Active waterproof detection method and system for electronic equipment interface

Abstract

The invention provides an active waterproof detection method and system for an electronic equipment interface, and relates to the technical field of waterproof detection, and the method comprises the steps: configuring an impedance sensing network at the electronic equipment interface; monitoring a plurality of real-time impedance sensing signals of the plurality of electrode pairs in real time through an impedance sensing network; performing signal abnormal characteristic comparison on the plurality of real-time impedance sensing signals and a reference impedance sensing sample signal library to judge whether an abnormal signal is triggered or not, and if the abnormal signal is triggered, detecting the plurality of real-time impedance sensing signals and a water vapor impedance characteristic detection model obtained by training to obtain a plurality of water vapor condensation ports; and performing intrusion path tracing according to the plurality of water vapor condensation ports to obtain a first tracing abnormal source, and generating a corresponding first abnormal adjustment signal based on the first tracing abnormal source. The technical problem that the active waterproof detection effect of the electronic equipment interface is poor in the prior art can be solved, and the technical effect of improving the waterproof detection effect is achieved.

Description

Technical Field

[0001] This application relates to the field of waterproof testing technology, and in particular to an active waterproof testing method and system for electronic device interfaces. Background Technology

[0002] With the widespread application of electronic devices, especially in fields requiring high precision and high reliability, such as smart hardware, industrial control, and communication equipment, higher demands are placed on the protection performance of device interfaces. Waterproofing and moisture-proofing, particularly the issue of water vapor intrusion, have become crucial for ensuring stable equipment operation. Electronic device interfaces typically feature compact structures, dense conductive contacts, and small spatial dimensions. When external moisture enters the interface through gaps, connectors, or connection points, it easily condenses locally under the influence of factors such as temperature gradients, electric field distribution, and differences in material surface energy. This condensation then diffuses and migrates along the internal structure of the interface, creating a multi-point, gradual water vapor intrusion process. This process exhibits significant dynamic, concealed, and nonlinear characteristics.

[0003] Currently, most existing waterproofing detection technologies rely on single moisture sensors or electrical fault detection methods. These technologies only issue alarms when moisture intrusion is severe and lack the ability to accurately trace the source of moisture intrusion or precisely sense the location, diffusion path, and evolution process of moisture within electronic device interfaces. Existing methods often indirectly infer the presence of moisture by measuring changes in humidity outside the equipment or the overall environment, or based on resulting fault signals such as short circuits or leakage at the interface. However, because the condensation and diffusion process of moisture within the interface is affected by multiple factors such as interface structure layout, material differences, and changes in operating conditions, it exhibits non-uniform and phased development characteristics. Traditional sensors often struggle to achieve high-sensitivity detection, leading to significant false alarms or missed alarms. Furthermore, most existing moisture monitoring systems do not consider the accurate tracing of moisture intrusion paths and lack dynamic adjustment and active protection mechanisms, meaning that once moisture intrusion occurs, equipment often requires a considerable amount of time to recover.

[0004] In summary, the existing technology suffers from a lack of real-time monitoring and location capabilities for moisture intrusion, which makes it impossible to effectively prevent moisture from entering the equipment interface in the early stages, further affecting the stability and safety of the equipment. Summary of the Invention

[0005] The purpose of this application is to provide an active waterproof detection method and system for electronic device interfaces, in order to solve the technical problem in the prior art that the lack of real-time monitoring and location capabilities for water vapor intrusion makes it impossible to effectively prevent water vapor from entering the device interface in the early stage, thereby further affecting the stability and safety of the device.

[0006] In view of the above problems, this application provides an active waterproof detection method and system for electronic device interfaces.

[0007] In a first aspect, this application provides an active waterproof detection method for electronic device interfaces, implemented through an active waterproof detection system for electronic device interfaces, comprising: configuring an impedance sensing network consisting of multiple electrode pairs on the electronic device interface; extracting a reference impedance sensing sample signal library; monitoring multiple real-time impedance sensing signals of the multiple electrode pairs in real time through the impedance sensing network; comparing the multiple real-time impedance sensing signals with the reference impedance sensing sample signal library to determine whether an abnormal signal is triggered; if an abnormal signal is triggered, detecting the multiple real-time impedance sensing signals with a trained water vapor impedance feature detection model to obtain multiple water vapor condensation points; tracing the intrusion path according to the spatial distribution of the multiple water vapor condensation points to obtain a first traceable abnormal source, and generating a first abnormal adjustment signal based on the first traceable abnormal source.

[0008] Preferably, the active waterproof detection method for an electronic device interface further includes: the impedance sensing network is connected to a signal excitation source, the signal excitation source is used to inject a periodic excitation signal into the plurality of electrode pairs, and the plurality of electrode pairs monitor a plurality of real-time impedance sensing signals corresponding to the periodic excitation signal; wherein, the reference impedance sensing sample signal library is constructed by reference impedance sensing sample signals collected based on the periodic excitation signal under a known water vapor-free environment.

[0009] Preferably, the active waterproof detection method for an electronic device interface further includes: the impedance sensing network is connected to a signal excitation source, the signal excitation source includes a periodic excitation frequency, and the periodic excitation frequency and the load index of the electronic device interface have a directly proportional nonlinear functional relationship; and the injected periodic excitation signal is configured according to the periodic excitation frequency.

[0010] Preferably, the active waterproof detection method for an electronic device interface further includes: extracting multiple sets of impedance feature vectors from the multiple real-time impedance sensing signals; extracting the reference impedance feature vector from the reference impedance sensing sample signal library; performing normalized impedance deviation calculation on the multiple sets of impedance feature vectors and the reference impedance feature vector to obtain an impedance deviation index; and triggering an abnormal signal if the impedance deviation index corresponding to any electrode pair is greater than a preset impedance deviation threshold.

[0011] Preferably, the active waterproof detection method for an electronic device interface further includes: performing water vapor-related high-dimensional feature convolution on multiple sets of impedance feature vectors of the multiple real-time impedance sensing signals to obtain multiple sets of water vapor-related impedance feature vectors; performing vector similarity detection on the multiple sets of water vapor-related impedance feature vectors and the trained water vapor impedance feature detection model to obtain a similarity index, wherein the water vapor impedance feature detection model is obtained by training on positive training samples of water vapor intrusion and negative training samples of other interferences that are not water vapor intrusion; marking the spatial locations of electrode pairs with a similarity greater than a preset value as water vapor condensation ports, until multiple water vapor condensation ports are obtained.

[0012] Preferably, the active waterproof detection method for an electronic device interface further includes: identifying the three-dimensional spatial distribution of the electronic device interface; performing node mapping on the three-dimensional spatial distribution according to the spatial location of the plurality of water vapor condensation ports to obtain a spatial distribution of water vapor intrusion nodes; and tracing the intrusion path based on the intrusion similarity of the plurality of water vapor condensation ports according to the spatial distribution of water vapor intrusion nodes to obtain a first traceable anomaly source.

[0013] Preferably, the active waterproof detection method for an electronic device interface further includes: acquiring multiple access sources connected to the electronic device interface; identifying intrusion risk characteristics of the multiple access sources, the intrusion risk characteristics including access source type, access environment humidity, access duration, and access history intrusion frequency; calculating multiple conditional probabilities under multiple water vapor similarity events corresponding to the multiple water vapor condensation ports based on the intrusion risk characteristics, the multiple conditional probabilities corresponding to the multiple access sources; and determining a first traceable anomaly source based on the magnitude of the multiple conditional probabilities.

[0014] Preferably, the active waterproof detection method for an electronic device interface further includes: calculating each of the plurality of conditional probabilities, including calculating the spatial distribution similarity between the current spatial distribution of water vapor intrusion nodes and the historical spatial distribution of water vapor intrusion nodes, and the pattern similarity between the current plurality of water vapor similarities and the historical plurality of water vapor similarities.

[0015] Preferably, the active waterproof detection method for an electronic device interface further includes: identifying an active protection strategy for the first traceable anomaly source; analyzing multiple feedback impedance sensing signals of the multiple electrode pairs collected according to the action parameters of the active protection strategy to obtain a first action parameter without triggering an anomaly signal; and generating a first anomaly adjustment signal corresponding to the first traceable anomaly source based on the first action parameter.

[0016] Secondly, this application also provides an active waterproof detection system for an electronic device interface, used to execute an active waterproof detection method for an electronic device interface as described in the first aspect, comprising: a sensor network configuration module, used to configure an impedance sensing network composed of multiple electrode pairs on the electronic device interface; a signal monitoring module, used to extract a reference impedance sensing sample signal library, and monitor multiple real-time impedance sensing signals of the multiple electrode pairs in real time through the impedance sensing network; a water vapor condensation port acquisition module, used to compare the multiple real-time impedance sensing signals with the reference impedance sensing sample signal library to determine whether an abnormal signal is triggered, and if an abnormal signal is triggered, to detect the multiple real-time impedance sensing signals with a trained water vapor impedance feature detection model to acquire multiple water vapor condensation ports; and a signal generation module, used to trace the intrusion path according to the spatial distribution of the multiple water vapor condensation ports to obtain a first traceable abnormal source, and generate a first abnormal adjustment signal based on the first traceable abnormal source.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goals of accurate monitoring, early warning and source tracing of water vapor intrusion, it can improve the protection performance of equipment interfaces, improve the stability of equipment operation and extend the service life of equipment.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an active waterproof testing method for an electronic device interface according to this application.

[0021] Figure 2 This is a schematic diagram of the structure of an active waterproof detection system for an electronic device interface according to this application.

[0022] Figure labeling: Sensor network configuration module 1, signal monitoring module 2, water vapor condensation port acquisition module 3, signal generation module 4. Detailed Implementation

[0023] This application provides an active waterproof detection method and system for electronic device interfaces, solving the technical problem in existing technologies where the lack of real-time monitoring and location capabilities for moisture intrusion prevents effective prevention of moisture entering the device interface in its early stages, thus affecting the stability and safety of the equipment. It achieves the technical goals of accurate monitoring, early warning, and source tracing of moisture intrusion, thereby improving the protection performance of the device interface, enhancing equipment operational stability, and extending equipment lifespan.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides an active waterproof testing method for electronic device interfaces, applied to an active waterproof testing system for electronic device interfaces, specifically including the following steps: An impedance sensing network consisting of multiple electrode pairs is configured at the interface of an electronic device.

[0026] Specifically, in electronic device interfaces, an impedance sensing network consisting of multiple electrode pairs is employed to monitor the interface's status by detecting impedance changes between these pairs. An impedance sensing network is a sensor system that measures external physical phenomena through impedance induction between electrode pairs. Each electrode pair consists of a pair of electrodes, and impedance is formed between them through electrical signal transmission. This impedance reflects the interface's electrical characteristics in real time and is used to detect the impact of external factors, such as moisture intrusion, on the device interface. The impedance sensing network can sense and monitor impedance changes between electrode pairs, providing timely feedback on changes in the device interface environment. The arrangement of multiple electrode pairs allows for multi-point monitoring of the electronic device interface, improving coverage and sensitivity. This provides a necessary foundation for real-time monitoring of the electronic device interface's health status. During waterproof testing, changes in impedance are used to identify whether moisture intrusion has occurred.

[0027] Extract a reference impedance sensing sample signal library, and monitor multiple real-time impedance sensing signals of the multiple electrode pairs in real time through the impedance sensing network.

[0028] Furthermore, this application also includes: the impedance sensing network is connected to a signal excitation source, the signal excitation source is used to inject a periodic excitation signal into the plurality of electrode pairs, and the plurality of electrode pairs monitor a plurality of real-time impedance sensing signals corresponding to the periodic excitation signal; wherein, the reference impedance sensing sample signal library is constructed by reference impedance sensing sample signals collected based on the periodic excitation signal under a known water vapor-free condensation environment.

[0029] Furthermore, this application also includes: the impedance sensing network is connected to a signal excitation source, the signal excitation source includes a periodic excitation frequency, and the periodic excitation frequency and the load index of the electronic device interface have a directly proportional nonlinear functional relationship; and the injected periodic excitation signal is configured according to the periodic excitation frequency.

[0030] Specifically, the reference impedance sensing sample signal library is constructed by sampling and storing impedance sensing networks. It includes a set of reference impedance sensing signals obtained through periodic excitation signals under specific environments such as no water vapor condensation. This provides a reference standard for subsequent monitoring and can help distinguish the signal differences between normal and potentially abnormal environments.

[0031] The impedance sensing network is connected to a signal excitation source, which injects a periodic excitation signal into multiple electrode pairs. This excitation signal is a periodically changing electrical signal used to excite the impedance response of the electrode pairs. The multiple electrode pairs capture the real-time impedance sensing signal corresponding to the periodic excitation signal by monitoring changes in the impedance signal, thus reflecting the real-time electrical state of the device interface.

[0032] The reference impedance sensing sample signal library is constructed by collecting signals in an environment where water vapor condensation is known to be absent. The signal acquisition process is carried out under conditions where water vapor condensation is absent, thereby obtaining a stable reference signal that can be used for comparison. This provides a clear standard for subsequent anomaly detection and can be used to compare and analyze the real-time signals obtained in actual operation.

[0033] Furthermore, the impedance sensing network is connected to the signal excitation source. In the detection architecture of the electronic device interface, the impedance sensing network used to sense impedance changes is electrically connected to the signal excitation source used to output the excitation signal, so that the impedance sensing network can work under external excitation conditions.

[0034] The signal excitation source includes a periodic excitation frequency, which is a frequency parameter that exhibits periodic changes in the excitation signal over time. This frequency is used to control the rate of change and energy distribution of the excitation signal. The load index of an electronic device interface characterizes the electrical load characteristics of the interface under different operating conditions. The load index reflects changes in power consumption, current, or impedance when the interface is connected to external devices or operating in different modes. There is a direct proportional nonlinear functional relationship between the periodic excitation frequency and the load index of the electronic device interface. As the load index of the electronic device interface increases, the periodic excitation frequency generally shows an increasing trend. However, this change is not a linear correspondence but rather a mapping through a nonlinear function, allowing the excitation frequency to more precisely adapt to the electrical characteristics of the interface under different load conditions.

[0035] Secondly, configuring the injected periodic excitation signal according to the periodic excitation frequency means that after determining the periodic excitation frequency that matches the current electronic device interface load index, the output signal of the signal excitation source is configured based on the frequency parameter, and the configured periodic excitation signal is injected into multiple electrode pairs. This enables the impedance sensing network to obtain stable and targeted excitation input under different load conditions, thereby ensuring that the acquired real-time impedance sensing signal is compatible with the current working state of the interface in terms of spectral characteristics, sensitivity, and anti-interference capability.

[0036] The multiple real-time impedance sensing signals are compared with the reference impedance sensing sample signal library to determine whether an abnormal signal is triggered. If an abnormal signal is triggered, the multiple real-time impedance sensing signals are detected with the trained water vapor impedance feature detection model to obtain multiple water vapor condensation ports.

[0037] Furthermore, this application also includes: extracting multiple sets of impedance feature vectors from the multiple real-time impedance sensing signals; extracting the reference impedance feature vector from the reference impedance sensing sample signal library; performing normalized impedance deviation calculation on the multiple sets of impedance feature vectors and the reference impedance feature vector to obtain an impedance deviation index; and triggering an abnormal signal if the impedance deviation index corresponding to any electrode pair is greater than a preset impedance deviation threshold.

[0038] Furthermore, this application also includes: performing water vapor-related high-dimensional feature convolution on multiple sets of impedance feature vectors of the multiple real-time impedance sensing signals to obtain multiple sets of water vapor-related impedance feature vectors; performing vector similarity detection on the multiple sets of water vapor-related impedance feature vectors and the trained water vapor impedance feature detection model to obtain a similarity index, wherein the water vapor impedance feature detection model is obtained by training on positive training samples of water vapor intrusion and negative training samples of other interferences that are not water vapor intrusion; marking the spatial locations of electrode pairs with a similarity greater than a preset value as water vapor condensation ports, until multiple water vapor condensation ports are obtained.

[0039] Specifically, extracting multiple sets of impedance feature vectors from multiple real-time impedance sensing signals means processing and analyzing multiple impedance signals acquired in real time, transforming the signals into vector forms that reflect their characteristics. An impedance feature vector is a set of numerical values ​​used to describe the variation and electrical characteristics of the impedance signal at different time points or under different conditions. Multiple sets of impedance feature vectors are extracted from the original impedance signals using mathematical methods to enable more efficient subsequent comparison and analysis.

[0040] Next, the reference impedance feature vector is extracted from the reference impedance sensor sample signal library. This involves selecting signal feature vectors representing normal operating conditions from the pre-constructed reference impedance sensor sample signal library. The reference feature vectors are obtained by data acquisition in a known water-vapor-free environment and reflect the electrical characteristics of the electronic equipment interface under normal operating conditions. The normalized impedance deviation calculation between multiple sets of impedance feature vectors and the reference impedance feature vector involves normalizing the data to eliminate differences in signal strength or units, comparing the impedance feature vectors of multiple real-time signals with the feature vector of the reference signal, and calculating the deviation between them. The magnitude of the deviation is represented by an impedance deviation index, reflecting the degree of difference between the real-time signal and the reference signal.

[0041] Finally, if the impedance deviation of any electrode pair exceeds a preset impedance deviation threshold, an abnormal signal is triggered. If, during the comparison process, the impedance deviation of any electrode pair exceeds the set threshold, the signal of that electrode pair is determined to be abnormal, thereby triggering an alarm signal. This is used to achieve real-time monitoring and identification of possible abnormal states, such as moisture intrusion, ensuring timely system response.

[0042] Performing high-dimensional feature convolution on multiple sets of impedance feature vectors from various real-time impedance sensing signals related to water vapor intrusion means applying a convolution operation to the impedance feature vectors of multiple electrode pairs acquired in real time to extract high-dimensional features related to water vapor intrusion. High-dimensional feature convolution combines the spatial information and frequency domain features of the signal, thereby enhancing the sensitivity to water vapor intrusion. Water vapor-related high-dimensional feature convolution can identify and strengthen the parts of the impedance signal directly related to water vapor intrusion, generating a new set of water vapor-related impedance feature vectors that reflect the influence of water vapor in the interface region.

[0043] Next, vector similarity detection is performed between multiple sets of water vapor-related impedance feature vectors and the trained water vapor impedance feature detection model to obtain a similarity index. The degree of agreement between the current signal and the water vapor intrusion characteristics is determined by comparing the similarity between the water vapor-related impedance feature vectors and the trained water vapor impedance feature detection model. The water vapor impedance feature detection model learns from positive training samples under water vapor intrusion conditions and negative training samples from other interference signals without water vapor intrusion, thus obtaining a model that can accurately distinguish water vapor from other interferences. Through vector similarity detection, it outputs a similarity index, representing the degree of matching between the real-time monitoring signal and the water vapor characteristics.

[0044] Finally, the spatial locations of electrode pairs with a similarity greater than a preset value are marked as water vapor condensation points until multiple water vapor condensation points are obtained. When the similarity index of an electrode pair exceeds a set threshold, it is determined that water vapor condensation exists at that location and it is marked as a water vapor condensation point. This allows for the spatial identification of specific areas where water vapor may intrude. By continuously detecting and marking multiple water vapor condensation points, a comprehensive monitoring of water vapor intrusion at electronic device interfaces is achieved.

[0045] The intrusion path is traced according to the spatial distribution of the multiple water vapor condensation ports to obtain the first traceable anomaly source, and a first anomaly adjustment signal is generated based on the first traceable anomaly source.

[0046] Furthermore, this application also includes: identifying the three-dimensional spatial distribution of the electronic device interface; performing node mapping on the three-dimensional spatial distribution according to the spatial location of the plurality of water vapor condensation ports to obtain the spatial distribution of water vapor intrusion nodes; and tracing the intrusion path based on the spatial distribution of water vapor intrusion nodes and the multiple water vapor similarities corresponding to the plurality of water vapor condensation ports to obtain the first traceable anomaly source.

[0047] Furthermore, this application also includes: acquiring multiple access sources connected to the interface of the electronic device; identifying intrusion risk characteristics of the multiple access sources, the intrusion risk characteristics including access source type, access environment humidity, access duration, and access history intrusion frequency; calculating multiple conditional probabilities under multiple water vapor similarity events corresponding to the multiple water vapor condensation ports based on the intrusion risk characteristics, the multiple conditional probabilities corresponding to the multiple access sources; and determining a first traceable anomaly source based on the magnitude of the multiple conditional probabilities.

[0048] Furthermore, this application also includes: calculating each conditional probability of the plurality of conditional probabilities includes calculating the spatial distribution similarity between the current spatial distribution of water vapor intrusion nodes and the historical spatial distribution of water vapor intrusion nodes, as well as the pattern similarity between the current plurality of water vapor similarities and the historical plurality of water vapor similarities.

[0049] Furthermore, this application also includes: an active protection strategy for identifying the first traceability anomaly source; analyzing the multiple feedback impedance sensing signals of the multiple electrode pairs collected according to the action parameters of the active protection strategy to obtain a first action parameter without triggering an anomaly signal; and generating a first anomaly adjustment signal corresponding to the first traceability anomaly source based on the first action parameter.

[0050] Specifically, identifying the three-dimensional spatial distribution of electronic device interfaces refers to obtaining the specific location and geometric shape of the electronic device interface in three-dimensional space by spatial modeling the interface. Three-dimensional spatial distribution refers to the coordinate arrangement of the electronic device interface and its related components in three-dimensional space, which can comprehensively reflect the structural characteristics of the interface and its surrounding environment. By using spatial positioning technology or sensor data, the spatial location of the device interface and its relationship with other components can be determined.

[0051] Next, node mapping is performed on the three-dimensional spatial distribution of multiple water vapor condensation points according to their spatial locations, resulting in a spatial distribution of water vapor intrusion nodes. By matching the spatial locations of the identified water vapor condensation points with the three-dimensional spatial distribution of electronic device interfaces, each water vapor condensation point is mapped to a corresponding node location, thus forming a spatial distribution of water vapor intrusion nodes. The spatial distribution of water vapor intrusion nodes represents the specific location and distribution of water vapor condensation points in three-dimensional space, which is used for further analysis of water vapor propagation paths and intrusion sources.

[0052] Finally, based on the spatial distribution of water vapor intrusion nodes, the intrusion path is traced using the similarity of multiple water vapor condensation points to identify the first traceable anomaly source. Tracing the spatial distribution of water vapor intrusion nodes allows us to determine the intrusion path of water vapor and analyze how it diffuses from one node to other locations. This enables us to locate the initial water vapor intrusion source and mark it as the first traceable anomaly source. Intrusion path tracing involves analyzing the similarity and spatial relationships of multiple water vapor condensation points to determine the propagation order and source of water vapor.

[0053] Identifying multiple access sources connected to an electronic device interface refers to identifying and listing all external devices or interfaces that connect to the electronic device interface; these external devices or interfaces are the access sources. An access source refers to the physical or logical connection point between an external device or communication system and the electronic device interface.

[0054] Next, identifying the intrusion risk characteristics of multiple access sources involves analyzing the potential security risks of each access source and extracting relevant characteristics that may lead to moisture intrusion or other malfunctions. Intrusion risk characteristics include access source type, ambient humidity, access duration, and historical intrusion frequency. Access source type refers to the category of external device or interface, such as sensors or controllers; ambient humidity describes the humidity level of the environment in which the access source is located; access duration refers to the length of time the access source is connected to the electronic device interface; and historical intrusion frequency indicates the frequency with which the access source has experienced abnormal events or moisture intrusion in the past.

[0055] Next, based on the intrusion risk characteristics, multiple conditional probabilities are calculated for multiple water vapor similarity events corresponding to multiple water vapor condensation outlets. This involves using the previously identified intrusion risk characteristics to perform probabilistic analysis on different events occurring at the water vapor condensation outlets. Each water vapor condensation outlet may be affected by multiple access sources. By calculating multiple conditional probabilities, the degree of influence of different access sources on the water vapor condensation outlet can be analyzed. Conditional probability reflects the likelihood of a water vapor intrusion event occurring at a certain water vapor condensation outlet under specific risk characteristics. The correspondence between multiple conditional probabilities and multiple access sources means that each conditional probability value is associated with a corresponding access source to determine which access source has the greatest impact on the water vapor condensation outlet.

[0056] Finally, determining the first traceable anomaly source based on the magnitude of multiple conditional probabilities means identifying the inlet source with the greatest impact on the water vapor condensation port after comparing the conditional probabilities of various inlet sources, and designating this inlet source as the first traceable anomaly source. This allows for accurate tracing of the root cause of water vapor intrusion, providing a basis for further protection and anomaly handling.

[0057] Calculating multiple conditional probabilities involves assessing the probability of water vapor intrusion occurring under current environmental conditions for each specific water vapor condensation point during the detection process. This includes calculating the spatial similarity between the current spatial distribution of water vapor intrusion nodes and their historical distribution. The current spatial distribution refers to the location of water vapor condensation points in three-dimensional space at the current moment, while the historical spatial distribution refers to the location of water vapor intrusion events that occurred within a certain time period in the past. Spatial distribution similarity is quantified by comparing current and historical data, thereby assessing the risk level of the current water vapor intrusion.

[0058] Next, calculating the pattern similarity between current and historical water vapor similarities refers to further evaluating the relationship between the similarities of current and historical water vapor condensation sites. Water vapor similarity assesses the similarity between current and historical water vapor condensation sites by comparing the characteristics of water vapor intrusion events. Pattern similarity, on the other hand, measures the similarity between current and historical water vapor intrusion patterns based on multiple water vapor similarities. Through pattern similarity, the similarity between current and historical water vapor intrusion characteristics can be determined, thereby predicting potential water vapor intrusion risks.

[0059] Furthermore, the proactive protection strategy for identifying the primary source of the anomaly refers to identifying and determining a protective strategy to address the anomaly after determining the source of moisture intrusion. A proactive protection strategy refers to a series of measures or operations used to actively avoid or mitigate problems or risks caused by the anomaly, including preventing further development or expansion of the problem by adjusting working conditions, activating protective equipment, and changing operating parameters.

[0060] Next, based on the action parameters of the active protection strategy, the feedback impedance sensing signals from multiple electrode pairs are analyzed to obtain the first action parameters under conditions where no abnormal signal is triggered. According to the identified protection strategy, the action parameters are used to analyze the feedback impedance sensing signals collected from multiple electrode pairs. Feedback impedance sensing signals refer to the signals fed back by the electrode pairs during system operation or protection, reflecting the real-time status of the device interface. By analyzing these signals, the action parameters required to be executed without triggering an abnormal signal can be determined, ensuring that the device can still operate normally under the protection strategy without generating new anomalies.

[0061] Finally, based on the first action parameters, a first anomaly adjustment signal corresponding to the first traceable anomaly source is generated. This means that after acquiring the necessary action parameters, an adjustment signal is generated through processing these parameters to adjust the operating state or working conditions of the equipment in order to address potential problems caused by the first traceable anomaly source. The first anomaly adjustment signal is intended to ensure normal operation under protective measures, prevent the triggering of anomaly signals, and enhance the robustness and security of the system.

[0062] In summary, the active waterproof detection method for electronic device interfaces provided in this application has the following technical effects: by achieving the technical goals of accurate monitoring, early warning, and source tracing of water vapor intrusion, it can improve the protection performance of device interfaces, enhance the operational stability of devices, and extend the service life of devices.

[0063] Example 2: Based on the same inventive concept as the active waterproof detection method for an electronic device interface in the foregoing embodiments, this application also provides an active waterproof detection system for an electronic device interface. Please refer to the appendix. Figure 2 The system includes: a sensor network configuration module 1, used to configure an impedance sensing network consisting of multiple electrode pairs on the interface of an electronic device; a signal monitoring module 2, used to extract a reference impedance sensing sample signal library and monitor multiple real-time impedance sensing signals of the multiple electrode pairs in real time through the impedance sensing network; a water vapor condensation port acquisition module 3, used to compare the multiple real-time impedance sensing signals with the reference impedance sensing sample signal library to determine whether an abnormal signal is triggered; if an abnormal signal is triggered, the multiple real-time impedance sensing signals are detected with a trained water vapor impedance feature detection model to acquire multiple water vapor condensation ports; and a signal generation module 4, used to trace the intrusion path according to the spatial distribution of the multiple water vapor condensation ports to obtain a first traceable abnormal source and generate a first abnormal adjustment signal based on the first traceable abnormal source.

[0064] Furthermore, the active waterproof detection system for an electronic device interface is also used in the following ways: the impedance sensing network is connected to a signal excitation source, the signal excitation source is used to inject periodic excitation signals into the plurality of electrode pairs, and the plurality of electrode pairs monitor a plurality of real-time impedance sensing signals corresponding to the periodic excitation signals; wherein, the reference impedance sensing sample signal library is constructed by reference impedance sensing sample signals collected based on the periodic excitation signals under a known water vapor condensation-free environment.

[0065] Furthermore, the active waterproof detection system for an electronic device interface is also used for: connecting the impedance sensing network to a signal excitation source, the signal excitation source including a periodic excitation frequency, the periodic excitation frequency having a direct proportional nonlinear functional relationship with the load index of the electronic device interface; and configuring the injected periodic excitation signal according to the periodic excitation frequency.

[0066] Furthermore, the active waterproof detection system for an electronic device interface is also used to: extract multiple sets of impedance feature vectors from the multiple real-time impedance sensing signals; extract the reference impedance feature vector from the reference impedance sensing sample signal library; perform normalized impedance deviation calculation on the multiple sets of impedance feature vectors and the reference impedance feature vector to obtain an impedance deviation index; if the impedance deviation index corresponding to any electrode pair is greater than a preset impedance deviation threshold, an abnormal signal is triggered.

[0067] Furthermore, the active waterproof detection system for an electronic device interface is also used for: performing water vapor-related high-dimensional feature convolution on multiple sets of impedance feature vectors of the multiple real-time impedance sensing signals to obtain multiple sets of water vapor-related impedance feature vectors; performing vector similarity detection on the multiple sets of water vapor-related impedance feature vectors and the trained water vapor impedance feature detection model to obtain a similarity index, wherein the water vapor impedance feature detection model is obtained by training on positive training samples of water vapor intrusion and negative training samples of other interferences that are not water vapor intrusion; marking the spatial locations of electrode pairs with a similarity greater than a preset value as water vapor condensation ports, until multiple water vapor condensation ports are obtained.

[0068] Furthermore, the active waterproof detection system for an electronic device interface is also used to: identify the three-dimensional spatial distribution of the electronic device interface; perform node mapping on the three-dimensional spatial distribution according to the spatial location of the plurality of water vapor condensation ports to obtain the spatial distribution of water vapor intrusion nodes; and trace the intrusion path based on the spatial distribution of water vapor intrusion nodes and the multiple water vapor similarities corresponding to the plurality of water vapor condensation ports to obtain the first traceable anomaly source.

[0069] Furthermore, the active waterproof detection system for an electronic device interface is also used for: acquiring multiple access sources connected to the electronic device interface; identifying intrusion risk characteristics of the multiple access sources, the intrusion risk characteristics including access source type, access environment humidity, access duration, and access history intrusion frequency; calculating multiple conditional probabilities under multiple water vapor similarity events corresponding to the multiple water vapor condensation ports based on the intrusion risk characteristics, the multiple conditional probabilities corresponding to the multiple access sources; and determining a first traceable anomaly source based on the magnitude of the multiple conditional probabilities.

[0070] Furthermore, the active waterproof detection system for an electronic device interface is also used to: calculate each conditional probability of the plurality of conditional probabilities, including calculating the spatial distribution similarity between the current spatial distribution of water vapor intrusion nodes and the historical spatial distribution of water vapor intrusion nodes, and the pattern similarity between the current plurality of water vapor similarities and the historical plurality of water vapor similarities.

[0071] Furthermore, the active waterproof detection system for an electronic device interface is also used for: identifying the active protection strategy of the first traceable anomaly source; analyzing the multiple feedback impedance sensing signals of the multiple electrode pairs collected according to the action parameters of the active protection strategy to obtain the first action parameters under the condition of not triggering an anomaly signal; and generating the first anomaly adjustment signal corresponding to the first traceable anomaly source based on the first action parameters.

[0072] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The active waterproof detection method and specific examples of an electronic device interface in the foregoing embodiment one are also applicable to the active waterproof detection system of an electronic device interface in this embodiment. Through the foregoing detailed description of the active waterproof detection method of an electronic device interface, those skilled in the art can clearly understand the active waterproof detection system of an electronic device interface in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0073] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0074] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. An active waterproof testing method for electronic device interfaces, characterized in that, The method includes: An impedance sensing network consisting of multiple electrode pairs is configured at the interface of an electronic device. Extract a reference impedance sensing sample signal library, and monitor multiple real-time impedance sensing signals of the multiple electrode pairs in real time through the impedance sensing network. The multiple real-time impedance sensing signals are compared with the reference impedance sensing sample signal library to determine whether an abnormal signal is triggered. If an abnormal signal is triggered, the multiple real-time impedance sensing signals are detected with the trained water vapor impedance feature detection model to obtain multiple water vapor condensation ports. The intrusion path is traced according to the spatial distribution of the multiple water vapor condensation ports to obtain the first traceable anomaly source, and a first anomaly adjustment signal is generated based on the first traceable anomaly source.

2. The active waterproof detection method for an electronic device interface as described in claim 1, characterized in that, The method for real-time monitoring of multiple real-time impedance sensing signals of the multiple electrode pairs through the impedance sensing network includes: The impedance sensing network is connected to a signal excitation source, which is used to inject periodic excitation signals into the plurality of electrode pairs. The plurality of electrode pairs monitor a plurality of real-time impedance sensing signals based on the periodic excitation signals. The reference impedance sensing sample signal library is constructed by collecting reference impedance sensing sample signals based on the periodic excitation signal under a known water vapor-free condensation environment.

3. The active waterproof detection method for an electronic device interface as described in claim 2, characterized in that, The impedance sensing network is connected to a signal excitation source, which includes a periodic excitation frequency. The periodic excitation frequency and the load index of the electronic device interface have a proportional nonlinear functional relationship. The injected periodic excitation signal is configured according to the periodic excitation frequency.

4. The active waterproof testing method for an electronic device interface as described in claim 2, characterized in that, The method involves comparing the multiple real-time impedance sensing signals with the reference impedance sensing sample signal library to determine whether an abnormal signal has been triggered. Extract multiple sets of impedance feature vectors from the multiple real-time impedance sensing signals; Extract the reference impedance feature vector from the reference impedance sensing sample signal library, and perform normalized impedance deviation calculation between the multiple sets of impedance feature vectors and the reference impedance feature vector to obtain the impedance deviation index. If the impedance deviation index corresponding to any electrode pair is greater than the preset impedance deviation threshold, an abnormal signal is triggered.

5. The active waterproof detection method for an electronic device interface as described in claim 1, characterized in that, If an abnormal signal is triggered, the multiple real-time impedance sensing signals are compared with the trained water vapor impedance feature detection model to obtain multiple water vapor condensation ports. The method includes: Multiple sets of water vapor-related high-dimensional feature convolutions are performed on the multiple sets of impedance feature vectors of the multiple real-time impedance sensing signals to obtain multiple sets of water vapor-related impedance feature vectors. The similarity index is obtained by performing vector similarity detection on the multiple sets of water vapor-related impedance feature vectors and the trained water vapor impedance feature detection model. The water vapor impedance feature detection model is obtained by training on positive training samples of water vapor intrusion and negative training samples of other interferences that are not water vapor intrusion. The spatial locations of electrode pairs with a similarity greater than a preset value are marked as water vapor condensation ports until multiple water vapor condensation ports are obtained.

6. The active waterproof testing method for an electronic device interface as described in claim 1, characterized in that, The intrusion path is traced according to the spatial distribution of the multiple water vapor condensation points to obtain the first traceable anomaly source. The method includes: Identify the three-dimensional spatial distribution of the electronic device's interfaces; Based on the spatial location of the multiple water vapor condensation ports, node mapping is performed in the three-dimensional spatial distribution to obtain the spatial distribution of water vapor intrusion nodes; Based on the spatial distribution of the water vapor intrusion nodes, the intrusion path is traced for multiple water vapor similarities corresponding to the multiple water vapor condensation ports to obtain the first traceable anomaly source.

7. The active waterproof detection method for an electronic device interface as described in claim 6, characterized in that, Based on the spatial distribution of the water vapor intrusion nodes, the method for tracing the intrusion path of multiple water vapor condensation points corresponding to multiple water vapor similarities includes: Multiple access sources connected to the interface of the electronic device are acquired, and the intrusion risk characteristics of the multiple access sources are identified. The intrusion risk characteristics include access source type, access environment humidity, access duration, and access history intrusion frequency. Based on the intrusion risk characteristics, calculate multiple conditional probabilities under multiple water vapor similarity events corresponding to the multiple water vapor condensation ports, and the multiple conditional probabilities correspond to the multiple access sources; The first traceable anomaly source is determined based on the magnitude of the multiple conditional probabilities.

8. The active waterproof detection method for an electronic device interface as described in claim 7, characterized in that, Calculating each of the multiple conditional probabilities includes calculating the spatial distribution similarity between the current spatial distribution of water vapor intrusion nodes and the historical spatial distribution of water vapor intrusion nodes, as well as the pattern similarity between the current multiple water vapor similarities and the historical multiple water vapor similarities.

9. The active waterproof testing method for an electronic device interface as described in claim 1, characterized in that, The method further includes generating a first anomaly adjustment signal based on the first traced anomaly source: Active protection strategy to identify the first traceability anomaly source; Based on the action parameters of the active protection strategy, the multiple feedback impedance sensing signals of the multiple electrode pairs are analyzed to obtain the first action parameters under the condition of not triggering abnormal signals. Based on the first action parameters, a first anomaly adjustment signal corresponding to the first traceable anomaly source is generated.

10. An active waterproof detection system for an electronic device interface, characterized in that, The steps for implementing the active waterproof detection method for an electronic device interface according to any one of claims 1 to 9 include: A sensor network configuration module is used to configure an impedance sensing network consisting of multiple electrode pairs at an electronic device interface. The signal monitoring module is used to extract the reference impedance sensing sample signal library and monitor multiple real-time impedance sensing signals of the multiple electrode pairs in real time through the impedance sensing network. The water vapor condensation port acquisition module is used to compare the multiple real-time impedance sensing signals with the reference impedance sensing sample signal library to determine whether an abnormal signal is triggered. If an abnormal signal is triggered, the multiple real-time impedance sensing signals are detected with the trained water vapor impedance feature detection model to acquire multiple water vapor condensation ports. The signal generation module is used to trace the intrusion path according to the spatial distribution of the multiple water vapor condensation ports, obtain the first traceable anomaly source, and generate a first anomaly adjustment signal based on the first traceable anomaly source.

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