Type-C interface waterproof protection method and system based on corrosion detection

Abstract

The invention discloses a Type-C interface waterproof protection method and system based on corrosion detection, and relates to the related field of electronic equipment corrosion protection, and the method comprises the steps: collecting the electricity and environment response data between interface pins when a Type-C interface is in a power-on or plugging working state; constructing a corrosion precursor feature tensor after extracting a multi-dimensional feature quantity; inputting into a corrosion evolution discrimination model, executing evolution trajectory analysis in a sliding time window, and identifying a corrosion evolution state; dynamically constructing an interface waterproof protection strategy parameter set; and when it is judged that the system is in the reversible corrosion evolution state, active recovery operation for inhibiting corrosion expansion is applied based on the interface waterproof protection strategy parameter set. According to the Type-C interface waterproof protection method and device, the technical problems that protection is passive, the state is unknown and maintenance lags in existing Type-C interface waterproof protection are solved, and the technical effects of sensing the corrosion risk in real time, dynamically implementing active protection and improving the reliability and service life of the interface are achieved.

Description

Technical Field

[0001] This application relates to the field of corrosion protection for electronic devices, and in particular to a method and system for waterproof protection of Type-C interfaces based on corrosion detection. Background Technology

[0002] The Type-C interface, with its bidirectional power supply and high-speed transmission advantages, has become a core interconnect interface in consumer electronics, industrial control, and automotive intelligence. However, it is susceptible to electrochemical corrosion of its pins under complex conditions such as humidity and salt spray, leading to signal transmission interruption, decreased power supply stability, and even short circuits, severely impacting the reliability and lifespan of precision electronic systems. Therefore, corrosion protection and waterproof sealing of the Type-C interface have become key issues in improving the environmental adaptability of high-end electronic devices. Current mainstream protection solutions are mainly passive, including hardware optimization methods such as using waterproof sealing rings, gold-plated pins, and three-proof coatings, as well as reactive maintenance methods such as manual inspection and replacement after interface failure. Existing passive protection methods can only block direct corrosion from the external environment and cannot detect the pre-corrosion state and early degradation trends inside the interface in real time. Furthermore, reactive maintenance methods suffer from response lag, making it difficult to intervene accurately in the early stages of corrosion, resulting in frequent interface corrosion problems and high maintenance costs.

[0003] Currently, the waterproof protection of the Type-C interface suffers from technical problems such as passive protection, unknown status, and delayed maintenance. Summary of the Invention

[0004] This application provides a corrosion detection-based waterproof protection method and system for Type-C interfaces. It collects three types of electrical and environmental response data—pin contact impedance, micro-leakage current, and local humidity—when the interface is powered on or during plugging / unplugging. Multidimensional features are extracted from the data to construct a corrosion precursor feature tensor. This tensor is input into a corrosion evolution discrimination model. Through sliding time window evolution trajectory analysis, latent or active corrosion states are identified. Based on the identified states, a waterproof protection strategy parameter set is dynamically constructed. For reversible latent states, active recovery operations are performed, such as interface potential adjustment, intermittent switching of operating states, or local environmental state correction, to suppress corrosion propagation. This solves the technical problems of passive protection, unknowable state, and delayed maintenance in existing Type-C interface waterproof protection systems. It achieves real-time corrosion risk detection, dynamic active protection, and improved interface reliability and lifespan.

[0005] This application provides a corrosion detection-based waterproof protection method for Type-C interfaces, comprising: when the Type-C interface is in a powered-on or plugged-in / plugged-out state, collecting electrical and environmental response data between the interface pins, the electrical and environmental response data including pin contact impedance change data, micro-leakage current change data, and interface local humidity response data; extracting multidimensional features from the electrical and environmental response data to construct a corrosion precursor feature tensor; inputting the corrosion precursor feature tensor into a corrosion evolution discrimination model, performing evolution trajectory analysis within a sliding time window, and identifying the corrosion evolution state, the corrosion evolution state including a latent state and an active state; dynamically constructing an interface waterproof protection strategy parameter set based on the identified corrosion evolution state; when it is determined that the Type-C interface is in a reversible corrosion evolution state, applying an active recovery operation to inhibit corrosion propagation based on the interface waterproof protection strategy parameter set, the active recovery operation including interface potential adjustment, intermittent switching of working state, or local environmental state correction.

[0006] In a possible implementation, the corrosion precursor feature tensor is input into a corrosion evolution discrimination model, and evolution trajectory analysis is performed within a sliding time window. The following processes are executed: a multi-level corrosion evolution discrimination pyramid structure mapped to the corrosion precursor feature tensor is constructed. This multi-level pyramid structure includes, from bottom to top, a transient disturbance layer, an evolution trend layer, and a corrosion state determination layer. In the transient disturbance layer, a first sliding time window is used to perform high-temporal-resolution change analysis on the corrosion precursor feature tensor, separating short-term disturbance components caused by insertion / removal actions, power fluctuations, or transient environmental changes, and generating a stable feature sub-tensor after disturbance suppression. In the evolution trend layer, a multi-timescale evolution trajectory is constructed on the stable feature sub-tensor according to a second sliding time window, and trend characterization parameters representing the cumulative change behavior of corrosion precursor features are extracted. The second sliding time window is larger than the first sliding time window. In the corrosion state determination layer, the trend characterization parameters are mapped to the corrosion evolution discrimination space, and the corrosion evolution state is identified through the hierarchical distribution position.

[0007] In a possible implementation, a high temporal resolution change analysis is performed on the corrosion precursor feature tensor using a first sliding time window. The following processing is performed: using the first sliding time window as the basic analysis unit, the sign, magnitude, and duration of the feature changes of the corrosion precursor feature tensor between adjacent time windows are jointly analyzed to construct a transient evolution description vector to characterize the transient behavior pattern of the feature; based on the transient evolution description vector, it is determined whether the corrosion precursor feature tensor exhibits an event-triggered transient response mode or an evolutionary cumulative shift mode within the first sliding time window. The event-triggered transient response mode corresponds to short-term reversible disturbances caused by plugging / unplugging operations, power switching, or environmental abrupt changes, while the evolutionary cumulative shift mode corresponds to directional feature shifts under corrosion triggering conditions; the feature components corresponding to the event-triggered transient response mode are subjected to transient weight attenuation processing within the first sliding time window, and the feature components determined to be corresponding to the evolutionary cumulative shift mode are used as stable candidate features to construct a stable feature sub-tensor after disturbance suppression.

[0008] In a possible implementation, a multi-timescale evolution trajectory is constructed for the stable feature sub-tensor based on the second sliding time window. Trend characterization parameters representing the cumulative change behavior of corrosion precursor features are extracted, and the following processing is performed: using the second sliding time window as an evolution analysis unit, the feature change direction, change rate, and cumulative amplitude of the stable feature sub-tensor within multiple consecutive second sliding time windows are jointly modeled to construct a trend evolution vector reflecting the long-term evolution behavior of corrosion precursor features; based on the trend evolution vector, an evolution consistency index of the stable feature sub-tensor is calculated between different second sliding time windows. The evolution consistency index is used to characterize whether feature changes maintain the same cumulative characteristic across multiple time scales; when the evolution consistency index meets a preset trend consistency condition, the corresponding corrosion precursor feature is determined to have entered a continuous evolution state, and trend characterization parameters are generated synchronously.

[0009] In a possible implementation, the following processing is performed: The corrosion evolution discrimination model adopts a structured discrimination architecture that describes the step-by-step confirmation of corrosion evolution evidence, including a transient perturbation layer, an evolution trend layer, and a corrosion state determination layer, wherein: the transient perturbation layer operates under constraints within a first sliding time window, and uses a time alignment unit to reconstruct the corrosion precursor feature tensor into a transient feature sequence with equal time steps within the first sliding time window, in order to eliminate the time jitter caused by plugging / unplugging actions and power switching; the transient symbol-amplitude joint encoding unit is used to jointly encode the feature change symbol, change amplitude, and duration between adjacent time steps to generate a transient behavior encoding vector; the transient pattern discrimination unit is used to perform pattern discrimination based on the transient behavior encoding vector, and outputs a stable feature sub-tensor after pattern filtering through a transient gating unit.

[0010] In a possible implementation, the following processing is performed: the evolution trend layer and the transient perturbation layer are connected by a one-way evidence transfer. Under the constraint of the second time window, the multi-window trajectory splicing unit splices multiple consecutive stable feature sub-tensors into an evolution trajectory segment. The direction-rate-amplitude ternary evolution modeling unit performs joint modeling of the consistency of the change direction of each feature component in the evolution trajectory segment, the stability of the change rate, and the cumulative amplitude growth, forming a trend evolution vector. The cross-window consistency verification unit outputs a consistency index. When the evolution consistency index meets the preset conditions, the corresponding trend evolution vector is marked as valid evolution evidence and allowed to be passed to the upper layer.

[0011] In a possible implementation, an active recovery operation to suppress corrosion propagation is applied based on the interface waterproof protection strategy parameter set, and the following processing is performed: the current working state of the electronic device is read, and a preset execution task is obtained; an adaptation analysis of the interface waterproof protection strategy parameter set is performed according to the current working state and the preset execution task, and an adaptation strategy order is established; the adaptation strategy order is packaged into a selection notification signal, displayed on the display screen of the electronic device, and the user's selection feedback is obtained; and an active recovery operation is performed according to the selection feedback.

[0012] In a possible implementation, the following processing is performed: After obtaining user authorization, the active recovery operation that has been performed is fully recorded, including the operation type, potential adjustment amplitude and duration, working state switching time point, and local environmental correction parameters. After synchronously recording the electrical response data and environmental response data of the interface pins, association annotation is performed, and closed-loop feedback and iterative update management are performed based on the association annotation results.

[0013] In a possible implementation, the active recovery operation is performed based on the selection feedback, and the following processing is also performed: if the user does not provide selection feedback, a threshold triggering judgment is performed based on the corrosion evolution state; if the threshold triggering judgment result is a triggering result, the first-order adaptation strategy in the adaptation strategy order is selected to perform the active recovery operation.

[0014] This application also provides a corrosion detection-based waterproof protection system for a Type-C interface, comprising: a data acquisition module for acquiring electrical and environmental response data between interface pins when the Type-C interface is powered on or plugged in / out, the electrical and environmental response data including pin contact impedance change data, micro-leakage current change data, and interface local humidity response data; a corrosion precursor feature tensor construction module for extracting multi-dimensional features from the electrical and environmental response data and constructing a corrosion precursor feature tensor; and a corrosion evolution state identification module for inputting the corrosion precursor feature tensor into... A corrosion evolution discrimination model performs evolution trajectory analysis within a sliding time window to identify corrosion evolution states, including latent and active states. A dynamic interface waterproofing protection strategy parameter set construction module dynamically constructs an interface waterproofing protection strategy parameter set based on the identified corrosion evolution states. An active recovery operation application module applies active recovery operations to suppress corrosion propagation based on the interface waterproofing protection strategy parameter set when the Type-C interface is determined to be in a reversible corrosion evolution state. These active recovery operations include interface potential adjustment, intermittent switching of operating states, or local environmental state correction.

[0015] The proposed corrosion-detection-based waterproof protection method and system for Type-C interfaces, as described in this application, firstly collects electrical and environmental response data between interface pins when the Type-C interface is powered on or plugged in / out. This data includes pin contact impedance changes, micro-leakage current changes, and local humidity response data. Next, multi-dimensional features are extracted from this data to construct a corrosion precursor feature tensor. This tensor is then input into a corrosion evolution discrimination model to perform evolution trajectory analysis within a sliding time window, identifying the corrosion evolution state, which includes latent and active states. Based on the identified corrosion evolution states, a set of interface waterproof protection strategy parameters is dynamically constructed. Finally, when the Type-C interface is determined to be in a reversible corrosion evolution state, an active recovery operation to inhibit corrosion propagation is applied based on the interface waterproof protection strategy parameter set. This active recovery operation includes interface potential adjustment, intermittent switching of operating states, or local environmental state correction. Through this process, the proposed method and system achieve the technical effects of real-time corrosion risk detection, dynamic active protection, and improved interface reliability and lifespan. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described 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 preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a flowchart illustrating the corrosion detection-based waterproof protection method for Type-C interfaces provided in this application embodiment.

[0018] Figure 2 A schematic diagram of the structure of a corrosion detection-based waterproof protection system for a Type-C interface provided in an embodiment of this application.

[0019] Figure labeling: Data acquisition module 10, corrosion precursor feature tensor construction module 20, corrosion evolution state identification module 30, interface waterproof protection strategy parameter set dynamic construction module 40, active recovery operation application module 50. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0021] This application provides a method for waterproofing Type-C interfaces based on corrosion detection, such as... Figure 1 As shown, the method includes: Step S100: When the Type-C interface is in a powered-on or plugged-in working state, collect electrical and environmental response data between the interface pins. The electrical and environmental response data includes pin contact impedance change data, micro-leakage current change data, and interface local humidity response data.

[0022] Specifically, data acquisition is performed through three types of miniature sensors and detection circuits integrated within the Type-C interface. Pin contact impedance change data is acquired using a high-frequency AC impedance detection circuit. During interface power-on / plug-out, a constant AC current is injected into the pin pair at preset time intervals. An AD converter acquires the voltage change across the pin pair and calculates the contact impedance using the formula: Impedance = Voltage / Current. For example, a voltage jump from 5mV to 20mV during plug-in / plug-out corresponds to an impedance increase from 500mΩ to 2000mΩ. Micro-leakage current change data is acquired using a zero-flux current sensor connected in parallel across the interface's insulation layer. This sensor continuously monitors the leakage current between the pins and the interface housing, recording data at preset time intervals. For example, when the ambient humidity rises to 80%RH, the leakage current increases from 0.2μA to 1.5μA. Interface local humidity response data is acquired using a miniature capacitive humidity sensor embedded in the inner wall of the interface socket. Local humidity data within the interface is collected at preset time intervals, and the timestamps of humidity changes and interface operating status are recorded synchronously. For example, if the humidity is 65%RH when plug-in / plug-out occurs. All collected data is transmitted to the cache module via the device's I2C bus and stored in the format of timestamp-data type-value.

[0023] Step S200: After extracting multidimensional features from the electrical and environmental response data, construct the corrosion precursor feature tensor.

[0024] Specifically, the timestamp-data type-numerical format data stored in the cache module is preprocessed. Outliers are removed using the 3σ criterion, and data exceeding the mean ± 3 standard deviations are marked as outliers. Missing data is filled in using linear interpolation. Data is divided into preset time segments, and multidimensional features are extracted for three types of data within each time segment. These include: pin contact impedance features (maximum, minimum, and mean impedance within the preset time segment; mean and variance of impedance change rate within a preset sliding window; preset sliding window duration of 1 / 10 to 1 / 5 of the preset time segment; and maximum duration of continuous rise / fall); micro-leakage current features (cumulative current within the preset time segment; Pearson correlation coefficient between current and humidity; duration and frequency of current exceeding a preset safety threshold); and interface local humidity features (mean and maximum humidity within the preset time segment; number and total duration of humidity exceeding a preset high humidity threshold; and mean slope of humidity change).

[0025] All features are assembled according to a three-dimensional structure of time segment-feature category-feature index to form the erosion precursor feature tensor. For example, if the total data duration is 1 hour and the preset time segment is 5 minutes, then the time segment dimension length is 12; 8 feature indices are extracted for each data category, then the tensor shape is the number of time segments × 3 (feature category) × number of feature indices, and the tensor elements are the values ​​of the corresponding features.

[0026] Step S300: The corrosion precursor feature tensor is input into the corrosion evolution discrimination model to perform evolution trajectory analysis within a sliding time window and identify the corrosion evolution state, which includes a latent state and an active state. The corrosion evolution discrimination model employs a structured discrimination architecture describing the step-by-step confirmation of corrosion evolution evidence, including a transient perturbation layer, an evolution trend layer, and a corrosion state determination layer. Specifically: the transient perturbation layer operates under constraints within the first sliding time window, using a time alignment unit to reconstruct the corrosion precursor feature tensor into a transient feature sequence with equal time steps within the first sliding time window, eliminating the time jitter caused by insertion / removal actions and power switching; the transient symbol-amplitude joint encoding unit jointly encodes the feature change symbol, change amplitude, and duration between adjacent time steps to generate a transient behavior encoding vector; the transient pattern discrimination unit performs pattern discrimination based on the transient behavior encoding vector and outputs a stable feature sub-tensor after pattern filtering through a transient gating unit. The evolution trend layer and the transient perturbation layer are connected by a one-way evidence transmission. Under the constraint of the second time window, the multi-window trajectory splicing unit splices multiple consecutive stable feature sub-tensors into an evolution trajectory segment. The direction-rate-amplitude ternary evolution modeling unit performs joint modeling of the consistency of the change direction of each feature component in the evolution trajectory segment, the stability of the change rate, and the cumulative amplitude growth, forming a trend evolution vector. The cross-window consistency verification unit outputs a consistency index. When the evolution consistency index meets the preset conditions, the corresponding trend evolution vector is marked as valid evolution evidence and allowed to be transmitted to the upper layer.

[0027] Specifically, the corrosion evolution discrimination model adopts a hierarchical processing and unidirectional evidence transmission architecture, deployed in the device's real-time processing unit and edge computing unit. The transient perturbation layer is deployed in a low-power MCU, the evolution trend layer in an edge computing module such as an FPGA, and the corrosion state determination layer in the main processor. Inter-layer communication is achieved via high-speed buses, such as the SPI bus for transmitting stable feature sub-tensors and the UART bus for transmitting valid evolution evidence.

[0028] In the transient perturbation layer, the duration of the first sliding time window is set to the typical duration of a short-term interface perturbation, such as 1 to 3 times the duration of a plugging / unplugging action, and the sliding step size is 1 / 2 to 1 / 5 of the window duration. A time alignment unit interpolates the erosion precursor feature tensor of non-uniform time steps into a transient feature sequence of equal time steps, eliminating time jitter, where the step size is adapted according to the original sampling interval. A transient symbol-amplitude joint encoding unit calculates the changing symbol, changing amplitude, and duration of features from adjacent time steps to generate a transient behavior encoding vector. A transient gating unit filters out stable feature sub-tensors based on a preset threshold; if the amplitude exceeds the threshold and the duration is short, it is determined to be a perturbation.

[0029] In the evolution trend layer, the second sliding time window is longer than the first sliding time window. A multi-window trajectory stitching unit stitches together multiple consecutive stable feature sub-tensors into an evolution trajectory segment. A direction-rate-amplitude ternary evolution modeling unit calculates the direction of change, rate of change, and cumulative amplitude of the evolution trajectory segment features to form a trend evolution vector. A cross-window consistency verification unit calculates the consistency rate of the trend evolution vectors across multiple consecutive windows. If a preset consistency condition is met, it is marked as valid evolution evidence and passed to the erosion state determination layer.

[0030] In the corrosion state determination layer, the evolutionary evidence projection unit maps valid evolutionary evidence to a preset corrosion evolution discrimination space, with the coordinate axes representing the rate of change, cumulative amplitude, and duration. The hierarchical interval determination unit determines whether the current corrosion evolution state is latent or active based on spatial partitioning rules. For example, a latent state corresponds to a low rate and small amplitude, while an active state corresponds to a high rate and large amplitude. The state credibility generation unit then generates the credibility weight for the corresponding corrosion state.

[0031] In one possible implementation, the corrosion precursor feature tensor is input into the corrosion evolution discrimination model, and evolution trajectory analysis is performed within a sliding time window. Step S300 further includes step S310, constructing a multi-level corrosion evolution discrimination pyramid structure mapped to the corrosion precursor feature tensor. This multi-level corrosion evolution discrimination pyramid structure includes, from bottom to top, a transient perturbation layer, an evolution trend layer, and a corrosion state determination layer. Specifically, the transient perturbation layer is deployed on a low-power MCU, responsible for high-frequency real-time processing; the evolution trend layer is deployed on an edge computing module, responsible for multi-scale trajectory analysis; and the corrosion state determination layer is deployed on the main processor, responsible for final state decision-making. The time step dimension of the corrosion precursor feature tensor is mapped to the time window of the transient perturbation layer, the feature category dimension is mapped to the feature components of the evolution trend layer, and the feature index dimension is mapped to the discrimination space coordinate axes of the corrosion state determination layer. The transient perturbation layer and the evolution trend layer transmit stable feature sub-tensors via an SPI bus, and the evolution trend layer and the corrosion state determination layer transmit valid evolution evidence via a UART bus.

[0032] Step S320: In the transient disturbance layer, a high temporal resolution change analysis is performed on the corrosion precursor feature tensor using a first sliding time window to separate short-term disturbance components caused by insertion / removal actions, power fluctuations, or transient environmental changes, generating a stable feature sub-tensor after disturbance suppression. Specifically, the duration of the first sliding time window is set to the typical duration of short-term interface disturbances, such as 1 to 3 times the duration of insertion / removal actions, and the sliding step size is 1 / 2 to 1 / 5 of the window duration. An interpolation algorithm, such as linear interpolation, is used to convert the non-uniformly sampled corrosion precursor feature tensor into a sequence with equal time steps, ensuring consistent temporal resolution. The change in features at each time step is calculated. If the absolute value of the change exceeds a preset threshold and recovers to the initial value within a small number of consecutive time steps, it is determined to be a short-term disturbance, such as rapid fluctuations after insertion / removal. The feature components corresponding to non-disturbances are extracted and concatenated according to the time step-feature category-feature index structure to form a stable feature sub-tensor.

[0033] Step S330: In the evolution trend layer, a multi-timescale evolution trajectory is constructed for the stable feature sub-tensor according to the second sliding time window. Trend representation parameters characterizing the cumulative change behavior of corrosion precursor features are extracted, wherein the second sliding time window is longer than the first sliding time window. Specifically, the duration of the second sliding time window is set to be longer than that of the first sliding time window, and the sliding step size is 1 / 2 to 1 / 5 of the window duration. Multiple consecutive stable feature sub-tensors are concatenated into evolution trajectory segments, and multiple consecutive trajectory segments constitute a multi-timescale trajectory. The change direction, change rate, and cumulative amplitude of the feature components of each trajectory segment are calculated and encapsulated as trend representation parameters. Among them, the change direction is the sign ratio of the continuous time window, the change rate is equal to the feature change amount / time, and the cumulative amplitude is the maximum change amount of the feature.

[0034] Step S340: In the corrosion state determination layer, the trend characterization parameters are mapped to the corrosion evolution discrimination space, and the corrosion evolution state is identified by the hierarchical distribution position. Specifically, using the rate of change, cumulative amplitude, and consistency index as coordinate axes, the corrosion evolution discrimination space is divided into two regions: a latent layer and an active layer. The threshold for each region is set according to the interface corrosion characteristics. The trend characterization parameters are used as coordinate points and input into the discrimination space. The region to which the coordinate point belongs is determined, and the corresponding corrosion evolution state, including latent state and active state, is output.

[0035] In one possible implementation, a high temporal resolution change analysis is performed on the corrosion precursor feature tensor using a first sliding time window. Step S320 further includes step S321, which uses the first sliding time window as the basic analysis unit to jointly analyze the sign, magnitude, and duration of the feature changes of the corrosion precursor feature tensor between adjacent time windows, constructing a transient evolution description vector to characterize the transient behavior pattern of the features. Specifically, using the first sliding time window as a unit, the change sign, magnitude, and duration calculations are performed on the features of each time step. Specifically, the change direction of the feature values ​​of adjacent time steps is calculated, where +1 represents an increase, -1 represents a decrease, and 0 represents equality; the absolute difference of the feature values ​​of adjacent time steps is calculated and normalized to the 0~1 interval; and the number of time steps with consecutive identical signs is calculated. The sign-magnitude-duration of each time step is concatenated to form the transient evolution description vector.

[0036] Step S322: Based on the transient evolution description vector, determine whether the corrosion precursor feature tensor exhibits an event-triggered transient response mode or an evolutionary cumulative offset mode within the first sliding time window. The event-triggered transient response mode corresponds to short-term reversible disturbances caused by plugging / unplugging operations, power switching, or sudden environmental changes, while the evolutionary cumulative offset mode corresponds to directional feature offsets under corrosion-triggered conditions. Specifically, preset determination rules for the two modes are established: the event-triggered mode satisfies: change amplitude > preset threshold, change duration < preset number of steps, and sign inversion within a few time steps; the evolutionary cumulative mode satisfies: change amplitude < preset threshold, change duration > preset number of steps, and sign continuity. Each time step of the transient evolution description vector is traversed, matching the above rules, and the mode corresponding to each feature component is marked.

[0037] Step S323: The feature components corresponding to the event-triggered transient response mode undergo transient weight decay processing within the first sliding time window. The feature components identified as corresponding to the evolutionary cumulative shift mode are selected as stable candidate features, and a stable feature sub-tensor after perturbation suppression is constructed. Specifically, for the event-triggered component, a transient weight decay algorithm is used to multiply the component's value by a preset decay coefficient, such as 0.1, reducing its weight in subsequent analysis. For the evolutionary cumulative component, the original value is retained without modification. The processed evolutionary cumulative component is then concatenated according to the time step-feature category-feature index structure to form a stable feature sub-tensor.

[0038] In one possible implementation, a multi-timescale evolution trajectory is constructed for the stable feature sub-tensor based on the second sliding time window, and trend characterization parameters representing the cumulative change behavior of corrosion precursor features are extracted. Step S330 further includes step S331, using the second sliding time window as an evolution analysis unit, jointly modeling the feature change direction, change rate, and cumulative amplitude of the stable feature sub-tensor within multiple consecutive second sliding time windows to construct a trend evolution vector reflecting the long-term evolution behavior of corrosion precursor features. Specifically, using the second sliding time window as a unit, change direction modeling, change rate modeling, and cumulative amplitude modeling are performed on each feature component of the stable feature sub-tensor. Specifically, the consistency of the change direction in multiple consecutive windows is analyzed; if all are increasing, it is marked as +1. The mean and variance of the change rate in multiple consecutive windows are calculated. The sum of the amplitudes in multiple consecutive windows is calculated. The direction-rate mean-rate variance-amplitude sum of each feature component is concatenated to form the trend evolution vector.

[0039] Step S332: Based on the trend evolution vector, calculate the evolution consistency index of the stable feature sub-tensor across different second sliding time windows. The evolution consistency index characterizes whether feature changes maintain a consistent cumulative characteristic across multiple time scales. Specifically, the evolution consistency index includes three sub-indices: directional consistency rate, rate stability, and amplitude growth, which are weighted and summed to obtain the final index. The directional consistency rate is the proportion of consecutive windows with the same direction of change; rate stability is the ratio of the variance to the mean of the rates of consecutive windows, with a smaller value indicating greater stability; amplitude growth is the ratio of the sum of the amplitudes of consecutive windows to the amplitude of the first window, with a larger value indicating more significant growth. After normalizing the sub-indices to the 0-1 interval, a weighted sum is obtained to obtain the evolution consistency index.

[0040] Step S333: When the evolution consistency index meets the preset trend consistency condition, it is determined that the corresponding corrosion precursor feature has entered a continuous evolution state, and trend characterization parameters are generated simultaneously. Specifically, if the evolution consistency index is ≥ a preset threshold, the corresponding feature is determined to have entered a continuous evolution state. For the continuously evolving feature components, the change direction, change rate, cumulative amplitude, and consistency index are encapsulated as trend characterization parameters and stored in the cache of the edge computing module.

[0041] Step S400: Based on the identified corrosion evolution state, dynamically construct the interface waterproof protection strategy parameter set.

[0042] Specifically, the system has a built-in state-policy mapping database. This database contains pre-set basic policy templates for two core states. Combined with real-time collected interface data, such as current humidity and leakage current values, and dynamically adjusted parameters, the final output is a complete parameter set including operation type, core parameters, trigger conditions, termination conditions, and execution priority. The generated interface waterproof protection policy parameter set must pass hardware compatibility and business impact verification, such as ensuring that the potential adjustment amplitude does not exceed the adjustment range of the power management IC and that the intermittent switching interval does not cause a data transmission packet loss rate exceeding 0.1%. After passing the verification, the parameter set is stored in the policy execution buffer. Operation types include interface potential adjustment, enhanced local humidity monitoring, intermittent ventilation by a micro fan, intermittent switching of operating states, and coordinated dehumidification by a PTC heater and fan.

[0043] The construction logic of the latent state (reversible state) strategy parameter set is as follows: targeting the characteristics of early corrosion, gradual feature changes and recovery through mild intervention, with low power consumption, low interference and preventive protection as the core, the parameter adjustment is based on the ratio of the current ambient humidity and leakage current deviation from the reference value. The reference value is the typical data when the interface is working normally, such as leakage current of 0.2μA and humidity of 45%RH.

[0044] The construction logic of the active state strategy parameter set is as follows: targeting the characteristics of accelerated corrosion, significant feature changes and the need for strong intervention, the core is to quickly suppress and prioritize stop loss. The parameter adjustment is based on the corrosion rate and cumulative amplitude. For example, the impedance change rate is ≥50mΩ / minute and the cumulative amplitude is ≥250mΩ, which is determined to be the active state.

[0045] Step S500: When it is determined that the Type-C interface is in a reversible corrosion evolution state, an active recovery operation to suppress corrosion propagation is applied based on the interface waterproof protection strategy parameter set. The active recovery operation includes interface potential adjustment, intermittent switching of working state, or local environmental state correction.

[0046] Specifically, the reversible corrosion evolution state refers to the latent state identified in step S300. In this state, interface corrosion is in its initial stage, with gradual changes in characteristics. Intervention measures can block the corrosion process and restore normal interface performance, unlike irreversible active corrosion that requires hardware replacement. For the reversible corrosion state, proactive intervention inhibits corrosion propagation, preventing it from deteriorating into the active state, extending the lifespan of the Type-C interface, and ensuring service continuity during intervention. The core types of proactive recovery operations include interface potential adjustment, intermittent switching of operating states, and local environmental state correction. Interface potential adjustment refers to adjusting the bias potential of the Type-C interface pins through the device's power management module, such as the PMIC chip, to create a weak electric field on the pin surface, suppressing ion migration in the electrolyte, such as water films in humid environments, and reducing the electrochemical corrosion rate. Intermittent switching of operating states refers to intermittently switching the interface between operating mode and low-power standby mode without interrupting core services, such as data transmission and charging. The small current fluctuations during mode switching dissipate humid air inside the interface and reduce heat accumulation on the pins. Local environmental condition correction refers to the use of the built-in miniature PTC heater and miniature fan in the linkage interface socket to reduce the internal humidity of the interface through the coordinated heating, dehumidification, and ventilation. During the execution of all active recovery operations, electrical and environmental response data must be continuously collected by the sensors in step S100 to monitor the changing trends of impedance, leakage current, and humidity in real time. If the relevant parameters do not decrease after a certain period of operation, the system will automatically switch to the next priority strategy.

[0047] In one possible implementation, based on the interface waterproof protection strategy parameter set, an active recovery operation to suppress corrosion propagation is applied. Step S500 further includes step S510, reading the current operating state of the electronic device and obtaining a preset execution task. Specifically, the current operating state refers to the real-time service operation state of the Type-C interface, including charging state, data transmission state, standby state, etc. The preset execution task refers to core interface-related tasks preset by the user or being executed by the device, such as firmware upgrades, video transmission, etc. The device communicates with the interface controller via the system bus to obtain the real-time operating state parameters of the interface, including service type identifier, service priority level, and remaining task duration. These parameters are stored in a temporary buffer in the format of timestamp-service type-priority-remaining duration.

[0048] Step S520: Based on the current working state and preset execution tasks, perform an adaptation analysis of the interface waterproof protection strategy parameter set and establish an adaptation strategy order. Specifically, based on the current working state of the interface and preset tasks, select active recovery strategies that will not affect task execution. Prioritize the adaptation strategies according to the principle of low to high business impact and high to low corrosion suppression effect, generating a strategy execution sequence that balances business continuity and corrosion suppression effect. Specifically, a strategy disabling list is preset for different working states. For example: in high-speed data transmission state, interface potential adjustment is disabled to avoid data packet loss due to potential fluctuations; in fast charging state, intermittent switching of working states is disabled to avoid charging power fluctuations; in standby state, all types of active recovery operations are allowed. A weighted scoring method is used, with scoring dimensions including corrosion suppression efficiency and business impact. Corrosion suppression efficiency is determined based on historical data; for example, local environment correction has the highest efficiency (90 points), followed by potential adjustment (70 points), and finally working state switching (50 points). The lower the impact on business operations, the higher the score. For example, the impact score of a strategy in standby mode is 100, while the impact score of local environmental correction in fast charging mode is 80. A strategy order list is generated from high to low based on the weighted total score. For example, the order in standby mode is: local environmental correction > interface potential adjustment > intermittent switching of working state.

[0049] Step S530: The adaptation strategies are sequentially sorted and packaged into selection notification signals, displayed on the screen of the electronic device, and user selection feedback is obtained. Specifically, the sorted strategy list is converted into a visual interface, which includes the strategy name and core principle, execution parameters, business impact prompts, and selection buttons. User operations are collected through touch events on the device's touchscreen, and feedback signals are transmitted to the strategy execution module via GPIO pins. A collection timeout is set, for example, to 30 seconds; if there is no operation within 30 seconds, it is determined that there is no feedback.

[0050] Step S540: Perform an active recovery operation based on the selected feedback. Specifically, intervene according to the user-selected strategy to ensure the operation aligns with the user's wishes. After receiving the user feedback signal, the strategy execution module parses the identifier of the target strategy. It sends control commands, containing execution parameters, to the corresponding execution hardware via the SPI bus. Real-time acquisition of electrical and environmental data from the interface is performed. If the data shows a continuous increase in corrosion parameters, the current strategy is immediately terminated, triggering the threshold judgment process in step S560.

[0051] In one possible implementation, step S500 further includes step S550, whereby, after obtaining user authorization, the executed active recovery operation is fully recorded, including the operation type, potential adjustment amplitude and duration, working state switching time point, and local environmental correction parameters. Simultaneously, the electrical response data and environmental response data of the interface pins are recorded, followed by associated annotation. Closed-loop feedback and iterative update management are then performed based on the associated annotation results. Specifically, after obtaining user authorization, the parameters of the active recovery operation are bound to the interface response data before and after the operation, forming an operation-effect correspondence. The operation record content is recorded in the format of operation ID-operation type-execution parameters-execution time-termination reason. The operation record is bound to the interface response data within a certain time range before and after the operation. The annotation rules include: if the impedance decreases by ≥50mΩ and the humidity decreases by ≥10%RH after the operation, the annotation effect is significant; if the impedance decreases by 20mΩ~50mΩ and the humidity decreases by 5%~10%RH after the operation, the annotation effect is moderate; if the parameters do not change or increase after the operation, the annotation effect is invalid. Regularly perform statistical analysis on the associated annotation data to optimize strategy parameters. For example, if the average effect of local environmental correction at 65% RH is a 12% RH reduction, then adjust the execution time at that humidity level from 6.5 minutes to 5 minutes. By optimizing strategy parameters and adaptation rules based on the associated annotation results, iterative optimization of the strategy can be achieved, improving the accuracy of subsequent corrosion suppression.

[0052] In one possible implementation, the active recovery operation is performed based on the selected feedback. Step S500 further includes step S560: if the user does not provide selected feedback, a threshold trigger judgment is performed based on the corrosion evolution state. Specifically, when the user does not provide feedback within the acquisition timeout period, it is determined whether the current corrosion evolution state has reached the threshold condition that necessitates mandatory intervention to prevent further corrosion deterioration. Thresholds for core parameters are set based on the interface corrosion characteristics, such as: pin contact impedance increasing by ≥150mΩ from the reference value, micro-leakage current ≥1.2μA, and interface local humidity ≥75%RH. An OR logic is used for judgment; if any of the above conditions are met, it is determined as a trigger result; otherwise, it is determined as a non-trigger result. For example, if the current interface humidity is 76%RH, the threshold condition is met, and it is determined as a trigger result; if the current impedance increases by 80mΩ from the reference value, the leakage current is 0.8μA, and the humidity is 60%RH, none of the conditions are met, and it is determined as a non-trigger result. The judgment result is stored in the strategy execution buffer in the format of timestamp-judgment result-current parameter value.

[0053] In step S570, if the threshold trigger determination result is a trigger result, the first-ranked adaptation strategy in the adaptation strategy order is selected to perform an active recovery operation. Specifically, when the threshold condition is triggered, the optimal adaptation strategy is automatically executed to block the corrosion process. The first-ranked strategy is retrieved from the adaptation strategy order list generated in step S520. For example, if the adaptation strategy order is Local Environment Correction > Intermittent Switching of Working State, the Local Environment Correction strategy is retrieved. If the current working state is a high-priority task, such as firmware upgrade, execution is delayed and started immediately after the task is completed. If the current working state is a low-priority task, such as standby, it is started immediately without waiting. If the interface parameters are detected to have recovered below the baseline value during execution, the operation is terminated early. The type of forced strategy, execution duration, and parameter changes are recorded in the operation record of step S550 for closed-loop feedback optimization.

[0054] This application's embodiments collect three types of electrical and environmental response data—pin contact impedance, micro-leakage current, and local humidity—when the interface is powered on or plugged in / out. Multidimensional features are extracted from the data, and a corrosion precursor feature tensor is constructed. This tensor is input into a corrosion evolution discrimination model. Through sliding time window evolution trajectory analysis, latent or active corrosion states are identified. Based on the identified states, a waterproof protection strategy parameter set is dynamically constructed. For reversible latent states, active recovery operations such as interface potential adjustment, intermittent switching of operating states, or local environmental state correction are performed to suppress corrosion propagation. This solves the technical problems of passive protection, unknown state, and lagging maintenance in existing Type-C interface waterproof protection, achieving real-time corrosion risk perception, dynamic active protection, and improved interface reliability and lifespan.

[0055] In the above text, refer to Figure 1 A corrosion detection-based waterproof protection method for Type-C interfaces according to embodiments of the present invention is described in detail. Next, reference will be made to... Figure 2 A corrosion detection-based waterproof protection system for a Type-C interface is described according to an embodiment of the present invention.

[0056] The corrosion detection-based waterproof protection system for Type-C interfaces according to embodiments of the present invention addresses the technical problems of passive protection, unknown status, and delayed maintenance in existing Type-C interface waterproof protection systems. It achieves real-time detection of corrosion risks, dynamic implementation of proactive protection, and improved interface reliability and lifespan. The corrosion detection-based Type-C interface waterproof protection system includes: a data acquisition module 10, a corrosion precursor feature tensor construction module 20, a corrosion evolution state identification module 30, a dynamic construction module for interface waterproof protection strategy parameter sets 40, and an active recovery operation application module 50.

[0057] The data acquisition module 10 is used to acquire electrical and environmental response data between the interface pins when the Type-C interface is in a powered-on or plugged-in working state. The electrical and environmental response data includes pin contact impedance change data, micro-leakage current change data, and interface local humidity response data. The corrosion precursor feature tensor construction module 20 is used to extract multi-dimensional feature quantities from the electrical and environmental response data and construct a corrosion precursor feature tensor. The corrosion evolution state identification module 30 is used to input the corrosion precursor feature tensor into the corrosion evolution discrimination model, perform evolution trajectory analysis within a sliding time window, and identify the corrosion evolution state, which includes a latent state and an active state. The interface waterproof protection strategy parameter set dynamic construction module 40 is used to dynamically construct an interface waterproof protection strategy parameter set based on the identified corrosion evolution state. The active recovery operation application module 50 is used to apply an active recovery operation to inhibit corrosion propagation based on the interface waterproof protection strategy parameter set when it is determined that the Type-C interface is in a reversible corrosion evolution state. The active recovery operation includes interface potential adjustment, intermittent switching of working state, or local environmental state correction.

[0058] The specific configuration of the corrosion evolution state identification module 30 is described in detail below: As mentioned above, the corrosion precursor feature tensor is input into the corrosion evolution discrimination model, and evolution trajectory analysis is performed within a sliding time window. The corrosion evolution state identification module 30 may further include: a pyramid structure construction unit for constructing a multi-level corrosion evolution discrimination pyramid structure mapped to the corrosion precursor feature tensor, wherein the multi-level corrosion evolution discrimination pyramid structure includes a transient perturbation layer, an evolution trend layer, and a corrosion state determination layer from bottom to top; and a high temporal resolution change analysis unit for analyzing the corrosion precursor feature tensor within the transient perturbation layer using a first sliding time window. A high temporal resolution change analysis is performed to separate short-term disturbance components caused by insertion / removal actions, power fluctuations, or transient environmental changes, generating a stable feature sub-tensor after disturbance suppression. A multi-timescale evolution trajectory construction unit is used in the evolution trend layer to construct a multi-timescale evolution trajectory of the stable feature sub-tensor according to a second sliding time window, and extract trend characterization parameters that characterize the cumulative change behavior of corrosion precursor features, wherein the second sliding time window is larger than the first sliding time window. A corrosion evolution state identification unit is used in the corrosion state determination layer to map the trend characterization parameters to the corrosion evolution discrimination space and identify the corrosion evolution state through hierarchical distribution position.

[0059] The high-temporal-resolution change analysis unit for the corrosion precursor feature tensor is further comprised of: a joint analysis subunit, which uses the first sliding time window as the basic analysis unit to jointly analyze the sign, magnitude, and duration of feature changes of the corrosion precursor feature tensor between adjacent time windows, and constructs a transient evolution description vector to characterize the transient behavior pattern of the feature; a determination subunit, which determines whether the corrosion precursor feature tensor exhibits an event-triggered transient response pattern or an evolutionary cumulative shift pattern within the first sliding time window based on the transient evolution description vector, wherein the event-triggered transient response pattern corresponds to short-term reversible disturbances caused by plugging / unplugging operations, power switching, or environmental abrupt changes, and the evolutionary cumulative shift pattern corresponds to directional feature shifts under corrosion triggering conditions; and a stable feature sub-tensor construction subunit, which performs transient weight attenuation processing on the feature components corresponding to the event-triggered transient response pattern within the first sliding time window, and uses the feature components determined to be corresponding to the evolutionary cumulative shift pattern as stable candidate features to construct a stable feature sub-tensor after disturbance suppression.

[0060] Specifically, the multi-timescale evolution trajectory of the stable feature sub-tensor is constructed based on the second sliding time window, and trend characterization parameters representing the cumulative change behavior of corrosion precursor features are extracted. The multi-timescale evolution trajectory construction unit may further include: a joint modeling sub-unit used to jointly model the feature change direction, change rate, and cumulative amplitude of the stable feature sub-tensor within multiple consecutive second sliding time windows, using the second sliding time window as the evolution analysis unit, and constructing a trend evolution vector reflecting the long-term evolution behavior of corrosion precursor features; an evolution consistency index calculation sub-unit used to calculate the evolution consistency index of the stable feature sub-tensor between different second sliding time windows based on the trend evolution vector, wherein the evolution consistency index is used to characterize whether feature changes maintain the same cumulative characteristic in multiple time scales; and a continuous evolution state determination sub-unit used to determine that the corresponding corrosion precursor feature has entered a continuous evolution state when the evolution consistency index meets a preset trend consistency condition, and simultaneously generate trend characterization parameters.

[0061] The corrosion evolution state identification module 30 may further include: the corrosion evolution discrimination model adopts a structured discrimination architecture that describes the step-by-step confirmation of corrosion evolution evidence, including a transient perturbation layer, an evolution trend layer, and a corrosion state determination layer, wherein: the transient perturbation layer operates under constraints under a first sliding time window, and uses a time alignment unit to reconstruct the corrosion precursor feature tensor into a transient feature sequence with equal time steps within the first sliding time window, which is used to eliminate the time jitter caused by plugging and unplugging actions and power switching; the transient symbol-amplitude joint encoding unit is used to jointly encode the feature change symbol, change amplitude, and duration between adjacent time steps to generate a transient behavior encoding vector; the transient pattern discrimination unit is used to perform pattern discrimination based on the transient behavior encoding vector, and outputs a stable feature sub-tensor after pattern filtering through a transient gating unit.

[0062] The corrosion evolution state identification module 30 may further include: the evolution trend layer and the transient disturbance layer are connected by a one-way evidence transmission, and a multi-window trajectory splicing unit is used to splice multiple consecutive stable feature sub-tensors into an evolution trajectory segment under the constraint of the second time window. The direction-rate-amplitude ternary evolution modeling unit performs joint modeling of the consistency of the change direction of each feature component in the evolution trajectory segment, the stability of the change rate, and the cumulative amplitude growth to form a trend evolution vector. The cross-window consistency verification unit outputs a consistency index. When the evolution consistency index meets the preset conditions, the corresponding trend evolution vector is marked as valid evolution evidence and allowed to be transmitted to the upper layer.

[0063] The specific configuration of the active recovery operation application module 50 is described in detail below: As mentioned above, the active recovery operation that suppresses corrosion propagation is applied based on the interface waterproof protection strategy parameter set. The active recovery operation application module 50 may further include: an electronic device information acquisition unit for reading the current working state of the electronic device and acquiring a preset execution task; an adaptation analysis unit for performing adaptation analysis of the interface waterproof protection strategy parameter set according to the current working state and the preset execution task, and establishing an adaptation strategy order sorting; a user feedback acquisition unit for packaging the adaptation strategy order sorting into a selection notification signal, displaying it on the display screen of the electronic device, and acquiring the user's selection feedback; and an active recovery operation execution unit for executing the active recovery operation according to the selection feedback.

[0064] The active recovery operation application module 50 may further include: after obtaining user authorization, fully recording the active recovery operation that has been executed, including operation type, potential adjustment amplitude and duration, working state switching time point, local environmental correction parameters, synchronously recording the electrical response data and environmental response data of the interface pins, performing association annotation, and performing closed-loop feedback and iterative update management based on the association annotation results.

[0065] The active recovery operation application module 50, which performs the active recovery operation based on the selected feedback, may further include: a forced execution discrimination unit for performing a threshold trigger discrimination based on the corrosion evolution state if the user does not provide selection feedback; and an active recovery operation execution unit for selecting the first-order adaptation strategy in the adaptation strategy order and performing the active recovery operation if the threshold trigger discrimination result is a trigger result.

[0066] The corrosion-detection-based waterproof protection system for Type-C interfaces provided in this invention can execute the corrosion-detection-based waterproof protection method for Type-C interfaces provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method.

[0067] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. 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 each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A waterproof protection method for Type-C interfaces based on corrosion detection, characterized in that, The method includes: When the Type-C interface is powered on or plugged in, electrical and environmental response data between the interface pins are collected. The electrical and environmental response data includes pin contact impedance change data, micro-leakage current change data, and interface local humidity response data. After extracting multidimensional features from the electrical and environmental response data, a corrosion precursor feature tensor is constructed. The corrosion precursor feature tensor is input into the corrosion evolution discrimination model, and the evolution trajectory analysis within the sliding time window is performed to identify the corrosion evolution state, which includes a latent state and an active state. Based on the identified corrosion evolution state, a parameter set for the interface waterproof protection strategy is dynamically constructed. When the Type-C interface is determined to be in a reversible corrosion evolution state, an active recovery operation to suppress corrosion propagation is applied based on the interface waterproof protection strategy parameter set. The active recovery operation includes interface potential adjustment, intermittent switching of working state, or local environmental state correction.

2. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 1, characterized in that, The corrosion precursor feature tensor is input into the corrosion evolution discrimination model, and evolution trajectory analysis is performed within a sliding time window, including: A multi-level corrosion evolution discrimination pyramid structure is constructed based on the corrosion precursor feature tensor mapping. The multi-level corrosion evolution discrimination pyramid structure includes a transient perturbation layer, an evolution trend layer, and a corrosion state determination layer from bottom to top. In the transient disturbance layer, the corrosion precursor feature tensor is analyzed with high temporal resolution using the first sliding time window to separate the short-term disturbance components caused by insertion / removal actions, power fluctuations or transient environmental changes, and generate a stable feature sub-tensor after disturbance suppression. In the evolution trend layer, a multi-timescale evolution trajectory is constructed for the stable feature sub-tensor according to the second sliding time window, and trend characterization parameters that characterize the cumulative change behavior of corrosion precursor features are extracted, wherein the second sliding time window is larger than the first sliding time window; In the corrosion state determination layer, the trend characterization parameters are mapped to the corrosion evolution discrimination space, and the corrosion evolution state is identified by the hierarchical distribution position.

3. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 2, characterized in that, High temporal resolution variation analysis of the corrosion precursor characteristic tensor is performed using the first sliding time window, including: Using the first sliding time window as the basic analysis unit, the characteristic change sign, change amplitude and change persistence of the corrosion precursor characteristic tensor between adjacent time windows are jointly analyzed to construct a transient evolution description vector for characterizing the characteristic transient behavior pattern. Based on the transient evolution description vector, it is determined whether the corrosion precursor feature tensor exhibits an event-triggered transient response mode or an evolution-cumulative offset mode within the first sliding time window. The event-triggered transient response mode corresponds to short-term reversible disturbances caused by plugging / unplugging operations, power switching, or sudden environmental changes, while the evolution-cumulative offset mode corresponds to directional feature offsets under corrosion-triggered conditions. The feature components corresponding to the event-triggered transient response mode are subjected to transient weight decay processing within the first sliding time window. The feature components that are determined to be corresponding to the evolutionary cumulative offset mode are used as stable candidate features, and the stable feature sub-tensor after perturbation suppression is constructed.

4. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 3, characterized in that, Based on the second sliding time window, a multi-timescale evolution trajectory is constructed for the stable feature sub-tensor, and trend characterization parameters representing the cumulative change behavior of corrosion precursor features are extracted, including: Using the second sliding time window as the evolution analysis unit, the characteristic change direction, change rate and cumulative magnitude of the stable feature sub-tensor in multiple consecutive second sliding time windows are jointly modeled to construct a trend evolution vector that reflects the long-term evolution behavior of corrosion precursor features. Based on the trend evolution vector, the evolution consistency index of the stable feature sub-tensor is calculated between different second sliding time windows. The evolution consistency index is used to characterize whether the feature changes maintain the same cumulative property across multiple time scales. When the evolution consistency index meets the preset trend consistency condition, it is determined that the corresponding corrosion precursor feature has entered a continuous evolution state, and trend characterization parameters are generated simultaneously.

5. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 4, characterized in that, The corrosion evolution discrimination model adopts a structured discrimination architecture that describes the step-by-step confirmation of corrosion evolution evidence, including a transient disturbance layer, an evolution trend layer, and a corrosion state determination layer, wherein: The transient disturbance layer operates under constraints within the first sliding time window. It uses a time alignment unit to reconstruct the erosion precursor feature tensor into a transient feature sequence with equal time steps within the first sliding time window, thereby eliminating the time jitter caused by plugging / unplugging actions and power switching. The transient symbol-amplitude joint coding unit is used to jointly encode the characteristic change symbol, change amplitude and duration between adjacent time steps to generate a transient behavior coding vector. The transient pattern discrimination unit is used to perform pattern discrimination based on the transient behavior encoding vector, and outputs the stable feature sub-tensor after pattern filtering through the transient gating unit.

6. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 5, characterized in that, The evolution trend layer and the transient perturbation layer are connected by a one-way evidence transmission. Under the constraint of the second time window, the multi-window trajectory splicing unit splices multiple consecutive stable feature sub-tensors into an evolution trajectory segment. The direction-rate-amplitude ternary evolution modeling unit performs joint modeling of the consistency of the change direction of each feature component in the evolution trajectory segment, the stability of the change rate, and the cumulative amplitude growth, forming a trend evolution vector. The cross-window consistency verification unit outputs a consistency index. When the evolution consistency index meets the preset conditions, the corresponding trend evolution vector is marked as valid evolution evidence and allowed to be transmitted to the upper layer.

7. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 1, characterized in that, Based on the interface waterproof protection strategy parameter set, an active recovery operation to inhibit corrosion propagation is applied, including: Read the current operating status of the electronic device and obtain the preset task to be executed; Based on the current working state and preset execution tasks, perform an adaptation analysis of the interface waterproof protection strategy parameter set and establish an adaptation strategy order sorting. The adaptation strategies are sequentially sorted and packaged into selection notification signals, displayed on the screen of the electronic device, and user selection feedback is obtained. Based on the selected feedback, an active recovery operation is performed.

8. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 7, characterized in that, After obtaining user authorization, the active recovery operation performed is fully recorded, including operation type, potential adjustment amplitude and duration, working state switching time point, local environmental correction parameters, and the electrical response data and environmental response data of the interface pins are recorded simultaneously. Then, the association annotation is performed, and closed-loop feedback and iterative update management are carried out based on the association annotation results.

9. The waterproof protection method for Type-C interface based on corrosion detection as described in claim 7, characterized in that, Performing an active recovery operation based on the selected feedback also includes: If the user does not provide feedback, a threshold trigger judgment will be executed based on the corrosion evolution state. If the threshold trigger determination result is a trigger result, then the first-ranked adaptation strategy in the adaptation strategy order will be selected to perform the active recovery operation.

10. A Type-C interface waterproof protection system based on corrosion detection, characterized in that, The system is used to implement the corrosion detection-based waterproof protection method for Type-C interfaces according to any one of claims 1-9, and the system comprises: The data acquisition module is used to collect electrical and environmental response data between the interface pins when the Type-C interface is powered on or plugged in. The electrical and environmental response data includes pin contact impedance change data, micro-leakage current change data, and interface local humidity response data. The corrosion precursor feature tensor construction module is used to extract multidimensional features from the electrical and environmental response data and then construct the corrosion precursor feature tensor. The corrosion evolution state identification module is used to input the corrosion precursor feature tensor into the corrosion evolution discrimination model, perform evolution trajectory analysis within a sliding time window, and identify the corrosion evolution state, which includes a latent state and an active state. A dynamic construction module for interface waterproof protection strategy parameter set is used to dynamically construct the interface waterproof protection strategy parameter set based on the identified corrosion evolution state. The active recovery operation application module is used to apply an active recovery operation to suppress corrosion propagation based on the interface waterproof protection strategy parameter set when it is determined that the Type-C interface is in a reversible corrosion evolution state. The active recovery operation includes interface potential adjustment, intermittent switching of working state, or local environmental state correction.

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