detection method and system for Type-C interfaces. Background Art

[0002] With the popularization of smart devices and the continuous development of electronic products towards miniaturization and high performance, the design and protection performance of connection ports have become increasingly important. As a new type of connection port with high transmission rate and high current-carrying capacity, the Type-C interface has been widely used in various electronic devices such as smartphones, laptops, chargers, and headphones. However, with the increasing complexity of the usage environment, how to ensure the reliability of the Type-C interface in harsh environments such as humidity and rain has become a top priority. The waterproof performance of the Type-C interface is directly related to the reliability and service life of the device, which not only depends on the design of the interface itself but also is closely related to the overall sealing performance of the device. Current interface waterproof performance detection methods, such as tests based on the immersion method and detection by the spraying method, usually have difficulty simulating the complex water environment in actual use scenarios, cannot perform refined detection on the sealing performance of the interface, and lack efficient and accurate automatic control means, resulting in a complex detection process, poor detection efficiency, and being easily affected by human factors, making the accuracy of the water resistance performance detection result not high and unable to guarantee the reliability of the Type-C interface usage.

[0003] Therefore, in the current related technologies, there are technical problems such as difficulty in efficiently and accurately matching detection parameters, resulting in low accuracy and poor efficiency of waterproof performance detection. Summary of the Invention

[0004] By providing a waterproof performance detection method and system for Type-C interfaces, this application solves the technical problems in the prior art of difficulty in efficiently and accurately matching detection parameters, resulting in low accuracy and poor efficiency of waterproof performance detection, and achieves the technical effects of improving the detection efficiency, accuracy, and reliability of the interface waterproof performance detection result.

[0005] This application provides a waterproof performance detection method for a Type-C interface. The method includes: obtaining a waterproof performance detection device, which includes an airtightness test chamber, a Hall pressure sensor, a control processor, and an automatic fixture; constructing an interface attribute classifier, classifying the attribute information of the Type-C interface to be detected based on the interface attribute classifier, and obtaining a set of interface attribute parameters to be detected; obtaining an interface detection dual channel according to the control processor, where the interface detection dual channel includes a conventional detection channel and a special detection channel; performing matching and parsing on the set of interface attribute parameters to be detected based on the interface detection dual channel, and obtaining a waterproof performance detection parameter curve; placing the Type-C interface to be detected in the airtightness test chamber, fixing the Type-C interface to be detected through the automatic fixture, performing airtightness detection control on the Type-C interface to be detected according to the waterproof performance detection parameter curve, and simultaneously monitoring and obtaining a pressure change test data stream through the Hall pressure sensor; migrating to obtain a Type-C interface waterproof performance evaluator, and performing waterproof performance evaluation on the pressure change test data stream based on the Type-C interface waterproof performance evaluator to obtain a waterproof performance detection result of the Type-C interface.

[0006] In a possible implementation, the waterproof performance detection method for the Type-C interface further performs the following processing: obtaining interface attribute factor information, where the interface attribute factor information includes size specifications, number of pins, functional attributes, application attributes, and waterproof level requirements; mining and obtaining a Type-C interface product database, extracting attribute features from the Type-C interface product database based on the interface attribute factor information, and obtaining a set of interface product attribute features; using a decision tree to perform feature segmentation and recursive construction on the set of interface product attribute features to obtain an initial interface attribute classification tree; performing classification prediction verification and parameter strategy tuning on the initial interface attribute classification tree to construct the interface attribute classifier.

[0007] In a possible implementation, the waterproof performance detection method for the Type-C interface further performs the following processing: classifying and labeling the Type-C interface product database according to the Type-C interface production standard to obtain a conventional Type-C interface product dataset and a special Type-C interface product dataset; performing airtightness detection data crawling based on the conventional Type-C interface product dataset and the special Type-C interface product dataset to obtain a conventional interface airtightness detection dataset and a special interface airtightness detection dataset; performing airtightness detection analysis on the conventional interface airtightness detection dataset and the special interface airtightness detection dataset respectively to obtain a conventional detection channel and a special detection channel; connecting the conventional detection channel and the special detection channel in parallel to obtain an interface detection dual channel and storing it in the control processor.

[0008] In a possible implementation, the waterproof performance detection method for the Type-C interface further performs the following processing: performing optimal extraction of detection parameters on the conventional interface airtightness detection data set to obtain an available interface airtightness detection data set; performing correlation analysis on the interface attribute data and the airtightness detection parameters in the available interface airtightness detection data set to generate an interface attribute-airtightness detection parameter correlation model; configuring a conventional interface detection channel based on the interface attribute-airtightness detection parameter correlation model to obtain the conventional detection channel; and performing airtightness detection analysis based on the special interface airtightness detection data set and the interface attribute-airtightness detection parameter correlation model to obtain the special detection channel.

[0009] In a possible implementation, the waterproof performance detection method for the Type-C interface further performs the following processing: extracting special attribute data and correlation detection parameters from the special interface airtightness detection data set to obtain special interface detection feature data; using the interface attribute-airtightness detection parameter correlation model as a basic model framework to perform training loss analysis on the special interface detection feature data to obtain detection analysis loss data; performing incremental training and updating on the interface attribute-airtightness detection parameter correlation model based on the detection analysis loss data to obtain a special interface attribute-airtightness detection correlation update model; and configuring a special interface detection channel based on the special interface attribute-airtightness detection correlation update model to obtain the special detection channel.

[0010] In a possible implementation, the waterproof performance detection method for the Type-C interface further performs the following processing: selecting a source domain interface waterproof performance evaluation data set, where the source domain interface waterproof performance evaluation data set includes waterproof performance evaluation data sets for different types of interfaces; respectively performing evaluation training on the waterproof performance evaluation data sets for different types of interfaces to obtain a source domain interface waterproof performance evaluation model set; obtaining a Type-C interface waterproof performance evaluation target domain, and determining a transfer learning strategy according to the source domain interface waterproof performance evaluation model set and the Type-C interface waterproof performance evaluation target domain; and performing transfer training and optimization on the Type-C interface waterproof performance evaluation data set and the source domain interface waterproof performance evaluation model set based on the transfer learning strategy to obtain the Type-C interface waterproof performance evaluator.

[0011] In a possible implementation, the waterproof performance detection method of the Type-C interface further performs the following processing: extracting the model parameters of the waterproof performance evaluation model set of the source domain interface respectively to obtain a source domain model parameter set; performing selective transfer fine-tuning on the source domain model parameter set based on the transfer learning strategy to obtain basic evaluation model structure parameters; using the basic evaluation model structure parameters to perform performance evaluation and model tuning on the Type-C interface waterproof performance evaluation data set to obtain the Type-C interface waterproof performance evaluator.

[0012] The present application also provides a waterproof performance detection system for a Type-C interface, including: a waterproof performance detection device acquisition module, configured to acquire a waterproof performance detection device, where the waterproof performance detection device includes an airtightness test chamber, a Hall pressure sensor, a control processor, and an automatic fixture; an interface attribute classifier construction module, configured to construct an interface attribute classifier, and classify the attribute information of the Type-C interface to be detected based on the interface attribute classifier to obtain a to-be-detected interface attribute parameter set; an interface detection dual-channel acquisition module, configured to obtain an interface detection dual-channel according to the control processor, where the interface detection dual-channel includes a conventional detection channel and a special detection channel; a parameter matching and parsing module, configured to perform matching and parsing on the to-be-detected interface attribute parameter set based on the interface detection dual-channel to obtain a waterproof performance detection parameter curve; an airtightness detection control module, configured to place the to-be-detected Type-C interface in the airtightness test chamber, and fix the to-be-detected Type-C interface through the automatic fixture, and perform airtightness detection control on the to-be-detected Type-C interface according to the waterproof performance detection parameter curve, and simultaneously monitor and acquire a pressure change test data stream through the Hall pressure sensor; a waterproof performance evaluation module, configured to migrate to obtain a Type-C interface waterproof performance evaluator, and perform waterproof performance evaluation on the pressure change test data stream based on the Type-C interface waterproof performance evaluator to obtain a Type-C interface waterproof performance detection result.

[0013] A waterproof performance detection method and system for a Type-C interface proposed in this application are used to obtain a waterproof performance detection device; an interface attribute classifier is constructed to obtain a set of interface attribute parameter to be detected; an interface detection dual channel is obtained according to a control processor; the set of interface attribute parameter to be detected is matched and parsed; the Type-C interface to be detected is placed in an airtightness test chamber, and airtightness detection control is performed according to a waterproof performance detection parameter curve to obtain a pressure change test data stream; a waterproof performance evaluator for the Type-C interface is migrated to evaluate the waterproof performance of the pressure change test data stream to obtain a waterproof performance detection result. This solves the technical problem in the prior art that it is difficult to efficiently and accurately match detection parameters, resulting in low waterproof performance detection accuracy and poor efficiency, and achieves the technical effect of improving the detection efficiency, accuracy and reliability of the interface waterproof performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure will be briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the operations described above or below do not necessarily need to be executed precisely in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 Schematic flowchart of the waterproof performance detection method for the Type-C interface provided by the embodiment of the present application;

[0016] Figure 2 Schematic structural diagram of the waterproof performance detection system for the Type-C interface provided by the embodiment of the present application.

[0017] Description of reference numerals: Waterproof performance detection device acquisition module 10, interface attribute classifier construction module 20, interface detection dual channel acquisition module 30, parameter matching and parsing module 40, airtightness detection control module 50, waterproof performance evaluation module 60. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The above description is only an overview of the technical solution of this application. In order to be able to understand the technical means of this application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of this application more obvious and understandable, the following specifically gives the detailed implementation of this application.

[0019] To make the objectives, technical solutions, and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be construed as limiting the present application. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first / second" merely distinguish similar objects and do not represent a specific order for the objects. The terms "including" and "having", and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0021] The embodiments of the present application provide a method for detecting the waterproof performance of a Type-C interface, as Figure 1 shown. The method includes:

[0022] Step S100, obtaining a waterproof performance detection device, where the waterproof performance detection device includes an airtightness test chamber, a Hall pressure sensor, a control processor, and an automatic fixture.

[0023] Preferably, obtain a waterproof performance detection device. Among them, the waterproof performance detection device is a comprehensive test equipment for detecting the waterproof performance of the Type-C interface, including an airtightness test chamber, a Hall pressure sensor, a control processor, and an automatic fixture. Specifically, the airtightness test chamber is a closed test area for detecting the airtightness of the Type-C interface. The gas flow in the test chamber can be controlled to simulate the penetration situation, so as to detect whether the interface can effectively prevent the entry of water and gas, providing a closed and controlled environment for the test to ensure the accurate testing of the waterproof performance of the interface; the Hall pressure sensor is a pressure sensor based on the Hall effect principle. By detecting the pressure change generated by gas or liquid in the airtightness test chamber, it obtains the data of the interface waterproof performance, and can accurately measure the air pressure change caused by the interface sealing problem, thus helping to judge the waterproof ability of the interface; the control processor is the brain of the waterproof performance detection device, responsible for coordinating the work of each part, collecting and processing the detection data, and controlling the test process according to the preset detection standards to ensure the automation of the detection process and generate the corresponding detection results; the automatic fixture is used to fix the Type-C interface to be detected during the test, ensuring the stable position of the interface, avoiding human interference, ensuring the consistency of the interface position during each test, and avoiding test errors caused by the change of the interface position. Through the collaborative work of these components, the waterproof performance of the Type-C interface can be accurately and automatically tested in a controlled environment, providing reliable test data.

[0024] Step S200: Construct an interface attribute classifier, classify the attribute information of the Type-C interface to be detected based on the interface attribute classifier, and obtain a set of interface attribute parameters to be detected.

[0025] Preferably, construct an interface attribute classifier based on a decision tree for analyzing and classifying the attribute information of the Type-C interface to be detected. Specifically, the interface attribute classifier classifies the Type-C interface to be detected according to different attributes of the interface (such as size, number of pins, function, application requirements, and waterproof level, etc.), and then obtains a set of interface attribute parameters to be detected. Among them, the interface attribute information includes the size specification, number of pins, functional attribute, application attribute, and waterproof level requirement of the Type-C interface. After being classified by the interface attribute classifier, each interface to be detected will have a set of specific parameter data. The set of interface attribute parameters to be detected can include the physical characteristics, electrical characteristics, functional requirements, and environmental adaptability of the interface, etc., ensuring the selection of corresponding detection standards and processes according to the characteristics of different interfaces, and improving the pertinence and accuracy of the detection.

[0026] Further, step S200 further includes step S210 of obtaining interface attribute factor information, where the interface attribute factor information includes dimension specifications, number of pins, functional attributes, application attributes, and waterproof level requirements; step S220 of mining and obtaining a Type-C interface product database, and extracting attribute features from the Type-C interface product database based on the interface attribute factor information to obtain an interface product attribute feature dataset; step S230 of using a decision tree to perform feature segmentation and recursive construction on the interface product attribute feature dataset to obtain an initial interface attribute classification tree; and step S240 of performing classification prediction verification and parameter strategy optimization on the initial interface attribute classification tree to construct the interface attribute classifier.

[0027] Preferably, the interface attribute factor information includes dimension specifications (the physical dimensions of the Type-C interface, such as length, width, and height, etc.), number of pins (the number of available pins on the Type-C interface for different functions such as electrical connection, data transmission, power supply, etc.), functional attributes (the functional characteristics of the interface, such as whether it supports high-speed data transmission, fast charging, video output, etc.), application attributes (the specific device types and scenarios to which the interface is applied, such as mobile phones, laptops, chargers, audio devices, etc.), and waterproof level requirements (the specific requirements for waterproofing of the interface, for example, whether the interface needs to reach a certain specific waterproof level, such as IP67 or IP68 level). Among them, a large amount of Type-C interface product information is obtained through data mining technology to form a Type-C interface product database, which contains information such as different attributes, technical parameters, and functional requirements of each Type-C interface. Then, attribute features are extracted from the Type-C interface product database according to the interface attribute factor information, that is, the attribute features of the Type-C interface products are extracted from the Type-C interface product database as the interface product attribute feature set, which contains various attribute features of different Type-C interface products. The rows represent different interface products, and the columns represent different attribute features.

[0028] Preferably, the feature segmentation and recursive construction of the interface product attribute feature dataset are carried out according to the decision tree. Specifically, each segmentation selects an optimal segmentation feature (such as size, number of pins, waterproof level, etc.), so that the segmented sub-dataset is purer in this feature (for example, similar waterproof level requirements in the same type of dataset). For example, calculate information gain, gain ratio, etc. to determine the best segmentation feature. Recursive construction means starting from the root node, dividing the data layer by layer. Each segmentation point forms a tree node, and the segmented sub-dataset will be further recursively divided until the conditions are met, that is, the dataset only contains data of the same category, or no effective segmentation can be performed, or the data segmentation has reached the set purity threshold, and then the initial interface attribute classification tree is obtained. Then, classification prediction verification and parameter strategy tuning are carried out on the initial interface attribute classification tree. Specifically, classification prediction verification refers to using a part of the dataset (usually called the validation set) to test the classification ability of the initial classification tree, and measuring the performance of the classification tree by whether the prediction verification model can accurately distinguish different types of interface products, that is, discovering the deficiencies of the classification tree, such as whether there is overfitting (the model performs well on the training data but poorly on the unseen data). Then, by optimizing the construction parameters of the decision tree (such as limiting the maximum depth of the classification tree, setting the minimum sample segmentation number, controlling the minimum number of samples in the leaf node, etc.), improving its classification performance, avoiding overfitting and underfitting, and finally constructing an interface attribute classifier that can accurately classify according to the attribute features of the Type-C interface (such as size specifications, functional attributes, waterproof level, etc.), and automatically assign attribute categories to each Type-C interface to be detected, facilitating the subsequent detection process (such as waterproof performance detection) to adapt to specific test methods.

[0029] Step S300, obtain an interface detection dual channel according to the control processor, where the interface detection dual channel includes a regular detection channel and a special detection channel.

[0030] Preferably, by controlling the processor, two different types of detection paths (interface detection dual channels) are constructed, namely a conventional detection channel and a special detection channel, which are respectively used to meet the detection requirements of the Type-C interface under different conditions, thereby improving the flexibility and efficiency of detection, enabling the detection process to adapt to various interface attributes and scenario requirements. Among them, by real-time controlling the working parameters of the test equipment (such as the airtightness test chamber, Hall pressure sensor, etc.), it is ensured that the detection process meets the preset channel requirements. Specifically, the conventional detection channel is a standardized detection channel for the Type-C interface, applicable to most interface products with conventional attributes, providing a set of preset and standardized detection parameters and methods for detecting the basic waterproof performance of the interface, including tests on the airtightness of the interface, the tightness of the pins, the pressure tolerance ability, etc. For example, for a Type-C interface with a waterproof grade requirement of IP67, the conventional detection channel may include a pressure test of the airtightness test chamber and perform airtightness detection according to a fixed pressure curve and time parameters; the special detection channel is a detection channel designed for Type-C interfaces with special attribute requirements or application scenarios, allowing targeted detection according to a specific set of interface attribute parameters (such as higher waterproof grade requirements, specific function requirements, etc.), supporting flexible adjustment of detection parameters, such as pressure range, detection time, ambient temperature or humidity, etc., to meet the needs of special interfaces, and can be used to test interface products with non-standard specifications, such as interfaces with non-standard pin numbers and special functions; by dividing the detection process into a conventional detection channel and a special detection channel, the detection system can adapt to Type-C interfaces with different attributes and requirements. The conventional detection channel focuses on efficiency and standardization, while the special detection channel provides flexibility and pertinence, and can cover a wide range of detection requirements.

[0031] Further, step S300 further includes step S310, classifying and identifying the Type-C interface product database according to the Type-C interface production standard to obtain a conventional Type-C interface product data set and a special Type-C interface product data set; step S320, crawling airtightness detection data based on the conventional Type-C interface product data set and the special Type-C interface product data set to obtain a conventional interface airtightness detection data set and a special interface airtightness detection data set; step S330, respectively performing airtightness detection analysis based on the conventional interface airtightness detection data set and the special interface airtightness detection data set to obtain a conventional detection channel and a special detection channel; step S340, connecting the conventional detection channel and the special detection channel in parallel to obtain an interface detection dual channel and storing it in the control processor.

[0032] Preferably, the Type-C interface production standard is used to guide the production and detection of Type-C interfaces, including technical indicators such as the size specifications of the interfaces, the number of pins, and the waterproof level requirements. It may stipulate which interfaces can be classified as "conventional interfaces" (such as interfaces that meet the general usage specifications) and "special interfaces" (such as interfaces with unconventional attributes or designed for specific purposes). Extract the attribute information of each interface product (such as physical specifications, functional requirements, waterproof level, etc.) from the Type-C interface product database. According to these attribute information and the production standard, the products are classified into two major categories: the conventional Type-C interface product dataset, which contains interfaces that meet the general specifications, such as standard interfaces applicable to smartphones and laptops; and the special Type-C interface product dataset, which contains interfaces with special requirements, such as high waterproof level requirements, interfaces for industrial equipment, or non-standard size interfaces. Then, crawl the airtightness detection data, that is, collect the airtightness test data related to conventional and special interfaces from the existing detection records, including test conditions (such as pressure range, time), test results (such as pressure holding time, leakage rate), and the mapping relationship between interface attributes and test data, to obtain the conventional interface airtightness detection dataset (the airtightness detection data of conventional Type-C interfaces under standard test conditions, such as the performance of pressure holding ability under IP67 conditions) and the special interface airtightness detection dataset (the airtightness detection data of special Type-C interfaces under complex or non-standard conditions, such as IP68 test conditions or dynamic pressure test data).

[0033] Preferably, perform airtightness detection analysis respectively according to the obtained conventional interface airtightness detection dataset and special interface airtightness detection dataset, that is, analyze (such as analyze, model) the crawled detection data, and extract the rules and key parameters of the detection process. Specifically, analyze the conventional interface airtightness detection dataset, extract the standard detection process and parameters applicable to conventional interfaces, and construct a conventional detection channel. Analyze the special interface airtightness detection dataset, extract complex or non-standard detection processes and parameters, and construct a special detection channel. Finally, connect the parsed conventional detection channel and special detection channel in parallel to form an interface detection dual-channel, that is, the conventional channel and the special channel exist simultaneously and can be dynamically switched according to the interface attributes; the control processor can automatically select the appropriate detection channel according to the classification (conventional or special) of the interface to be detected; by splitting the detection tasks, the detection efficiency is improved while meeting the diverse interface requirements; store the interface detection dual-channel in the control processor, and the control processor can retrieve the corresponding classification information from the database according to the attributes of the interface to be detected, and dynamically select the applicable detection channel to control the operation of the detection equipment (such as airtightness test chamber, pressure sensor), ensure that the detection process meets the preset parameters, and thus achieve automated detection, improve the detection efficiency and ensure the consistency and reliability of the detection results.

[0034] Further, step S330 further includes step S331 of preferentially extracting detection parameters from the conventional interface airtightness detection data set to obtain an available interface airtightness detection data set; step S332 of performing correlation analysis on the interface attribute data and the airtightness detection parameters in the available interface airtightness detection data set to generate an interface attribute-airtightness detection parameter correlation model; step S333 of configuring a conventional interface detection channel based on the interface attribute-airtightness detection parameter correlation model to obtain the conventional detection channel; and step S334 of performing airtightness detection analysis based on the special interface airtightness detection data set and the interface attribute-airtightness detection parameter correlation model to obtain the special detection channel.

[0035] Preferably, for preferentially extracting detection parameters from the conventional interface airtightness detection data set, that is, screening out the relatively optimal detection parameters that have the most crucial influence on the interface airtightness performance from the detection data set, such as pressure value, detection time, ambient temperature, etc., to form an available interface airtightness detection data set containing high-quality and key detection parameters, and then performing correlation analysis on the interface attribute data and the airtightness detection parameters. Specifically, for each interface in the available interface airtightness detection data set, analyze the relationship between its attribute data and the corresponding airtightness detection parameters, and generate an interface attribute-airtightness detection parameter correlation model through data analysis or machine learning methods (such as regression analysis, classification models, etc.), which is used to describe the internal relationship between the physical attributes of the interface and its airtightness performance (such as leakage rate under different pressures, pressure holding ability, etc.), and can predict the parameters (such as maximum pressure, test time, etc.) that may be required during the airtightness test according to the input interface attributes (such as size, number of pins, waterproof level, etc.).

[0036] Preferably, an interface property - airtightness detection parameter correlation model is used. Combining with the property data of conventional interfaces (such as the size and waterproof level of a standard IP67 interface), appropriate detection channels are configured, including the pressure range, detection duration of the detection channels, and setting environmental conditions (temperature, humidity, etc.), to ensure that the detection process of conventional interfaces can be completed efficiently and accurately under predetermined conditions, obtaining conventional detection channels, which can automatically perform airtightness detection for different types of conventional interfaces and provide a standardized detection process; Similarly, using the interface property - airtightness detection parameter correlation model, the airtightness detection data of special interfaces is analyzed. The best test parameters (such as pressure, time, etc.) of special interfaces are predicted through the correlation model, and a detection channel suitable for special interfaces is constructed based on the results output by the model. Based on the analysis results, a special detection channel for special interfaces is generated. The configuration of the special detection channel may include simulating dynamic pressure changes (such as periodic pressure fluctuations), adapting to the temperature change range (such as performing detection in high - temperature or low - temperature environments), so as to be able to perform airtightness detection of different types of interfaces efficiently and accurately, and ensure the waterproof performance of the interfaces in different application scenarios.

[0037] Further, step S334 further includes step A: extracting special attribute data and associated detection parameters from the special interface airtightness detection data set to obtain special interface detection feature data; step B: using the interface property - airtightness detection parameter correlation model as the basic model framework, performing training loss analysis on the special interface detection feature data to obtain detection analysis loss data; step C: incrementally training and updating the interface property - airtightness detection parameter correlation model based on the detection analysis loss data to obtain a special interface property - airtightness detection association update model; step D: configuring a special interface detection channel based on the special interface property - airtightness detection association update model to obtain the special detection channel.

[0038] Preferably, the extraction of special attribute data and associated detection parameters from the airtightness detection dataset of special interfaces includes extracting attribute data related to special interfaces from the dataset (such as non-standard size interfaces, waterproof levels higher than conventional interfaces, etc.), and extracting the detection parameters corresponding to these special attributes (such as the pressure range for high waterproof levels, dynamic pressure changes, temperature change range adaptation, etc.). Combining these data serves as the detection feature data for special interfaces. Using the constructed interface attribute - airtightness detection parameter association model as the basic model framework (general model structure and initial parameters), perform training loss analysis on the detection feature data of special interfaces, that is, use the detection feature data of special interfaces to train the basic model and analyze the loss of the model when processing these data. Here, the loss is the error between the model prediction value and the true detection parameter value, reflecting the adaptability of the model to the detection feature data of special interfaces, and obtaining the detection analysis loss data, which represents the quantification of the model's deficiencies, including which features and detection parameter predictions have large errors.

[0039] Preferably, perform incremental training and update based on the detection analysis loss data, that is, optimize and train the basic model. Use the detection analysis loss data as a guide to dynamically adjust the weights and structure of the model to better adapt to the detection characteristics of special interfaces, optimize the model so that it can accurately predict the detection parameters of special interfaces, and finally obtain the special interface attribute - airtightness detection association update model, which can better adapt to the special interface attributes and detection requirements. Then, use the special interface attribute - airtightness detection association update model to configure a detection channel for the special interface. The configuration content includes the detection pressure range (such as dynamic pressure range or extremely high pressure), detection time (such as long - time airtightness retention test), environmental conditions (such as high temperature, low temperature, humidity change, etc.), and dynamic test requirements (such as periodic pressure change over time). Through the optimized model configuration, a channel for special interface detection is generated, which can accurately adapt to the attributes and detection requirements of special interfaces, ensuring the reliability and efficiency of the detection process.

[0040] Step S400: Based on the two - channel interface detection, perform matching and parsing on the set of attribute parameters of the interface to be detected, and obtain the waterproof performance detection parameter curve.

[0041] Preferably, through the interface detection dual channels (conventional detection channel and special detection channel), matching and parsing are performed according to the specific attribute parameter set of the Type-C interface to be detected, including matching the corresponding detection standards and methods, so as to generate a detection process and parameters suitable for this interface, and represent them in the form of a parameter curve, that is, obtain the waterproof performance detection parameter curve. Specifically, matching and parsing means selecting a suitable detection channel according to the attribute parameter set of the interface to be detected, and further determining the specific steps and requirements of the detection. If the parameter set of the interface conforms to the standard attributes (such as common dimensions, waterproof level IP67, etc.), it is matched to the conventional detection channel, which contains a preset detection process, such as a fixed pressure range, time, and curve template. If the parameter set of the interface has special requirements (such as non-standard dimensions, waterproof level IP68, or special functional requirements), it is matched to the special detection channel, and a customized detection process is generated according to the specific attributes. The result of matching and parsing is to determine the appropriate detection method and parameters to ensure that the detection process is consistent with the attribute requirements of the interface; then visualize the result of matching and parsing to define the key parameters involved in the detection process and their change rules, which may include a pressure curve (the change of pressure in the airtightness test cavity over time, for example, simulating the airtightness of the interface under a certain pressure environment), a time curve (defining the detection duration to ensure sufficient testing), and a dynamic change curve of the special detection channel (for the waterproof performance of the interface in a changing environment, the curve may represent the dynamic changes of pressure and temperature, for example, simulating different depths underwater or temperature changes), so as to make the detection process accurately adapt to the attribute requirements of different Type-C interfaces, greatly improving the pertinence, reliability, and efficiency of the detection.

[0042] Step S500, place the Type-C interface to be detected in the airtightness test cavity, and fix the Type-C interface to be detected through the automatic fixture, perform airtightness detection control on the Type-C interface to be detected according to the waterproof performance detection parameter curve, and simultaneously monitor and obtain the pressure change test data stream through the Hall pressure sensor.

[0043] Preferably, when performing the waterproof performance detection of the Type-C interface, devices such as an airtightness test chamber, an automatic fixture, and a Hall pressure sensor are used. The airtightness of the interface is accurately tested in combination with the waterproof performance detection parameter curve. Specifically, the Type-C interface to be detected is placed inside the airtightness test chamber, and the Type-C interface to be detected is fixed by the automatic fixture. The airtightness detection of the Type-C interface to be detected is controlled according to the waterproof performance detection parameter curve. At the same time, the pressure change test data stream is monitored and obtained through the Hall pressure sensor. Among them, the Type-C interface to be detected is placed in this sealed test chamber, and it is ensured that the interface is completely enclosed in this environment to avoid interference from external factors on the test results. The plug part and the connection area of the interface are both inside the chamber to ensure that there is no gas leakage during the test; the automatic fixture can automatically adjust and clamp according to the size and shape of the interface to ensure that the airtightness between the interface and the test chamber remains consistent, so as to ensure that the position, angle, etc. of the interface do not change; the airtightness detection control refers to automatically adjusting factors such as the pressure and time of the airtightness test chamber according to the waterproof performance detection parameter curve to simulate a real waterproof environment. For example, it may continuously pressurize for a certain period of time, or fluctuate within a specific pressure range to simulate the pressure changes that the interface may face during actual use; finally, the Hall pressure sensor senses the pressure change to generate a signal, so as to be able to monitor the pressure change inside the chamber in real time and obtain the pressure change test data stream (the pressure change of the interface during the test). For example, if there are defects in the airtightness of the interface, it may lead to pressure leakage, and the Hall sensor can capture these pressure change data and feedback them to the control processor in real time; thus, it can accurately judge whether the interface meets the required waterproof performance standard, and realizes automated and precise waterproof performance evaluation, improving the detection efficiency and accuracy.

[0044] Step S600, migrate to obtain the Type-C interface waterproof performance evaluator, and perform waterproof performance evaluation on the pressure change test data stream based on the Type-C interface waterproof performance evaluator to obtain the Type-C interface waterproof performance detection result.

[0045] Preferably, a transfer learning strategy is used for transfer training optimization to obtain a waterproof performance evaluator for the Type-C interface. The waterproof performance evaluator is used to analyze and evaluate the waterproof performance of the Type-C interface. It may be based on existing airtightness detection methods, pressure analysis algorithms, data modeling techniques, etc. Through adaptation or optimization, it can accurately evaluate the waterproof performance of the Type-C interface. By analyzing the data flow of the pressure change test, it determines whether the interface meets the waterproof performance requirements and outputs the detection result. Then, the waterproof performance evaluator for the Type-C interface is used to evaluate the waterproof performance of the pressure change test data flow, which may include anomaly detection (by analyzing the pressure change data flow, detecting whether there are abnormal fluctuations or pressure relief phenomena. If the pressure curve fails to remain stable within the specified time, it indicates that there may be air leakage or poor sealing in the interface), parameter matching (comparing the test data with the waterproof performance detection parameter curve to determine whether the data flow meets the standard, such as checking whether the pressure remains within the specified range for a sufficient long time), performance index calculation (calculating the specific indicators of the waterproof performance, such as the pressure relief rate, maximum pressure resistance value, etc.). According to the analysis results, the waterproof performance detection result of the Type-C interface is output, such as qualified, unqualified, or listing the performance indicators of the interface in detail (such as maximum pressure resistance, pressure relief rate, etc.). That is, if the pressure data flow completely conforms to the preset parameter curve, the detection result is output as qualified, indicating that the waterproof performance of the interface meets the requirements. Through automated and intelligent waterproof performance evaluation, the quality control and reliability detection of the Type-C interface are realized.

[0046] Further, step S600 further includes step S610 of selecting a source domain interface waterproof performance evaluation data set, where the source domain interface waterproof performance evaluation data set includes waterproof performance evaluation data sets of different types of interfaces; step S620 of respectively evaluating and training the waterproof performance evaluation data sets of different types of interfaces to obtain a source domain interface waterproof performance evaluation model set; step S630 of obtaining a Type-C interface waterproof performance evaluation target domain, and determining a transfer learning strategy according to the source domain interface waterproof performance evaluation model set and the Type-C interface waterproof performance evaluation target domain; step S640 of performing transfer training optimization on the Type-C interface waterproof performance evaluation data set and the source domain interface waterproof performance evaluation model set based on the transfer learning strategy to obtain the Type-C interface waterproof performance evaluator.

[0047] Preferably, transfer learning is used to extract knowledge from existing waterproof performance evaluation data and models, and combined with the specific data and requirements of the Type-C interface, the waterproof performance evaluator for the Type-C interface is trained and optimized. Specifically, data suitable for the characteristics of the Type-C interface (such as similar physical dimensions, waterproof level requirements, etc.) is selected from the source domain dataset. The source domain interface waterproof performance evaluation dataset is existing waterproof performance test data that has been collected and sorted, used to describe the waterproof performance evaluation results and their characteristics of different types of interfaces, including test data of various interface types (such as USB-A, USB-B, Type-C, etc.), covering the behavior and evaluation results of interfaces with different attributes (such as size, number of pins, waterproof level, etc.) in the waterproof performance test; the waterproof performance evaluation datasets of different types of interfaces are respectively evaluated and trained, that is, the selected source domain dataset is input into the basic evaluation model, and the model learns the feature patterns in the data through training. The data of different interface types are respectively used to train multiple sub-models, each model corresponding to an interface type (such as USB-A, USB-B, etc.), so that the model can accurately evaluate the waterproof performance of the input data, such as pressure holding ability, leakage characteristics, etc., and then form the source domain interface waterproof performance evaluation model set.

[0048] Preferably, obtain the waterproof performance evaluation target domain of the Type-C interface. The waterproof performance evaluation target domain of the Type-C interface is a dataset specific to the Type-C interface, including relevant data for its waterproof performance test, including the test data stream of the Type-C interface (such as pressure change, time curve, etc.) and the attribute information of the Type-C interface (such as size, number of pins, waterproof grade requirements, etc.). Then, according to the characteristics of the source domain model set and the target domain data, design a suitable transfer learning strategy to transfer the knowledge of the source domain model to the waterproof performance evaluation task of the Type-C interface, including extracting general waterproof performance features from the source domain model for data analysis of the Type-C interface, using the parameters of the source domain model as initial values, and adapting to the target domain data of the Type-C interface through fine-tuning. At the same time, train the source domain and target domain data to enable the model to optimize its adaptability to the target domain task while maintaining the knowledge of the source domain. Then, based on the transfer learning strategy, perform transfer training and optimization on the waterproof performance evaluation dataset of the Type-C interface and the source domain interface waterproof performance evaluation model set. Specifically, combine the source domain model with the target domain data of the Type-C interface for joint training. During the training process, the model gradually adjusts its parameters to enable it to adapt to the data characteristics of both the source domain and the target domain. Optimize the model through the test data of the target domain to make it more accurate in evaluating the waterproof performance of the Type-C interface. Adjust the key parameters of the model (such as learning rate, regularization coefficient, etc.) to ensure that the transferred model can better adapt to the characteristics of the Type-C interface, and then obtain a waterproof performance evaluator for the Type-C interface, which integrates the evaluation experience of the source domain interface and the specific data of the Type-C interface, can accurately analyze the waterproof performance test data stream of the Type-C interface, and give an evaluation result.

[0049] Further, step S640 further includes step S641, extracting the model parameters of the source domain interface waterproof performance evaluation model set respectively to obtain a source domain model parameter set; step S642, performing selective transfer fine-tuning on the source domain model parameter set based on the transfer learning strategy to obtain basic evaluation model structure parameters; step S643, using the basic evaluation model structure parameters to perform performance evaluation and model optimization on the waterproof performance evaluation dataset of the Type-C interface to obtain the waterproof performance evaluator for the Type-C interface.

[0050] Preferably, through transfer learning, the knowledge in the existing source domain model (such as model parameters and structure) is transferred to the waterproof performance evaluation task of the Type-C interface, and the performance evaluation model for the Type-C interface is gradually optimized and generated. Specifically, the model parameters of the source domain interface waterproof performance evaluation model set are extracted, that is, for each model in the source domain model set, the parameters optimized after training are extracted to obtain the source domain model parameter set, which may include the weight parameters, bias parameters, etc. of each layer of the model, providing initial parameters for reference for the transfer to the waterproof performance evaluation of the Type-C interface; then, based on the transfer learning strategy, the source domain model parameter set is selected and fine-tuned. Selective transfer means selecting the model parameters most relevant to the waterproof performance evaluation task of the Type-C interface from the source domain model parameter set, and the selection basis may include the similarity between the characteristics of the source domain interface and the target Type-C interface (such as size, waterproof level, functional attributes, etc.) or the performance of the model in the source domain task. Fine-tuning transfer means using the selected model parameters as the initial parameters of the Type-C interface model and fine-tuning these parameters using the target domain data of the Type-C interface, such as only optimizing the parameters highly relevant to the characteristics of the Type-C interface, so as to obtain the basic evaluation model structure parameters; finally, the basic evaluation model structure parameters are used to evaluate the performance and optimize the model for the waterproof performance evaluation data set of the Type-C interface, including using the fine-tuned basic evaluation model structure parameters to perform performance tests on the waterproof performance evaluation data set of the Type-C interface, verifying the prediction ability of the model, and further optimizing the model in combination with the evaluation results, adjusting the parameters and structure to improve the performance of the model in the Type-C interface task, and finally obtaining the waterproof performance evaluator for the Type-C interface, which can accurately evaluate the waterproof performance of the interface based on the waterproof performance test data of the Type-C interface, providing intelligent and automated support for the waterproof performance test, and greatly improving the evaluation efficiency and accuracy at the same time.

[0051] In the foregoing, reference has been made to Figure 1 a detailed description of the waterproof performance detection method for the Type-C interface according to the embodiments of the present invention. Next, reference will be made to Figure 2 describe the waterproof performance detection system for the Type-C interface according to the embodiments of the present invention.

[0052] The waterproof performance detection system for the Type-C interface according to the embodiments of the present invention is used to solve the technical problem in the prior art that it is difficult to efficiently and accurately match detection parameters, resulting in low accuracy and poor efficiency of waterproof performance detection, and achieves the technical effect of improving the detection efficiency, accuracy and reliability of the detection results of the interface waterproof performance. As Figure 2As shown in the figure, the waterproof performance detection system for a Type-C interface includes: a waterproof performance detection device acquisition module 10, an interface attribute classifier construction module 20, an interface detection dual-channel acquisition module 30, a parameter matching and analysis module 40, an airtightness detection control module 50, and a waterproof performance evaluation module 60.

[0053] The waterproof performance detection device acquisition module 10 is used to acquire a waterproof performance detection device, and the waterproof performance detection device includes an airtightness test chamber, a Hall pressure sensor, a control processor, and an automatic fixture; the interface attribute classifier construction module 20 is used to construct an interface attribute classifier, classify the attribute information of the Type-C interface to be detected based on the interface attribute classifier, and obtain a set of interface attribute parameters to be detected; the interface detection dual-channel acquisition module 30 is used to obtain an interface detection dual-channel according to the control processor, and the interface detection dual-channel includes a conventional detection channel and a special detection channel; the parameter matching and analysis module 40 is used to perform matching and analysis on the set of interface attribute parameters to be detected based on the interface detection dual-channel, and obtain a waterproof performance detection parameter curve; the airtightness detection control module 50 is used to place the Type-C interface to be detected in the airtightness test chamber, fix the Type-C interface to be detected through the automatic fixture, perform airtightness detection control on the Type-C interface to be detected according to the waterproof performance detection parameter curve, and simultaneously monitor and acquire a pressure change test data stream through the Hall pressure sensor; the waterproof performance evaluation module 60 is used to migrate and obtain a Type-C interface waterproof performance evaluator, and perform waterproof performance evaluation on the pressure change test data stream based on the Type-C interface waterproof performance evaluator to obtain a Type-C interface waterproof performance detection result.

[0054] Next, the specific configuration of the interface attribute classifier construction module 20 will be described in detail. The interface attribute classifier construction module 20 further includes: obtaining interface attribute factor information, where the interface attribute factor information includes size specifications, number of pins, functional attributes, application attributes, and waterproof level requirements; mining and obtaining a Type-C interface product database, performing attribute feature extraction on the Type-C interface product database based on the interface attribute factor information, and obtaining a set of interface product attribute features; using a decision tree to perform feature segmentation and recursive construction on the set of interface product attribute features to obtain an initial interface attribute classification tree; performing classification prediction verification and parameter strategy optimization on the initial interface attribute classification tree to construct the interface attribute classifier.

[0055] Next, the specific configuration of the interface detection dual-channel acquisition module 30 will be described in detail. The interface detection dual-channel acquisition module 30 further includes: classifying and identifying the Type-C interface product database according to the Type-C interface production standard to obtain a conventional Type-C interface product data set and a special Type-C interface product data set; crawling airtightness detection data based on the conventional Type-C interface product data set and the special Type-C interface product data set to obtain a conventional interface airtightness detection data set and a special interface airtightness detection data set; respectively performing airtightness detection analysis on the conventional interface airtightness detection data set and the special interface airtightness detection data set to obtain a conventional detection channel and a special detection channel; connecting the conventional detection channel and the special detection channel in parallel to obtain an interface detection dual-channel and storing it in the control processor.

[0056] Next, the specific configuration of the interface detection dual-channel acquisition module 30 will be further described in detail. The interface detection dual-channel acquisition module 30 further includes: preferentially extracting detection parameters from the conventional interface airtightness detection data set to obtain an available interface airtightness detection data set; performing correlation analysis on the interface attribute data and airtightness detection parameters in the available interface airtightness detection data set to generate an interface attribute-airtightness detection parameter correlation model; configuring the conventional interface detection channel based on the interface attribute-airtightness detection parameter correlation model to obtain the conventional detection channel; performing airtightness detection analysis based on the special interface airtightness detection data set and the interface attribute-airtightness detection parameter correlation model to obtain the special detection channel.

[0057] Next, the specific configuration of the interface detection dual-channel acquisition module 30 will be further described in detail. The interface detection dual-channel acquisition module 30 further includes: extracting special attribute data and associated detection parameters from the special interface airtightness detection data set to obtain special interface detection feature data; using the interface attribute-airtightness detection parameter correlation model as a basic model framework to perform training loss analysis on the special interface detection feature data to obtain detection analysis loss data; performing incremental training and updating on the interface attribute-airtightness detection parameter correlation model based on the detection analysis loss data to obtain a special interface attribute-airtightness detection association update model; configuring the special interface detection channel based on the special interface attribute-airtightness detection association update model to obtain the special detection channel.

[0058] Next, the specific configuration of the waterproof performance evaluation module 60 will be described in detail. The waterproof performance evaluation module 60 further includes: selecting a waterproof performance evaluation data set for the source domain interface, where the waterproof performance evaluation data set for the source domain interface includes waterproof performance evaluation data sets for different types of interfaces; respectively evaluating and training the waterproof performance evaluation data sets for different types of interfaces to obtain a source domain interface waterproof performance evaluation model set; obtaining a waterproof performance evaluation target domain for the Type-C interface, and determining a transfer learning strategy according to the source domain interface waterproof performance evaluation model set and the waterproof performance evaluation target domain for the Type-C interface; based on the transfer learning strategy, performing transfer training and optimization on the Type-C interface waterproof performance evaluation data set and the source domain interface waterproof performance evaluation model set to obtain the Type-C interface waterproof performance evaluator.

[0059] Next, the specific configuration of the waterproof performance evaluation module 60 will be further described in detail. The waterproof performance evaluation module 60 further includes: respectively extracting the model parameters of the source domain interface waterproof performance evaluation model set to obtain a source domain model parameter set; based on the transfer learning strategy, performing selective transfer and fine-tuning on the source domain model parameter set to obtain basic evaluation model structure parameters; using the basic evaluation model structure parameters to perform performance evaluation and model optimization on the Type-C interface waterproof performance evaluation data set to obtain the Type-C interface waterproof performance evaluator.

[0060] The waterproof performance detection system for the Type-C interface provided by the embodiments of the present invention can execute the waterproof performance detection method for the Type-C interface provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0061] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0062] The above specific embodiments do not constitute a limitation to the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A method for detecting the waterproof performance of a Type-C interface, characterized in that: The method comprises: Obtain a waterproof performance testing device, wherein the waterproof performance testing device includes an airtightness test chamber, a Hall pressure sensor, a control processor, and an automatic fixture; Construct an interface attribute classifier, classify the attribute information of the Type-C interface to be detected based on the interface attribute classifier, and obtain an interface attribute parameter set to be detected; Obtaining an interface detection dual channel according to the control processor, wherein the interface detection dual channel includes a conventional detection channel and a special detection channel; Based on the interface detection dual channels, matching and parsing the interface attribute parameter set to be detected are performed to obtain a waterproof performance detection parameter curve; The Type-C interface to be tested is built into the airtightness test chamber, and the Type-C interface to be tested is fixed by the automatic fixture, and the airtightness detection and control of the Type-C interface to be tested is performed according to the waterproof performance detection parameter curve, and at the same time, the pressure change test data stream is obtained by monitoring the Hall pressure sensor; Migrate and obtain a Type-C interface waterproof performance evaluator, perform waterproof performance evaluation on the pressure change test data stream based on the Type-C interface waterproof performance evaluator, and obtain a Type-C interface waterproof performance test result; The construction of the interface attribute classifier includes: Acquire interface attribute factor information, wherein the interface attribute factor information includes size specification, number of pins, functional attributes, application attributes, and waterproof level requirements; Mining and acquiring a Type-C interface product database, extracting attribute features from the Type-C interface product database based on the interface attribute factor information, and obtaining an interface product attribute feature data set; Using a decision tree to perform feature segmentation and recursive construction on the interface product attribute feature data set to obtain an initial interface attribute classification tree; The initial interface attribute classification tree is subjected to classification prediction verification and parameter strategy tuning to construct the interface attribute classifier.

2. The waterproof performance detection method of the Type-C interface according to claim 1, characterized in that: The obtaining of the interface detection dual channel according to the control processor comprises: Classify and identify the Type-C interface product database according to the Type-C interface production standard to obtain a regular Type-C interface product data set and a special Type-C interface product data set; Crawling air tightness detection data based on the conventional Type-C interface product data set and the special Type-C interface product data set to obtain a conventional interface air tightness detection data set and a special interface air tightness detection data set; Based on the conventional interface air tightness detection data set and the special interface air tightness detection data set, air tightness detection analysis is performed respectively to obtain a conventional detection channel and a special detection channel; The conventional detection channel and the special detection channel are connected in parallel to obtain an interface detection dual channel and store it in the control processor.

3. The waterproof performance detection method of the Type-C interface as claimed in claim 2, characterized in that: The obtaining of conventional detection channels and special detection channels comprises: Performing optimal extraction of detection parameters on the conventional interface air tightness detection data set to obtain an available interface air tightness detection data set; Performing correlation analysis on the interface attribute data and the air tightness detection parameters in the available interface air tightness detection data set to generate an interface attribute-air tightness detection parameter correlation model; Performing conventional interface detection channel configuration based on the interface attribute-air tightness detection parameter association model to obtain the conventional detection channel; Airtightness detection analysis is performed based on the special interface airtightness detection data set and the interface attribute-airtightness detection parameter association model to obtain the special detection channel.

4. The waterproof performance detection method of the Type-C interface as claimed in claim 3, characterized in that: The obtaining of the special detection channel comprises: Extracting special attribute data and associated detection parameters from the special interface air tightness detection data set to obtain special interface detection feature data; The interface attribute-air tightness detection parameter association model is used as a basic model framework, and a training loss analysis is performed on the special interface detection feature data to obtain detection analysis loss data; Based on the detection and analysis loss data, incremental training and updating are performed on the interface attribute-air tightness detection parameter association model to obtain a special interface attribute-air tightness detection association update model; Based on the special interface attribute-airtightness detection association update model, a special interface detection channel is configured to obtain the special detection channel.

5. The waterproof performance detection method of the Type-C interface according to claim 1, characterized in that: The migration obtains a Type-C interface waterproof performance evaluator, including: Selecting a source domain interface waterproof performance evaluation data set, wherein the source domain interface waterproof performance evaluation data set includes waterproof performance evaluation data sets of different types of interfaces; Conducting evaluation training on the waterproof performance evaluation data sets of the different types of interfaces respectively to obtain a source domain interface waterproof performance evaluation model set; Acquire a Type-C interface waterproof performance evaluation target domain, and determine a transfer learning strategy according to the source domain interface waterproof performance evaluation model set and the Type-C interface waterproof performance evaluation target domain; Based on the transfer learning strategy, the Type-C interface waterproof performance evaluation data set and the source domain interface waterproof performance evaluation model set are subjected to migration training and optimization to obtain the Type-C interface waterproof performance evaluator.

6. The method for detecting waterproof performance of the Type-C interface according to claim 5, characterized in that: The obtaining of the Type-C interface waterproof performance evaluator comprises: Extracting the model parameters of the source domain interface waterproof performance evaluation model set respectively to obtain a source domain model parameter set; Based on the transfer learning strategy, the source domain model parameter set is selected for transfer and fine-tuning to obtain basic evaluation model structure parameters; The basic evaluation model structure parameters are used to perform performance evaluation and model tuning on the Type-C interface waterproof performance evaluation data set to obtain the Type-C interface waterproof performance evaluator.

7. A Type-C interface waterproof performance detection system, characterized in that: The system is used to implement the waterproof performance detection method of the Type-C interface according to any one of claims 1 to 6, and the system includes: A waterproof performance detection device acquisition module, used to acquire a waterproof performance detection device, wherein the waterproof performance detection device includes an airtightness test chamber, a Hall pressure sensor, a control processor and an automatic fixture; An interface attribute classifier construction module is used to construct an interface attribute classifier, classify the attribute information of the Type-C interface to be detected based on the interface attribute classifier, and obtain a set of interface attribute parameters to be detected; An interface detection dual-channel acquisition module, used for acquiring an interface detection dual-channel according to the control processor, wherein the interface detection dual-channel includes a conventional detection channel and a special detection channel; A parameter matching and analysis module, used for matching and analyzing the attribute parameter set of the interface to be detected based on the interface detection dual channels to obtain a waterproof performance detection parameter curve; An airtightness detection control module is used to place the Type-C interface to be detected inside the airtightness test chamber, fix the Type-C interface to be detected by the automatic fixture, perform airtightness detection control on the Type-C interface to be detected according to the waterproof performance detection parameter curve, and simultaneously monitor and obtain the pressure change test data stream through the Hall pressure sensor; a waterproof performance evaluation module is used to migrate and obtain a Type-C interface waterproof performance evaluator, perform waterproof performance evaluation on the pressure change test data stream based on the Type-C interface waterproof performance evaluator, and obtain a Type-C interface waterproof performance test result.

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