A method for detecting corrosion resistance of a cable

Abstract

The application relates to the technical field of cable detection, in particular to a cable corrosion resistance detection method. The technical scheme is as follows: a cable corrosion resistance detection method comprises the following steps: S1, placing a cable sample to be detected into a detection container, injecting corrosion liquid, sealing, collecting an initial reference image of the surface of the cable sample; S2, in the corrosion process, the following operations are synchronously executed: S2.1, automatically collecting an image sequence of the surface of the cable sample at a set time interval; S2.2, monitoring the environmental parameters of the corrosion liquid; S3, comparing the image sequence obtained in the step S2.1 with the initial reference image, and quantifying the space-time evolution information of corrosion through an image recognition algorithm. Through the combination of a sealed environment and high-precision image collection technology, the whole corrosion process is visualized and monitored, and the accuracy and reliability of corrosion identification are greatly improved.

Description

Technical Field

[0001] This invention relates to the field of cable testing technology, and in particular to a method for testing the corrosion resistance of cables. Background Technology

[0002] As a critical carrier for power transmission and signal transmission, the corrosion resistance of cables, which operate in complex environments for extended periods, directly impacts the safe operation of power systems. Traditional methods for testing cable corrosion resistance primarily employ static immersion tests, placing cable samples in a corrosive solution of fixed concentration and assessing corrosion resistance by periodically observing surface changes or measuring weight loss. These methods have significant limitations: firstly, they cannot simulate the dynamic corrosion environment resulting from the coupled effects of multiple factors such as temperature and pressure in real-world operating conditions; secondly, they rely on periodic manual observation, making it difficult to capture the continuous process of corrosion initiation and development; and thirdly, they lack in-depth analysis of corrosion mechanisms, providing only apparent corrosion results without revealing corrosion kinetics. While some improvements have been made in existing technologies, such as using accelerated corrosion test chambers, key issues such as multi-parameter collaborative monitoring, in-situ real-time analysis, and corrosion mechanism modeling remain unresolved. This leads to significant discrepancies between test results and actual conditions, making it difficult to accurately assess and predict the durability of cable materials.

[0003] Based on the limitations of traditional cable corrosion resistance testing methods, this invention aims to solve the following core technical problems: Traditional static immersion tests cannot simulate the dynamic corrosion environment caused by the coupling of multiple factors such as temperature and pressure in real working conditions, resulting in significant deviations between the test results and actual service conditions; methods relying on periodic manual observation are difficult to capture the continuous process of corrosion initiation and development, and lack the ability to deeply analyze the corrosion mechanism; although existing improved schemes use accelerated corrosion test chambers, they still fail to achieve key requirements such as multi-parameter collaborative monitoring, in-situ real-time analysis, and corrosion mechanism modeling. Summary of the Invention

[0004] This invention proposes a method for testing the corrosion resistance of cables, which solves the problems of existing methods for testing cable corrosion resistance being unable to realistically simulate dynamic corrosion environments, achieve multi-parameter collaborative monitoring and intelligent control, and lack the ability to analyze corrosion mechanisms, resulting in a disconnect between test results and actual service conditions.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0006] A method for testing the corrosion resistance of cables includes the following steps:

[0007] S1. Sealed environment construction and baseline acquisition steps: Place the cable sample to be tested into the test container and inject the etching solution, then seal it. Before the etching begins, acquire an initial baseline image of the cable sample surface using an image acquisition device.

[0008] S2. Multimodal in-situ monitoring and dynamic control steps: During the corrosion process, the following operations are performed simultaneously:

[0009] S2.1 Visual monitoring: Automatically acquire image sequences of the cable sample surface at set time intervals;

[0010] S2.2 Environmental monitoring and control: Monitor the environmental parameters of the corrosive liquid, and actively control the detection environment by pressure cycling and / or temperature cycling based on preset programs or real-time monitoring data;

[0011] S3. Data fusion and mechanism analysis steps: Compare the image sequence obtained in step S2.1 with the initial reference image, quantify the spatiotemporal evolution information of corrosion through image recognition algorithm; and associate and match this evolution information with the time series data of environmental parameters obtained synchronously in step S2.2 to establish a correspondence model between corrosion kinetic behavior and environmental stress, thereby generating a quantitative evaluation index of cable corrosion resistance performance.

[0012] Furthermore, in step S3, the image recognition algorithm specifically performs the following operations: performing pixel-level difference calculations between each frame image and the initial reference image to identify newly formed corrosion areas, and calculating the uniform corrosion area growth rate and pitting density growth rate respectively.

[0013] Furthermore, in steps S2.1 and S2.2, a unified timestamp is assigned to each set of synchronously acquired image data and environmental parameter data; the association matching in step S3 is based on the unified timestamp to accurately correspond the changes in corrosion morphology with the fluctuations in environmental parameters on the time axis.

[0014] Furthermore, the dynamic control in step S2.2 is an adaptive feedback control based on the correspondence model; specifically: when the pitting density growth rate exceeds the first threshold, the frequency of pressure cycling is automatically increased; when the uniform corrosion area growth rate exceeds the second threshold, the amplitude of temperature cycling is automatically increased.

[0015] Furthermore, the pressure cycle and temperature cycle in step S2.2 are coupled, and the coupling mode is as follows: heating is performed simultaneously in the low-pressure stage of the pressure cycle, and cooling is performed simultaneously in the high-pressure stage, so as to simulate the harsh working conditions of alternating heat and humidity and sudden pressure changes.

[0016] Furthermore, the correspondence model established in step S3 is used to predict the critical environmental conditions for pitting corrosion, including the critical chloride ion concentration and the critical temperature.

[0017] Furthermore, in step S2, the accumulation of corrosion ion concentration is indirectly assessed by monitoring the change in conductivity of the corrosive solution; when the rate of change in conductivity tends to level off, an automatic prompt message is triggered, indicating that the effective data acquisition cycle of this test has ended.

[0018] Furthermore, the quantitative evaluation index generated in step S3 is a comprehensive corrosion index, which is calculated by weighting the uniform corrosion area growth rate, the pitting density growth rate, and the predicted value of critical environmental conditions.

[0019] A cable corrosion resistance testing system for implementing the aforementioned cable corrosion resistance testing method, the system comprising:

[0020] Sealed testing container for holding cable samples and corrosive solutions;

[0021] Image acquisition device, used to acquire initial reference images and monitoring image sequences of cable samples;

[0022] An environmental parameter sensor array is used to monitor the temperature, pH value, and conductivity of the corrosive solution;

[0023] The environmental control module includes a pressure control unit and a temperature control unit, which are used to couple or independently control the internal environment of the detection container;

[0024] The data processing and control center is communicatively connected to the image acquisition device, sensor group, and control module, and is configured to execute the image recognition algorithm, establish the correspondence model, and implement adaptive feedback control.

[0025] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the cable corrosion resistance testing method.

[0026] The positive effects of this invention are:

[0027] By employing multimodal in-situ monitoring and dynamic control technology, a significant breakthrough has been achieved in evaluating cable corrosion resistance, moving from a single indicator to a comprehensive multidimensional assessment. Through the combination of sealed environment construction and high-precision image acquisition technology, full-process visual monitoring of the corrosion process has been realized. Pixel-level differential algorithms can accurately identify micron-level corrosion features, and morphological processing technology effectively distinguishes between uniform corrosion and pitting corrosion, significantly improving the accuracy and reliability of corrosion identification.

[0028] A pressure-temperature coupled control mechanism is employed, using a collaborative control algorithm to accurately reproduce harsh operating conditions such as alternating damp heat and sudden pressure changes. An adaptive feedback control system dynamically adjusts experimental parameters based on real-time monitoring data, ensuring the accelerated testing is both effective and maintains the authenticity of the corrosion mechanism. Based on multi-source data fusion analysis technology, a precise correspondence model between corrosion morphology evolution and environmental parameter changes is established. The application of machine learning algorithms enables the prediction of critical environmental conditions for pitting corrosion, providing a scientific basis for corrosion risk early warning. By establishing a comprehensive corrosion index evaluation system, multiple corrosion characteristic parameters are weighted and fused to form a unified quantitative evaluation standard. This system solves the problem of single evaluation indicators in traditional methods, enabling objective comparison of the corrosion resistance performance of different cable samples.

[0029] The fully automated testing process eliminates human error, and the intelligent data acquisition and processing system significantly shortens the testing cycle. The system possesses self-diagnostic and adaptive capabilities, automatically optimizing testing parameters based on the corrosion process, thus significantly improving testing efficiency while ensuring testing quality. This represents a significant shift in cable corrosion resistance testing from experience-based judgment to scientific prediction, providing a reliable technical means for cable product quality assessment and lifespan prediction. Detailed Implementation

[0030] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0031] Example 1

[0032] A method for testing the corrosion resistance of cables includes the following steps:

[0033] S1. Sealed environment construction and baseline acquisition steps: Place the cable sample to be tested into the test container and inject the etching solution, then seal it. Before the etching begins, acquire an initial baseline image of the cable sample surface using an image acquisition device.

[0034] S2. Multimodal in-situ monitoring and dynamic control steps: During the corrosion process, the following operations are performed simultaneously:

[0035] S2.1 Visual monitoring: Automatically acquire image sequences of the cable sample surface at set time intervals;

[0036] S2.2 Environmental monitoring and control: Monitor the environmental parameters of the corrosive liquid, and actively control the detection environment by pressure cycling and / or temperature cycling based on preset programs or real-time monitoring data;

[0037] S3. Data fusion and mechanism analysis steps: Compare the image sequence obtained in step S2.1 with the initial reference image, quantify the spatiotemporal evolution information of corrosion through image recognition algorithm; and associate and match this evolution information with the time series data of environmental parameters obtained synchronously in step S2.2 to establish a correspondence model between corrosion kinetic behavior and environmental stress, thereby generating a quantitative evaluation index of cable corrosion resistance performance.

[0038] Specifically, the steps for constructing the S1 sealed environment and acquiring the baseline are as follows. The core technology lies in establishing a precise reference frame. A high-resolution industrial camera is used in conjunction with a multi-angle light source system, and polarization filtering technology is employed to eliminate interference from reflections on the metal surface. During image acquisition, fixed object distance and aperture parameters are set to ensure spatial consistency for subsequent image comparisons. After the reference image acquisition is completed, a feature point matching algorithm is used to perform 3D reconstruction of the multi-view images, establishing a digital twin model of the cable surface, providing a geometric benchmark for subsequent spatiotemporal evolution analysis.

[0039] The S2 multimodal in-situ monitoring and dynamic control system employs a visual monitoring system that combines timed triggering and event triggering. Timed triggering acquires images at preset intervals (e.g., every minute), while event triggering automatically increases the acquisition frequency when environmental parameters change abruptly. The environmental monitoring system collects parameters such as temperature and pressure in real time through a distributed sensor network and uses a Kalman filter algorithm to smooth the raw data. The dynamic control system, based on fuzzy control theory, establishes a transfer function model of corrosion rate and environmental parameters, and adjusts the parameters of pressure and temperature cycles by solving the optimal control problem in real time.

[0040] The S3 data fusion and mechanism analysis steps are characterized by three key technical features:

[0041] First, at the image recognition algorithm level, a deep convolutional neural network is used to achieve pixel-level semantic segmentation. The network structure adopts an encoder-decoder architecture, where the encoder extracts multi-scale features through a residual network, and the decoder achieves feature map upsampling through transposed convolution. During training, a focal loss function is used to address the sample imbalance problem between eroded and non-eroded regions. For spatiotemporal evolution analysis, a motion estimation algorithm based on optical flow is developed, which quantifies the expansion speed and direction of the eroded region by calculating the displacement vectors of pixels between adjacent frames.

[0042] Secondly, at the data association and matching level, a dynamic time warping algorithm is used to solve the alignment problem of data with different sampling rates. By establishing a time-series database of corrosion characteristic parameters and environmental parameters, cross-correlation analysis is used to quantitatively assess the impact of different environmental factors on corrosion development. Simultaneously, principal component analysis is applied to reduce dimensionality and extract key environmental factors affecting corrosion development.

[0043] Finally, at the model building level, a physical information neural network method is used to construct a corrosion kinetic model. Physical constraints such as the laws of conservation of mass and charge are embedded in the neural network training process, and the evolution of the corrosion process is described by solving partial differential equations. The model input includes time-series data of environmental parameters and initial boundary conditions, and the output is the predicted result of the corrosion depth distribution changing over time.

[0044] System collaborative working mechanism: Subsystems interact via a data bus, employing a publish-subscribe model for loosely coupled communication. The central controller ensures time consistency of multi-source data through a timestamp synchronization mechanism and leverages database transaction characteristics to guarantee data integrity and reliability. The system also includes an anomaly handling mechanism; when monitored data deviates from expected ranges, a fault diagnosis program is automatically initiated to ensure the continuity of the detection process and the reliability of the results.

[0045] This implementation method achieves accurate monitoring and scientific evaluation of cable corrosion processes by organically combining deep learning image analysis technology, multi-source data fusion algorithms, and physical mechanism modeling methods, providing a complete technical solution for the durability assessment of cable materials.

[0046] Through sealed environment construction and baseline acquisition, a precise digital benchmark model of the cable surface was established using a high-resolution image acquisition system and 3D reconstruction technology. This eliminated environmental interference and ensured the geometric consistency and data comparability of the initial detection state. In the multimodal in-situ monitoring and dynamic control step, comprehensive real-time data acquisition of the corrosion process was achieved through dual-mechanism visual monitoring (timed and event-triggered), a distributed sensor network, and a Kalman filter algorithm. Furthermore, the pressure-temperature cycle parameters were dynamically optimized using fuzzy control theory, significantly improving the accuracy of environmental simulation and the adaptability of detection conditions. In the data fusion and mechanism analysis step, pixel-level semantic segmentation technology based on deep convolutional neural networks enabled accurate identification and quantification of corrosion areas. Simultaneously, through dynamic time warping algorithms and physical information neural network models, multi-source heterogeneous data was deeply integrated with the physical laws of corrosion kinetics. This not only accurately revealed the spatiotemporal evolution of corrosion but also enabled the prediction of corrosion development rates, thereby improving the overall accuracy, reliability, and efficiency of the detection results and providing a scientific basis for the durability assessment of cable materials.

[0047] Example 2

[0048] Based on Example 1: In step S3, the image recognition algorithm specifically performs the following operations: performs pixel-level difference calculation between each frame image and the initial reference image to identify newly formed corrosion areas, and calculates the uniform corrosion area growth rate and pitting density growth rate respectively.

[0049] In the pixel-level difference calculation stage, a pixel-by-pixel comparison algorithm based on the grayscale matrix is ​​adopted. First, the reference image and the real-time image are converted to grayscale images, and Gaussian filtering is used to eliminate noise interference. The difference calculation uses the formula ΔI(x,y)=|I_t(x,y)-I_0(x,y)|, where I_t is the pixel value of the real-time image, and I_0 is the pixel value at the corresponding position in the reference image. A threshold T=25 is set; when ΔI(x,y)≥T, the pixel is determined to belong to the eroded region.

[0050] For erosion region identification, morphological opening operations are used to process the binarized difference image to eliminate isolated noise points. Connected erosion regions are identified using a region growing algorithm and classified according to their morphological characteristics: regions with an area greater than a set threshold and a regular shape are identified as uniform erosion regions; regions with a smaller area and a discrete distribution are identified as pitting erosion regions.

[0051] The uniform corrosion area growth rate is calculated using the formula: R_u=(A_t-A_0) / (t·A_0), where A_t is the total area of ​​the corrosion region at the current moment, A_0 is the initial area, and t is the time interval. The pitting corrosion density growth rate is calculated using the formula: R_p=(N_t-N_0) / (t·S), where N_t is the number of pits at the current moment, N_0 is the initial number of pits, and S is the sample surface area.

[0052] The above formulas achieve precise characterization of corrosion development through quantitative calculations. The ΔI(x,y) formula effectively identifies minute corrosion changes through pixel-level differentiation, and the threshold T=25 is optimized based on a large amount of experimental data, which can suppress noise interference while ensuring detection sensitivity. The R_u formula reflects the development speed of uniform corrosion through the area change rate, and its normalization process eliminates the influence of sample size; the R_p formula characterizes the local corrosion tendency through the change in the number of pitting corrosions per unit area. The combination of these two indicators can comprehensively evaluate the corrosion resistance of the cable.

[0053] By employing pixel-level differential calculation and corrosion region classification algorithms, precise quantitative analysis of the cable corrosion process was achieved. Its beneficial effects are mainly reflected in improved accuracy and automation of corrosion detection: the differential algorithm based on the grayscale matrix can effectively identify minute corrosion initiation points, avoiding subjective errors from manual interpretation; automatic corrosion region classification achieved through morphological processing and region growing algorithms can accurately distinguish between different development patterns of uniform corrosion and pitting corrosion; and the use of dual evaluation indicators—area growth rate and pitting density growth rate—provides comprehensive data support for corrosion development trend analysis. These technical effects collectively enhance the accuracy and reliability of corrosion assessment.

[0054] In steps S2.1 and S2.2, a unified timestamp is assigned to each set of synchronously acquired image data and environmental parameter data; the association matching in step S3 is based on the unified timestamp to accurately correspond the changes in corrosion morphology with the fluctuations in environmental parameters on the time axis.

[0055] Timestamp generation utilizes a high-precision real-time clock module, appending complete time information (year-month-day-hour-minute-second-millisecond) to each data set. Synchronous acquisition is achieved through hardware trigger signals; when the image acquisition device begins exposure, a synchronization signal is simultaneously sent to all environmental sensors to ensure consistent data acquisition start times.

[0056] For data association and matching, a time-series database is constructed to store all monitoring data. A dynamic time warping algorithm is used to address the alignment problem of data with different sampling rates, such as image data sampling rate of 1 frame / minute and environmental parameter sampling rate of 1 time / second. The optimal path is found to align the two time series, establishing a one-to-one data correspondence.

[0057] The accurate correspondence relies on temporal interpolation techniques. For any given time point, the environmental parameter value at that moment is calculated through linear interpolation of adjacent sampling points. Simultaneously, a sliding window averaging method is used to smooth data fluctuations and ensure the stability of the correlation analysis.

[0058] The time synchronization mechanism ensures the synchronization of data acquisition through hardware triggering, with timestamp accuracy reaching the millisecond level, providing a reliable time reference for subsequent data analysis. The dynamic time warping algorithm effectively solves the alignment problem of data with different sampling rates by minimizing the distance metric between two time series. The linear interpolation formula y=y_0+(y_1-y_0)*(x-x_0) / (x_1-x_0) achieves accurate estimation of environmental parameters at any given time. The sliding window averaging method smooths random fluctuations using the formula y'i=1 / n∑{j=im}^{i+m}y_j. The combination of these methods ensures a precise correspondence between corrosion morphology and changes in environmental parameters.

[0059] By establishing a unified timestamp-based synchronous acquisition mechanism, this claim achieves precise temporal alignment of multi-source monitoring data. Its technical advantages are reflected in the following aspects: the hardware-triggered synchronization mechanism ensures the temporal consistency of data at different sampling rates, laying a solid foundation for subsequent correlation analysis; the application of the dynamic time warping algorithm effectively solves the matching problem of heterogeneous data, achieving a precise correspondence between corrosion morphology and environmental parameter changes; and the introduction of time interpolation technology makes continuous time series analysis possible. These effects collectively construct a high-precision time series data analysis platform, providing a reliable technical means for corrosion mechanism research.

[0060] The dynamic control in step S2.2 is an adaptive feedback control based on the correspondence model; specifically: when the pitting density growth rate exceeds the first threshold, the frequency of pressure cycling is automatically increased; when the uniform corrosion area growth rate exceeds the second threshold, the amplitude of temperature cycling is automatically increased.

[0061] The adaptive feedback control system is based on the closed-loop control principle. The system monitors the pitting density growth rate R_p and the uniform corrosion area growth rate R_u in real time and compares them with preset thresholds. When R_p exceeds the first threshold (e.g., 0.5 pits / cm²·h), the controller adjusts the pressure cycle frequency according to a proportional-integral relationship, with the adjustment amount Δf = K_p·e_p + Ki·∫e_p dt, where e_p is the deviation of R_p from the threshold.

[0062] A similar strategy is used to regulate the temperature cycling amplitude. When R_u exceeds the second threshold (e.g., 0.1% / h), the temperature amplitude is adjusted according to ΔA = K_p'·e_u + Ki'·∫e_u dt. The control system incorporates an anti-saturation mechanism to prevent excessive accumulation of the integral term, which could lead to overshoot. Dead-zone control is also introduced; no adjustment is made when the deviation is within the allowable range to avoid frequent actions.

[0063] The PID control formula achieves precise control of the system by rapidly responding to deviation changes with the proportional term and eliminating steady-state errors with the integral term. The proportional coefficients K_p and K_p' are determined based on the system response characteristics, while the integral coefficients K_i and K_i' are optimized according to system stability requirements. The anti-saturation mechanism prevents system oscillation by limiting the cumulative range of the integral term, and dead-zone control avoids unnecessary adjustments by setting an insensitive region. The combination of these control strategies ensures that the system is both fast-responding and stable and reliable.

[0064] The adaptive feedback control system of this claim achieves intelligent control of the detection process. Its key advantages are: a closed-loop control strategy based on real-time monitoring data dynamically optimizes test conditions according to the corrosion development state; a proportional-integral control algorithm ensures rapid response and stability of the control process; and a threshold triggering mechanism enables targeted control of different corrosion modes. This intelligent control method not only improves test efficiency but also ensures consistency between the accelerated test and the actual corrosion process.

[0065] The pressure cycle and temperature cycle in step S2.2 are coupled. The coupling mode is as follows: heating is carried out simultaneously in the low-pressure stage of the pressure cycle, and cooling is carried out simultaneously in the high-pressure stage to simulate the harsh working conditions of alternating heat and humidity and sudden pressure changes.

[0066] Pressure-temperature coupled control is achieved through a collaborative control algorithm. During the low-pressure phase of the pressure cycle (e.g., 10-50 kPa), the heating device is simultaneously activated, with the heating rate controlled at 3-5 °C / min. The pressure-reducing boiling effect promotes the penetration of the corrosive medium, while the temperature increase accelerates the electrochemical reaction rate.

[0067] During the high-pressure phase (100-200 kPa), the cooling system is activated simultaneously, with the cooling rate controlled at 2-4 °C / min. Pressure increase suppresses bubble formation, while cooling simulates a condensation environment. The coupled control employs a master-slave mode, with pressure cycling as the primary timing sequence and temperature control following as a secondary system.

[0068] The coupled control enhances the corrosion process through the synergistic effect of pressure and temperature. The low-pressure heating stage utilizes the Clapeyron equation dP / dT=ΔH / (TΔV) to lower the boiling point and promote medium penetration by utilizing the pressure drop. The high-pressure cooling stage suppresses gas evolution through Henry's law C=kP·P_gas. The heating rate of 3-5℃ / min and the cooling rate of 2-4℃ / min are set based on material thermal stress analysis to ensure that accelerated corrosion is achieved without damaging the sample. This coupled mode effectively simulates the alternating humid and hot environment in actual working conditions.

[0069] This invention achieves accurate simulation of complex operating conditions through a pressure-temperature coupled control mechanism. Its technical advantages are mainly reflected in: the collaborative control algorithm reproducing harsh environmental conditions such as alternating humidity and heat, and sudden pressure changes; the master-slave control mode ensuring the synchronization and coordination of multi-parameter control; and the coupling mechanism based on physicochemical principles effectively enhancing the corrosion process. This coupled control method significantly improves the realism of operating condition simulation in accelerated laboratory tests.

[0070] The correspondence model established in step S3 is used to predict the critical environmental conditions for pitting corrosion, including the critical chloride ion concentration and the critical temperature.

[0071] The correspondence model was established using multiple regression analysis. The pitting corrosion rate was used as the dependent variable, and environmental parameters as independent variables. The regression coefficients were solved using the least squares method. The model form is: P = β_0 + β_1·C + β_2·T + β_3·pH + ε, where P is the pitting corrosion probability, C is the chloride ion concentration, and T is the temperature.

[0072] Critical environmental conditions were determined using the limit state method. Environmental parameter values ​​were gradually increased, and the change in pitting corrosion rate was observed. The parameter value corresponding to a pitting corrosion probability exceeding 50% was considered the critical condition. Model validation employed leave-one-out cross-validation to ensure prediction accuracy.

[0073] The regression model P = β_0 + β_1·C + β_2·T + β_3·pH + ε uses the coefficient β_i to quantitatively characterize the influence of various environmental factors on pitting corrosion. The least squares method solves for the optimal coefficients by minimizing the sum of squared residuals ∑ε_i^2, ensuring the model's goodness of fit. The critical condition of 50% probability is set based on reliability theory, indicating that the corrosion risk has reached an acceptable upper limit. Leave-one-out cross-validation ensures the model's generalization ability through repeated training and validation; its formula MSE = 1 / n∑(y_i - ŷ_i)^2 evaluates the prediction accuracy.

[0074] The predictive model established in this claim enables early warning of corrosion risks. Its beneficial effects are as follows: the multiple regression model quantitatively reveals the intrinsic relationship between environmental parameters and pitting corrosion development; the critical conditions determined by the limit state method provide a scientific basis for safety assessment; and the cross-validation mechanism ensures the reliability of the predictive model. These effects provide important technical support for the durability design and safe operation and maintenance of cable materials.

[0075] In step S2, the accumulation of corrosion ion concentration is indirectly assessed by monitoring the change in conductivity of the corrosive solution; when the rate of change in conductivity tends to level off, an automatic prompt message is triggered, indicating that the effective data acquisition cycle of this test has ended.

[0076] Conductivity monitoring employs a four-electrode sensor to eliminate the influence of polarization effects. The monitoring system collects conductivity data every 5 minutes, and random fluctuations are eliminated through moving average filtering. The assessment of cumulative ion concentration is based on the linear relationship between conductivity and ion concentration: σ = k·C, where σ is conductivity, C is ion concentration, and k is a proportionality coefficient.

[0077] The determination of a stable rate of change is achieved using statistical methods. The rate of change of conductivity is calculated over 10 consecutive sampling periods, and a stable period is determined when the standard deviation is less than a set threshold (e.g., 0.1%). Triggering prompts is achieved through both audible and visual alarms and system message boxes.

[0078] The conductivity formula σ=k·C is established based on electrolyte theory, and the proportionality coefficient k is determined through standard solution calibration. Moving average filtering uses the formula σ'i=1 / m∑{j=i-m+1}^iσ_j to suppress random errors. The rate of change is determined by calculating the standard deviation s=√[1 / (n-1)∑(Δσ_i-μ)^2], and a threshold of 0.1% ensures the reliability of the determination. These methods collectively achieve accurate judgment of the corrosion process, providing a scientific basis for terminating the experiment.

[0079] By employing conductivity monitoring technology, this claim enables accurate assessment of the corrosion process. Its key advantages are: the four-electrode measurement method eliminates polarization interference, ensuring the accuracy of the monitoring data; the moving average filtering algorithm effectively suppresses random fluctuations; and the stable period determination criterion based on statistical principles provides an objective standard for test termination. These advantages collectively ensure the validity of the detection data and the economic efficiency of the experimental process.

[0080] The quantitative evaluation index generated in step S3 is a comprehensive corrosion index, which is calculated by weighting the uniform corrosion area growth rate, the pitting density growth rate, and the predicted value of critical environmental conditions.

[0081] The comprehensive corrosion index is calculated using the weighted summation method: CI = w_1·R_u + w_2·R_p + w_3·P_c, where CI is the comprehensive corrosion index, R_u is the uniform corrosion area growth rate, R_p is the pitting corrosion density growth rate, P_c is the critical condition prediction value, and w_i is the weighting coefficient.

[0082] The weights are determined using the entropy weighting method, which allocates weights based on the degree of data variation for each indicator. First, each indicator is normalized; then, the information entropy is calculated; finally, the weight coefficients are determined based on the entropy values. The index value is set to a range of 0-100, with higher values ​​indicating poorer corrosion resistance.

[0083] The comprehensive index formula integrates multi-dimensional information through weighted summation. The entropy weight method, with weights w_i = (1-E_i) / (n-∑E_i), assigns weights based on the degree of index variation, ensuring the objectivity of the evaluation results. Normalization employs the min-max method to eliminate the influence of dimensions. The index range of 0-100 facilitates result comparison and grade classification, with the threshold setting based on statistical analysis of extensive experimental data. This method realizes the transformation of corrosion evaluation from qualitative to quantitative, providing a unified standard for material performance assessment.

[0084] The weighting coefficients determined by the entropy weighting method ensure the objectivity of the evaluation results; the weighted summation model integrates information from different types of corrosion; and the normalization process eliminates the influence of dimensions. This comprehensive evaluation method provides a unified standard for the performance comparison and quality control of cable materials.

[0085] Example 3

[0086] The difference between this embodiment and Embodiment 2 is that:

[0087] A cable corrosion resistance testing system for implementing the method described in Embodiment 1 or Embodiment 2, the system comprising:

[0088] Sealed testing container for holding cable samples and corrosive solutions;

[0089] Image acquisition device, used to acquire initial reference images and monitoring image sequences of cable samples;

[0090] An environmental parameter sensor array is used to monitor the temperature, pH value, and conductivity of the corrosive solution;

[0091] The environmental control module includes a pressure control unit and a temperature control unit, which are used to couple or independently control the internal environment of the detection container;

[0092] The data processing and control center is communicatively connected to the image acquisition device, sensor group, and control module, and is configured to execute the image recognition algorithm, establish the correspondence model, and implement adaptive feedback control.

[0093] This system adopts a modular integrated design, achieving automated testing of cable corrosion resistance through the collaborative work of various functional units. Its working principle is based on the closed-loop control concept, constructing a complete testing system from environmental simulation and data acquisition to intelligent analysis.

[0094] System Component Working Principle and Coordination: The sealed testing container, as the core reaction unit, adopts a multi-layered composite structure design. The inner layer is made of corrosion-resistant alloy material, the middle layer is a thermal insulation layer, and the outer layer is equipped with a reinforced observation window. The container achieves airtight sealing through a hydraulic sealing system, providing a stable environmental space for corrosion testing. A dedicated clamping system is installed inside the container to fix cable samples of different specifications, ensuring standardized testing. The image acquisition device integrates a high-resolution optical imaging system and a multi-angle illumination unit. During operation, it first acquires an initial reference image of the cable sample to establish a digital archive of the surface morphology. During the testing process, it automatically captures a sequence of surface images according to a preset time sequence, ensuring image quality through automatic focusing and exposure control. This device is coupled to the sealed container through an optical window, achieving non-contact monitoring. The environmental parameter sensor group adopts a distributed arrangement strategy, including a temperature sensor, a pH sensor, and a conductivity sensor. The temperature sensor uses a PT100 platinum resistance thermometer to accurately monitor changes in the temperature of the corrosive solution; the pH sensor uses a composite electrode structure to measure the acidity and alkalinity of the solution in real time; the conductivity sensor uses a four-electrode design to eliminate polarization interference. Each sensor connects to the container via a waterproof interface to enable in-situ monitoring.

[0095] The environmental control module consists of a pressure control unit and a temperature control unit. The pressure control unit includes a vacuum pump and a pressurization device, controlling internal pressure changes via precision valves. The temperature control unit employs a jacketed heat exchange design, adjusting heating and cooling power through a PID controller. The two units use a master-slave collaborative control mode, enabling independent or coupled control. The data processing and control center, acting as the system's brain, utilizes an industrial-grade computer platform. The center connects to various components via multiple communication interfaces, receiving monitoring data and sending control commands in real time. Its built-in professional analysis software integrates image processing algorithms, environmental parameter analysis modules, and intelligent control algorithms to achieve data fusion processing and decision output.

[0096] After system startup, an initial self-test is performed to confirm that all components are functioning normally. The operator places the cable sample into the container, injects the etching solution, and seals the container. The control system automatically performs baseline acquisition to obtain initial images and environmental parameters. Upon entering the detection phase, the system activates a multi-tasking parallel operation mode: the image acquisition device captures surface images at a set frequency; environmental sensors continuously monitor parameter changes; and the control module executes environmental adjustments according to a preset program. All monitoring data is transmitted to the control center in real time for synchronous processing and analysis.

[0097] The core processing flow of the control center includes: performing differential calculations on image sequences to identify changes in corrosion characteristics; conducting trend analysis on environmental parameters to assess the state of the corrosive environment; and generating control commands based on preset algorithms to drive the environmental control module. The system has adaptive control capabilities and can dynamically adjust experimental conditions according to the corrosion development status.

[0098] During the testing process, the system displays monitoring data and analysis results in real time and generates a testing log. When the preset testing endpoint is reached, the system automatically stops the test and outputs a comprehensive testing report, including corrosion morphology change curves, environmental parameter records, and performance evaluation results. Through precise mechanical design, reliable sensing technology, and intelligent control algorithms, the entire system achieves automation, precision, and intelligence in cable corrosion resistance testing, providing a reliable technical platform for cable material quality assessment.

[0099] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described in Embodiment 1 or Embodiment 2.

[0100] The above-described embodiments are detailed and specific, illustrating preferred embodiments of the present invention. They are only used to illustrate the technical ideas and features of the present invention, with the aim of enabling those skilled in the art to understand the content of the present invention and implement it accordingly. However, they are not limited to the present invention, and the patent scope of the present invention cannot be limited by this embodiment alone. That is, any equivalent changes or modifications made to the spirit disclosed in the present invention, without departing from the structure of the present invention, such as local improvements within the system and modifications or transformations between subsystems, are still within the patent scope of the present invention.

Claims

1. A method for testing the corrosion resistance of cables, characterized in that, Includes the following steps: S1. Sealed environment construction and baseline acquisition steps: Place the cable sample to be tested into the test container and inject the etching solution, then seal it. Before the etching begins, acquire an initial baseline image of the cable sample surface using an image acquisition device. S2. Multimodal in-situ monitoring and dynamic control steps: During the corrosion process, the following operations are performed simultaneously: S2.1 Visual monitoring: Automatically acquire image sequences of the cable sample surface at set time intervals; S2.2 Environmental Monitoring and Control: Monitor the environmental parameters of the corrosive liquid, and actively control the pressure and temperature cycles of the detection environment based on preset programs or real-time monitoring data; the pressure and temperature cycles in step S2.2 are coupled, and the coupling mode is: simultaneously implement heating operation in the low-pressure stage of pressure cycle, and simultaneously implement cooling operation in the high-pressure stage, so as to simulate the harsh working conditions of alternating humidity and heat and sudden pressure changes. S3. Data Fusion and Mechanism Analysis Steps: The image sequence obtained in step S2.1 is compared with the initial reference image. The spatiotemporal evolution information of corrosion is quantified through image recognition algorithms, including the calculation of the uniform corrosion area growth rate and the pitting density growth rate. This evolution information is then correlated and matched with the time series data of environmental parameters obtained synchronously in step S2.2 to establish a correspondence model between corrosion kinetics and environmental stress, thereby generating a quantitative evaluation index for the corrosion resistance performance of the cable.

2. The method for testing the corrosion resistance of cables according to claim 1, characterized in that, In step S3, the image recognition algorithm specifically performs the following operations: performing pixel-level difference calculations between each frame of image and the initial reference image to identify newly eroded areas.

3. The method for testing the corrosion resistance of cables according to claim 2, characterized in that, In steps S2.1 and S2.2, a unified timestamp is assigned to each set of synchronously acquired image data and environmental parameter data; the association matching in step S3 is based on the unified timestamp to accurately correspond the changes in corrosion morphology with the fluctuations in environmental parameters on the time axis.

4. The method for testing the corrosion resistance of cables according to claim 3, characterized in that, The dynamic control in step S2.2 is an adaptive feedback control based on the correspondence model; specifically: when the pitting density growth rate exceeds the first threshold, the frequency of pressure cycling is automatically increased; when the uniform corrosion area growth rate exceeds the second threshold, the amplitude of temperature cycling is automatically increased.

5. The method for testing the corrosion resistance of cables according to claim 3, characterized in that, The correspondence model established in step S3 is used to predict the critical environmental conditions for pitting corrosion, including the critical chloride ion concentration and the critical temperature.

6. The method for testing the corrosion resistance of cables according to claim 1, characterized in that, In step S2, the accumulation of corrosion ion concentration is indirectly assessed by monitoring the change in conductivity of the corrosive solution; when the rate of change in conductivity tends to level off, an automatic prompt message is triggered, indicating that the effective data acquisition cycle of this test has ended.

7. The method for testing the corrosion resistance of cables according to claim 5, characterized in that, The quantitative evaluation index generated in step S3 is a comprehensive corrosion index, which is calculated by weighting the uniform corrosion area growth rate, the pitting density growth rate, and the predicted value of critical environmental conditions.

8. A cable corrosion resistance testing system, the system being configured to perform the cable corrosion resistance testing method as described in any one of claims 1-7, characterized in that, The system includes: Sealed testing container for holding cable samples and corrosive solutions; Image acquisition device, used to acquire initial reference images and monitoring image sequences of cable samples; An environmental parameter sensor array is used to monitor the temperature, pH value, and conductivity of the corrosive solution; The environmental control module includes a pressure control unit and a temperature control unit, which are used to couple or independently control the internal environment of the detection container; The data processing and control center is communicatively connected to the image acquisition device, sensor group, and control module, and is configured to execute the image recognition algorithm, establish the correspondence model, and implement adaptive feedback control.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1-7.