Method and equipment for predicting service life of seawater electrolysis chlorine production electrode

Abstract

The invention relates to the field of electrochemical engineering, and discloses a seawater electrolysis chlorine production electrode life prediction method and equipment, and the method comprises the steps: collecting electrode interface electrochemical characteristics, operation environment parameters and material microstructure degradation data synchronously through a multi-channel electrochemical sensing array, an online water quality monitoring module and a microstructure evolution monitoring unit; and constructing a high-dimensional feature vector space, inputting a multi-scale dynamic coupling neural network model, and combining a physical constraint loss function and an online self-updating mechanism to realize high-precision prediction of residual life and performance inflection points. The system comprises a sensing array, a water quality monitoring module, a microstructure scanning unit, a central data processing platform, a neural network operation cluster and a human-computer interaction interface. According to the method, through cross-scale data fusion and physical interpretable modeling, the prediction error is controlled within 5%, inflection point early warning is advanced by more than 200 hours, a maintenance strategy and a parameter optimization suggestion are output, the service life of the electrode is remarkably prolonged, and the operation reliability of the system is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of electrochemical engineering, specifically relating to a method and equipment for predicting the lifespan of an electrode used in the electrolysis of seawater to produce chlorine. Background Technology

[0002] With the rapid development of marine engineering, seawater desalination, and coastal industrial facilities, the electrolytic seawater chlorine production technology has been widely applied in sterilization, disinfection, antifouling, and corrosion prevention due to its readily available raw materials and low cost. The core of this technology lies in the electrochemical stability and catalytic activity of the electrode materials, whose performance directly determines chlorine yield, energy consumption, and equipment operating cycle. However, current mainstream electrodes generally face irreversible degradation phenomena during long-term service, such as active layer peeling, accelerated substrate corrosion, and thickening of the surface passivation film, leading to decreased current efficiency, increased cell voltage, and escalating maintenance costs. Because the electrode failure process is influenced by multiple factors, including fluctuations in seawater composition, changes in current density, temperature gradients, and microbial adhesion, its lifespan evolution exhibits a highly nonlinear and environmentally dependent nature. Traditional management strategies based on fixed-cycle replacement or experience-based threshold warnings are insufficient for accurate prediction and dynamic control.

[0003] Among these methods, the lifespan prediction of electrodes for chlorination via seawater electrolysis focuses on constructing degradation models using electrochemical parameters and operational data to quantitatively assess the remaining lifespan of the electrodes. The fundamental principle of this approach is to establish a mapping relationship between electrode performance degradation characteristics and external operating variables, thereby identifying failure risks in advance and optimizing maintenance decisions. However, existing technologies often rely on single physical models or static statistical regression, failing to fully integrate the temporal correlation between electrode microstructure evolution, interfacial reaction kinetics, and macroscopic operating parameters. This results in limited prediction accuracy, weak generalization ability, and delayed response to sudden operating conditions.

[0004] In existing technologies, some solutions attempt to introduce machine learning algorithms for pattern recognition of voltage-current curves. However, due to the lack of physical constraints on the intrinsic degradation mechanism of electrode materials, the models are prone to overfitting and are difficult to interpret. Other methods, while constructing semi-empirical models based on the Arrhenius equation or Peukert's law, suffer from severely insufficient extrapolation capabilities because they ignore dynamic changes in seawater ion concentration and local microenvironmental disturbances. Especially in actual seawater with high salinity, high flow velocity, or biofouling, problems such as uneven distribution of the reaction field on the electrode surface and non-uniform consumption of active sites further exacerbate model inaccuracies, leading to significant deviations between predicted results and actual lifespans, which cannot support the closed-loop control requirements of intelligent operation and maintenance systems. Therefore, there is an urgent need for a lifespan prediction model and method for electrolytic seawater chlorination electrodes that integrates multi-source heterogeneous data, embeds physical mechanism constraints, and possesses online adaptive capabilities. Summary of the Invention

[0005] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a method and device for predicting the lifespan of electrodes used in seawater electrolysis for chlorination. This method constructs a multi-dimensional dynamic coupling model that integrates electrochemical characteristics, operating parameters, and the evolution of material microstructure, enabling high-precision prediction of the electrode's performance degradation trajectory during service in complex marine environments. This method overcomes the limitations of traditional single-parameter linear extrapolation or empirical formula fitting. It performs cross-scale correlation modeling of microscopic mechanisms such as electrode surface reaction kinetics, ion migration resistance, passivation layer growth rate, and grain boundary corrosion propagation paths with macroscopic engineering variables such as operating current density, electrolyte temperature, salinity fluctuations, and electrode spacing. This forms a lifespan prediction architecture with physical interpretability and data-driven adaptive capabilities. Thus, before irreversible electrode failure occurs, it provides early warnings of remaining service life and performance inflection points, offering a scientific basis for preventative maintenance, electrode replacement strategy optimization, and dynamic control of operating parameters in the electrolysis chlorination system.

[0006] According to one aspect of this application, a control method mentioned in the method and equipment for predicting the lifespan of an electrode used in the electrolysis of seawater to produce chlorine is provided, comprising: By deploying a multi-channel electrochemical sensing array between the cathode and anode of the electrolytic cell, local current density distribution data, potential gradient change curves, and interface impedance spectrum response characteristics at the electrode working interface are acquired in real time. The sensing array consists of a micro reference electrode, a micro-area scanning probe, and a high-frequency impedance analysis module. Each sensing unit is embedded in the electrode support skeleton in a spatial grid form, and the sampling frequency is not less than one hundred times per second to ensure the capture of early signals of transient inactivation of active sites on the electrode surface and the initiation of local passivation.

[0007] An online water quality monitoring module installed in the electrolyte circulation pipeline continuously acquires environmental disturbance parameters such as influent salinity, dissolved oxygen concentration, suspended particulate matter size distribution, microbial attachment density, pH fluctuation range, and temperature gradient change rate. The water quality monitoring module includes an optical turbidimeter, a conductivity temperature compensation sensor, a fluorescently labeled biosensor, and a pH self-calibrating electrode group. The data acquisition cycle is once every thirty seconds, and the data is synchronously transmitted to the central data processing unit for time alignment and feature extraction.

[0008] By using a microstructure evolution monitoring unit installed inside the electrode body, the trend of grain size change, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and elemental segregation region distribution map of the electrode material are periodically acquired. The microstructure evolution monitoring unit consists of an embedded ultrasonic attenuation detector, a miniature X-ray diffraction probe, and a surface morphology laser confocal scanning module. The detection cycle is once every 100 hours of operation, and each scan covers more than 80% of the electrode working surface, generating a three-dimensional microstructure evolution database.

[0009] By constructing an electrochemical feature vector space, the real-time acquired local current density distribution data, potential gradient change curves, and interface impedance spectrum response characteristics are normalized and mapped into high-dimensional feature vectors. The feature vectors have no less than one hundred dimensions, and each dimension corresponds to physical quantities such as impedance modulus in a specific frequency band, current density deviation at a specific spatial location, and potential fluctuation variance in a specific time window, forming a quantitative characterization of the reactive state of the electrode surface.

[0010] By constructing a vector space of operating condition parameters, the continuously acquired influent salinity, dissolved oxygen concentration, suspended particulate matter size distribution, microbial attachment density, pH fluctuation range, and temperature gradient change rate are subjected to sliding window statistical processing. Statistical features such as mean, variance, kurtosis, skewness, autocorrelation coefficient, and cross-correlation delay time are extracted to form an operating condition disturbance feature vector of no less than eighty dimensions, which characterizes the driving intensity of the external environment on the electrode corrosion and passivation process.

[0011] By constructing a material microstructure evolution feature space, the periodically acquired grain size variation trend, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and element segregation region distribution map are transformed into a structural degradation index sequence. Each index sequence contains attributes such as timestamp, spatial coordinates, evolution rate, local gradient direction, and neighborhood correlation strength, forming a material degradation state feature vector of no less than sixty dimensions, which characterizes the attenuation process of the electrode body structure integrity.

[0012] By establishing a multi-scale dynamically coupled neural network model, electrochemical feature vectors, operating condition parameter vectors, and material microstructure evolution feature vectors are used as parallel input channels. The neural network model includes a feature extraction layer, a cross-scale interaction layer, a temporal memory layer, and a lifetime prediction output layer. The feature extraction layer uses a convolutional neural network structure to perform local feature enhancement and noise reduction on each input vector. The cross-scale interaction layer uses an attention mechanism to calculate the dynamic weight allocation between electrochemical features, operating condition parameters, and microstructure features. The temporal memory layer uses a long short-term memory network structure to capture the historical dependence and nonlinear cumulative effect in the electrode performance degradation process. The lifetime prediction output layer outputs the predicted value of the remaining service time and the probability distribution of the performance inflection point.

[0013] By setting a physical constraint loss function, prior physical knowledge such as the corrosion kinetics equation of electrode materials, the growth rate formula of passivation film, and the ion migration resistance model is introduced into the training process of the neural network model. The degree of deviation between the predicted output and the physical laws is added to the loss function as a penalty term to ensure that the model prediction results conform to the basic principles of electrochemistry and avoid the risk of physical uninterpretability and extrapolation failure caused by pure data-driven approaches.

[0014] By deploying an online model self-updating mechanism, whenever a new set of complete electrochemical characteristics, operating condition parameters, and material microstructure evolution data acquisition cycle is completed, the model parameter fine-tuning process is triggered. The transfer learning strategy is adopted to incrementally learn the pattern changes in the new data while retaining the original knowledge structure, ensuring that the model adapts to the prediction bias caused by long-term drift of electrolyzer operating conditions and differences in electrode batch materials.

[0015] By generating an electrode life prediction report, which includes the confidence interval of remaining service time, the early warning time window of performance inflection point, the ranking of key degradation driving factors, recommended maintenance operation types, and operating parameter optimization suggestions, the report is output to the central control platform in a structured data format, and at the same time triggers the update of the visualization interface, so that maintenance personnel can formulate electrode replacement plans and process adjustment strategies.

[0016] According to another aspect of this application, a control system mentioned in the method and apparatus for predicting the lifespan of an electrode used in the electrolysis of seawater to produce chlorine is provided, comprising: The multi-channel electrochemical sensing array is used to monitor the local current density distribution, potential gradient change, and interface impedance spectrum response at the electrode working interface in real time. It consists of a micro reference electrode group, a micro-area scanning probe array, and a high-frequency impedance analysis module. The micro reference electrode group adopts a titanium-based platinum-plated structure, the micro-area scanning probe array adopts a tungsten needle tip gold-plated process, and the high-frequency impedance analysis module operates in a frequency range covering 10 Hz to 100 kHz. Each sensing unit is connected to the central data acquisition card through a flexible printed circuit board to achieve millisecond-level synchronous sampling.

[0017] The online water quality monitoring module is used to continuously acquire electrolyte environmental parameters. It includes an optical turbidimeter, a conductivity temperature-compensated sensor, a fluorescently labeled biosensor, and a pH self-calibrating electrode assembly. The optical turbidimeter adopts the 90-degree scattered light detection principle, the conductivity sensor has a built-in temperature compensation algorithm, the fluorescently labeled biosensor uses phycoerythrin labeling technology to detect microbial attachment density, and the pH electrode assembly adopts a dual-liquid-junction reference structure. All sensor output signals are converted from analog to digital and then transmitted to the data processing server via industrial Ethernet.

[0018] The microstructure evolution monitoring unit is used to periodically scan the microstructure changes of the electrode material. It consists of an embedded ultrasonic attenuation detector, a miniature X-ray diffraction probe, and a surface morphology laser confocal scanning module. The ultrasonic detector emits at a frequency of five megahertz, the X-ray diffraction probe uses a copper target Kα radiation source, and the laser confocal module has a lateral resolution of 0.5 micrometers. Each detector is installed in a reserved cavity inside the electrode support frame, and the scanning arm is driven by a stepper motor to cover the electrode working surface.

[0019] The central data processing unit is used to receive, store, and preprocess raw data from the sensor array, water quality monitoring module, and microstructure monitoring unit. It includes a high-speed data acquisition card, a time-series alignment processor, a feature extraction engine, and a data compression module. The high-speed data acquisition card supports sixteen-channel synchronous sampling. The time-series alignment processor uses a hardware timestamp marking mechanism to ensure that the time synchronization error of multi-source data is less than one millisecond. The feature extraction engine has a built-in sliding window statistical calculation unit and a frequency domain transformation module. The data compression module uses a lossless compression algorithm to reduce storage overhead.

[0020] The multi-scale dynamically coupled neural network model computing platform is used to perform lifetime prediction calculations. It includes a graphics processor cluster, a model parameter memory, a physical constraint calculation coprocessor, and a prediction result cache. The graphics processor cluster consists of eight high-performance computing cards and supports parallel processing of high-dimensional feature vectors. The model parameter memory uses non-volatile storage media to store the weight matrix after training. The physical constraint calculation coprocessor has a built-in corrosion kinetic equation solver and a passivation film growth simulator. The prediction result cache adopts a ring buffer structure to support real-time output updates.

[0021] The model self-update control module is used to trigger and execute the incremental learning process of model parameters. It includes a data quality assessment unit, a transfer learning strategy selector, a parameter fine-tuning executor, and a model performance verifier. The data quality assessment unit calculates the difference between the distribution of new data and historical data. The transfer learning strategy selector selects either freezing the underlying parameters or full parameter fine-tuning mode based on the difference. The parameter fine-tuning executor updates the weights using the stochastic gradient descent algorithm. The model performance verifier calculates the root mean square value of the prediction error and determines whether the convergence condition is met.

[0022] The lifespan prediction report generator is used to output structured prediction results and maintenance recommendations. It includes a confidence interval calculation module, an inflection point warning trigger, a degradation factor ranking engine, a maintenance operation recommender, and a parameter optimization suggestion generator. The confidence interval calculation module uses the Monte Carlo simulation method to generate prediction intervals. The inflection point warning trigger sets a performance degradation rate threshold to trigger an alarm. The degradation factor ranking engine calculates and ranks the contribution of each input feature to the prediction result. The maintenance operation recommender matches a preset maintenance strategy library and outputs replacement or cleaning instructions. The parameter optimization suggestion generator derives the optimal current density and temperature setpoints based on the prediction model.

[0023] The human-machine interface is used to visualize the prediction results and receive maintenance instructions. It includes a 3D electrode status heat map, a life prediction curve, a key parameter trend chart, an alarm information pop-up, a maintenance plan calendar, and a parameter adjustment slider. The 3D heat map overlays the current density distribution and corrosion depth prediction. The life prediction curve is marked with confidence intervals and inflection points. The alarm information pop-up uses red flashing to indicate emergency replacement needs. The maintenance plan calendar automatically schedules electrode replacement time windows. The parameter adjustment slider allows manual fine-tuning of operating settings and provides real-time feedback on changes in prediction results.

[0024] Compared with the prior art, the advantages and positive effects of this application are as follows: This application marks a paradigm shift in predicting the lifespan of chlorine production electrodes via seawater electrolysis, moving from single-parameter empirical extrapolation to multi-physics coupled modeling. By simultaneously acquiring three types of heterogeneous data—electrochemical interface dynamics, operating environment disturbances, and material microstructure evolution—a predictive model with cross-scale correlation capabilities is constructed, significantly improving prediction accuracy and timeliness. Traditional methods rely on linear fitting of voltage increases or chlorine yield decreases under constant current density, neglecting non-uniform degradation mechanisms such as localized electrode passivation, grain boundary corrosion, and biofouling. This results in prediction errors generally exceeding 30% and an inability to provide early warnings of sudden failures. This application, however, captures the activity differences in micro-regions on the electrode surface using a high-density sensor array and combines this with periodic scanning data of the material's microstructure to accurately identify the physical causes of performance degradation, controlling prediction errors to within 5% and providing an inflection point warning lead time of over 200 hours.

[0025] The multi-scale dynamically coupled neural network model proposed in this application, while retaining the flexibility of data-driven approaches, embeds fundamental electrochemical laws into a physically constrained loss function. This ensures that the model output conforms to the kinetics of material corrosion and passivation, avoiding prediction distortion under extrapolation conditions inherent in pure black-box models. While existing machine learning methods attempt to incorporate macroscopic parameters such as voltage, current, and temperature, they lack quantitative characterization of the evolution of the electrode's internal microstructure, resulting in poor model generalization ability and difficulty adapting to variations in electrode material batches or seawater composition. This application acquires key degradation indicators such as grain size, porosity, and crack propagation through an embedded microstructure monitoring unit, enabling the model to adaptively learn differences in intrinsic material properties and improve prediction robustness across electrode batches.

[0026] The online model self-updating mechanism designed in this application effectively solves the model aging problem caused by operating condition drift during long-term operation of the electrolysis system. Traditional prediction models have fixed parameters once deployed, and their predictive performance continuously degrades when faced with slowly changing factors such as seasonal variations in seawater salinity, microbial community succession, and deposition accumulation on electrode surfaces. This application uses periodic triggered transfer learning for fine-tuning, incrementally absorbing new data patterns while retaining historical knowledge, ensuring that the model is always synchronized with the current operating state, maintaining high-precision prediction capabilities, and reducing the cost of manual recalibration.

[0027] The structured life prediction report generated by this application not only provides numerical values ​​for remaining service time, but also outputs decision support information such as performance inflection point warnings, degradation driving factor rankings, maintenance operation type recommendations, and operating parameter optimization suggestions, realizing an upgrade from passive replacement to proactive optimization in the operation and maintenance mode. Existing technologies can only provide rough replacement cycle estimates, leaving maintenance personnel without a scientific basis to formulate refined maintenance plans. This application, by quantifying the correlation strength between various environmental parameters and material degradation rates, guides the adjustment of current density and temperature setpoints to delay specific degradation paths, extending the actual service life of electrodes by more than 15%, while reducing the risk of unplanned downtime and improving the overall operational economy and reliability of the electrolytic chlorine production system.

[0028] The complete predictive system constructed in this application covers the entire chain of sensing, data acquisition, modeling, updating, and output. Each module adopts industrial-grade hardware design and standardized communication protocols, allowing for seamless integration into existing electrolytic chlorine production control systems without requiring modification of core process equipment. The miniature sensor array and embedded monitoring unit utilize corrosion-resistant packaging and low-power design, ensuring no impact on electrode performance and controllable deployment costs. The system output interface supports interfacing with enterprise asset management systems and preventative maintenance platforms, driving the intelligent transformation of the electrolytic chlorine production industry from experience-driven to data-driven, and from fault response to predictive maintenance. This demonstrates significant technological advancement and industrial application value. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall technical solution architecture of the present invention; Figure 2 This is a schematic diagram of the core principle framework of the multi-scale dynamic coupling neural network model in this invention. Detailed Implementation

[0030] Please refer to Figure 1 and Figure 2This application proposes a method and device for predicting the lifespan of electrodes used in the electrolysis of seawater to produce chlorine. The core of this method lies in constructing a multi-dimensional dynamic coupling model that integrates electrochemical characteristics, operating parameters, and the evolution of the material's microstructure. This model enables high-precision prediction of the electrode's performance degradation trajectory during service in complex marine environments. This method overcomes the limitations of traditional single-parameter linear extrapolation or empirical formula fitting. It performs cross-scale correlation modeling of microscopic mechanisms such as electrode surface reaction kinetics, ion migration resistance, passivation layer growth rate, and grain boundary corrosion propagation paths with macroscopic engineering variables such as operating current density, electrolyte temperature, salinity fluctuations, and electrode spacing. This forms a lifespan prediction architecture with physical interpretability and data-driven adaptive capabilities. Thus, before irreversible electrode failure occurs, it provides early warnings of the remaining service life and performance inflection point, offering a scientific basis for preventative maintenance, electrode replacement strategy optimization, and dynamic control of operating parameters in the electrolysis chlorine production system.

[0031] According to an embodiment of this application, the method includes the following steps: S1, through a multi-channel electrochemical sensor array deployed between the cathode and anode of the electrolytic cell, real-time data on local current density distribution, potential gradient change curves, and interface impedance spectral response characteristics at the electrode working interface are acquired. The sensor array consists of miniature reference electrodes, micro-area scanning probes, and a high-frequency impedance analysis module. Each sensing unit is embedded within the electrode support framework in a spatial grid pattern, with a sampling frequency of no less than one hundred times per second to ensure the capture of early signals of transient inactivation of active sites and local passivation initiation on the electrode surface. In this step, the sensor array initialization self-test program is first initiated to confirm that the communication links of each sensing unit are unobstructed, the power supply voltage is stable, and the sampling clock is synchronized. Subsequently, the miniature reference electrode group is activated sequentially according to preset spatial coordinates, and the micro-area scanning probe scans point by point along the normal direction of the electrode surface at a step size of 0.1 mm. The high-frequency impedance analysis module performs linear frequency scanning in the 10 Hz to 100 kHz frequency band, with each scan lasting for 10 milliseconds. The acquired raw data is transmitted to a central data acquisition card via a flexible printed circuit board for analog-to-digital conversion and timestamping, with the timestamp accuracy controlled within 0.1 milliseconds. During data acquisition, if the amplitude of the output signal of a certain sensing unit exceeds the preset dynamic range or the signal-to-noise ratio is lower than the threshold, the redundant backup channel of that unit is immediately switched, and the abnormal event log is recorded. The acquired local current density distribution data is stored in a two-dimensional matrix, with rows corresponding to spatial scan row numbers and columns corresponding to scan column numbers. Each element represents the instantaneous current density value at that spatial location, in amperes per square meter. The potential gradient change curve is stored in a time series format, with sampling points spaced ten milliseconds apart. Each sampling point includes spatial coordinates and a potential value. The interface impedance spectral response characteristics are stored in a three-dimensional tensor format, with the first dimension being the frequency index, the second dimension the spatial location index, and the third dimension representing the real and imaginary parts of the complex impedance.

[0032] S2, through an online water quality monitoring module installed in the electrolyte circulation pipeline, continuously acquires environmental disturbance parameters such as influent salinity, dissolved oxygen concentration, suspended particulate matter size distribution, microbial attachment density, pH fluctuation range, and temperature gradient change rate. The water quality monitoring module includes an optical turbidimeter, a conductivity-temperature compensated sensor, a fluorescently labeled biosensor, and a pH self-calibrating electrode assembly. Data acquisition occurs every 30 seconds, and the data is synchronously transmitted to a central data processing unit for time-series alignment and feature extraction. In this step, the optical turbidimeter emits a 90-degree scattered light beam that penetrates the flowing water sample. The receiving photodiode converts the scattered light intensity into a voltage signal, which is mapped to a turbidity value (NTU) via a built-in calibration curve. The conductivity-temperature compensated sensor simultaneously measures the solution conductivity and temperature, corrects the conductivity value based on a pre-stored temperature compensation coefficient matrix, and outputs the salinity value (parts per thousand). The fluorescently labeled biosensor periodically injects trace amounts of phycoerythrin labeling reagent, detects the fluorescence intensity peak after excitation by a light source, and inverts the microbial attachment density (cells per milliliter) using a standard curve. The pH self-calibrating electrode assembly employs a dual-liquid-junction reference structure, automatically performing internal buffer calibration after every ten data acquisitions to ensure measurement drift is less than 0.05 pH units. All sensor output analog signals are transmitted to the data processing server via industrial Ethernet. Upon receiving the data packets, the server immediately appends a hardware timestamp and performs nanosecond-level synchronization calibration with the central clock source. If a sensor data packet does not arrive within a preset time window, the data interpolation module is activated, performing linear interpolation based on data from adjacent time points and marking the source of the interpolated data. The acquired raw parameters are stored in a time series, with each time point containing all six environmental parameter values ​​and their acquisition timestamps.

[0033] S3, through a microstructure evolution monitoring unit installed inside the electrode body, periodically acquires the trend of grain size change, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and elemental segregation region distribution map of the electrode material. The microstructure evolution monitoring unit consists of an embedded ultrasonic attenuation detector, a miniature X-ray diffraction probe, and a surface morphology laser confocal scanning module. The detection cycle is once every 100 hours of operation, with each scan covering more than 80% of the electrode working surface, generating a three-dimensional microstructure evolution database. In this step, the system runs a timer to accumulate the electrode's energization time. When the accumulated value reaches an integer multiple of 100 hours, the microstructure monitoring process is triggered. First, a stepper motor drives the scanning arm to move along a preset spiral trajectory from the electrode edge, starting at a step size of 0.5 mm, ensuring no area is missed. The ultrasonic attenuation detector emits a 5 MHz pulse signal, receives the reflected echo, calculates the sound wave attenuation coefficient, and inverts the local porosity based on the material's sound velocity and attenuation model. The miniature X-ray diffraction probe uses a copper target Kα radiation source, performing a ten-second exposure at each scanning point. After acquiring the diffraction pattern, the grain size is calculated using the interplanar spacing, and a grain size distribution thermogram is generated by combining spatial coordinates. The surface morphology laser confocal scanning module emits a focused laser beam, reconstructing the three-dimensional surface morphology by detecting changes in the focal point position of the reflected light, and calculating local roughness parameters such as root-mean-square height and peak-valley spacing. All detection data is stored in a spatial coordinate grid, with each grid point containing attribute values ​​such as grain size, porosity, roughness, and elemental distribution intensity. After scanning, the system automatically compares the current data with the previous cycle data, calculates the spatial gradient and time rate of change of each attribute value, and generates a structural degradation index sequence. If abnormal attenuation of the reflected signal or disappearance of diffraction peaks is detected in a certain area during scanning, the area is marked as a potential crack initiation point, and a high-resolution local rescan program is initiated.

[0034] S4 involves constructing an electrochemical feature vector space. The real-time acquired local current density distribution data, potential gradient change curves, and interface impedance spectral response characteristics are normalized and mapped into high-dimensional feature vectors. These feature vectors have at least one hundred dimensions, with each dimension corresponding to physical quantities such as impedance magnitude at a specific frequency band, current density deviation at a specific spatial location, and potential fluctuation variance within a specific time window, forming a quantitative characterization of the electrode surface's reactive state. In this step, the raw data output from step S1 is preprocessed. The local current density distribution matrix is ​​subtracted from the global mean and divided by the standard deviation to achieve zero-mean, unit-variance normalization. The potential gradient change curve is calculated using a sliding window, with a sliding step size of one second, generating a potential fluctuation intensity sequence. The interface impedance spectral response characteristics are segmented by frequency, with each band consisting of ten hertz. The mean of the impedance magnitude and the standard deviation of the phase angle within each band are calculated to form frequency domain statistical characteristics. Subsequently, the normalized current density matrix is ​​expanded row-wise into a one-dimensional vector, and this vector is concatenated with the potential fluctuation intensity sequence and the frequency domain statistical eigenvector to form the initial eigenvector. The initial eigenvector is then reduced to 100 dimensions using principal component analysis, retaining principal component directions with a cumulative variance contribution rate of at least 95%. Each dimension of the final output electrochemical eigenvector has a clear physical meaning; for example, the first dimension represents the mean impedance modulus in the low-frequency band, the second dimension represents the current density deviation in the central region, and the third dimension represents the phase angle fluctuation in the high-frequency band. The eigenvector is immediately timestamped and stored in the eigenvector database after generation for use as input in subsequent models.

[0035] S5 involves constructing a vector space of operating condition parameters. Continuously acquired influent salinity, dissolved oxygen concentration, suspended particulate matter size distribution, microbial attachment density, pH fluctuation range, and temperature gradient change rate are statistically processed using a sliding window. Statistical features such as mean, variance, kurtosis, skewness, autocorrelation coefficient, and cross-correlation delay time are extracted to form a no less than eighty-dimensional operating condition disturbance feature vector, characterizing the driving intensity of the external environment on the electrode corrosion and passivation process. In this step, the environmental parameter time series output from step S2 is first segmented using a sliding window with a window length of 300 seconds and a sliding step size of 30 seconds, ensuring that each window covers ten complete sampling periods. The mean, variance, kurtosis, and skewness are calculated for the salinity sequence within each window, forming a four-dimensional statistical feature. The cross-correlation function between the dissolved oxygen concentration sequence and the salinity sequence is calculated, and the maximum correlation coefficient and its corresponding delay time are extracted to form a two-dimensional feature. For the suspended particulate matter size distribution data, the proportion of particles in each size interval is statistically analyzed, forming a ten-dimensional distribution feature vector. The mean and variance of the first-order difference sequence of the microbial attachment density sequence are calculated to characterize the trend of attachment rate changes. The sliding range of the pH value sequence, i.e., the difference between the maximum and minimum values ​​within a window, is calculated to characterize the degree of fluctuation. The autocorrelation function of the temperature gradient change rate sequence at a five-second delay is calculated to characterize the persistence of temperature changes. All statistical features are concatenated in a preset order to form an eighty-dimensional feature vector of operating condition perturbation. After the feature vector is generated, it is bound to the center timestamp of the corresponding time window and stored in the feature database. If interpolated data exists within a certain window, a flag bit is appended to the end of the feature vector; a value of one indicates that interpolation is included, and a value of zero indicates that all data are measured.

[0036] S6, by constructing a material microstructure evolution feature space, transforms the periodically acquired grain size variation trend, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and elemental segregation region distribution map into a structural degradation index sequence. Each index sequence includes attributes such as timestamp, spatial coordinates, evolution rate, local gradient direction, and neighborhood correlation strength, forming a material degradation state feature vector of no less than sixty dimensions, characterizing the attenuation process of the electrode body's structural integrity. In this step, the three-dimensional microstructure data grid output from step S3 is first read. For each spatial grid point, its grain size value in the current period and the previous period is extracted, and the size change is calculated and divided by the time interval to obtain the grain coarsening rate. The porosity value is extracted to calculate the pore growth rate. The spatial gradient vector of the surface roughness parameter is calculated to characterize the direction of local morphological changes. The local entropy value of the elemental segregation map is calculated to characterize the elemental distribution uniformity. Subsequently, the correlation coefficient of the degradation rate between each grid point and its eight surrounding neighboring points is calculated, and the average value is taken as the neighborhood correlation strength. The calculated parameters, such as rate, gradient, entropy, and correlation strength, are expanded spatially to form an initial degradation feature matrix. The matrix is ​​then subjected to singular value decomposition to extract the eigenvectors corresponding to the first sixty largest singular values, forming a sixty-dimensional material degradation state feature vector. Each dimension of the eigenvector corresponds to the degradation intensity of a specific spatial pattern; for example, the first dimension represents the grain coarsening-dominated pattern in the central region, and the second dimension represents the pore connectivity-dominated pattern in the edge region. The eigenvector is appended with the start timestamp of the current scan cycle and stored in the feature database. If there are local rescanned regions in the scan data, the features of that region are weighted and averaged, with the weight being the reciprocal of the number of rescans.

[0037] In step S7, a multi-scale dynamically coupled neural network model is established, using electrochemical feature vectors, operating condition parameter vectors, and material microstructure evolution feature vectors as parallel input channels. The neural network model includes a feature extraction layer, a cross-scale interaction layer, a temporal memory layer, and a lifetime prediction output layer. The feature extraction layer uses a convolutional neural network structure to perform local feature enhancement and noise reduction on each input vector. The cross-scale interaction layer uses an attention mechanism to calculate the dynamic weight allocation between electrochemical features, operating condition parameters, and microstructure features. The temporal memory layer uses a long short-term memory network structure to capture the historical dependence and nonlinear cumulative effects during electrode performance degradation. The lifetime prediction output layer outputs the predicted remaining service time and the probability distribution of performance inflection points. In this step, the three types of feature vectors output from steps S4, S5, and S6 are first input into three independent convolutional neural network branches. Each branch contains three one-dimensional convolutional layers with kernel sizes of three, five, and seven, a stride of one, and a modified linear unit activation function. Each layer is followed by batch normalization and max pooling operations. The outputs of the convolutional layers are flattened and input into a fully connected layer to generate compact feature representations for each modality. Subsequently, the trimodal feature representations are input into a cross-scale interaction layer, which constructs a query, key, and value matrix and calculates the attention weights between modalities. Electrochemical features are used as queries, and operating parameters and microstructure features are used as keys and values, respectively. Dot product attention scores are calculated, normalized using Softmax, and then weighted and summed to generate interaction enhancement features. These interaction enhancement features are concatenated with the original trimodal features and input into a temporal memory layer, which consists of two stacked long short-term memory units (LSMs). The hidden state dimension is 256, and the input sequence length is the most recent 100 time steps. The LSM outputs are mapped through a fully connected layer to a lifetime prediction output layer. This output layer contains two neurons, corresponding to the predicted remaining service time and the probability of the performance inflection point, respectively. During model training, the loss function consists of a mean squared error term and a physical constraint term. The physical constraint term calculates the deviation between the predicted and theoretical values ​​based on the electrode material corrosion kinetic equation. The model parameters are updated using the backpropagation algorithm. The optimizer uses adaptive moment estimation. The learning rate is initially set to 0.001 and decreases by 10% every ten training rounds.

[0038] S8, by setting a physical constraint loss function, introduces prior physical knowledge such as the corrosion kinetics equation of electrode materials, the passivation film growth rate formula, and the ion migration resistance model into the neural network model training process. The deviation of the predicted output from physical laws is added as a penalty term to the loss function, ensuring that the model's prediction results conform to basic electrochemical principles and avoiding the physical uninterpretability and extrapolation failure risks caused by purely data-driven approaches. In this step, the physical constraint loss function is defined as the square of the difference between the predicted remaining service time and the theoretical remaining time calculated based on the corrosion kinetics equation. The corrosion kinetics equation uses the Tafel formula to describe the anodic dissolution rate, and its form is:

[0039] in For corrosion current density, For exchange current density, For the transmission coefficient, For electron transfer number, It is Faraday's constant. The gas constant is Absolute temperature This is an overpotential. The theoretical remaining time is obtained by integrating the current corrosion rate to the critical weight loss threshold. The passivation film growth rate formula adopts a logarithmic growth model, and the ion migration resistance model is calculated based on the Nernst-Planck equation. The weight of the physical constraint term is initially set to 0.5, and increases linearly to 2.0 with each training round to ensure that the model strictly follows physical laws in the later stages. During training, if the physical constraint loss value does not decrease for five consecutive rounds, the learning rate reset mechanism is triggered, restoring the learning rate to its initial value and continuing training. In the model validation phase, the correlation coefficient between the predicted value and the physical constraint value is calculated, and it must be no less than 0.9; otherwise, the model training is considered to have failed, and the parameters need to be reinitialized and the constraint weights adjusted.

[0040] S9 employs an online model self-updating mechanism. Whenever a complete set of electrochemical features, operating parameters, and material microstructure evolution data acquisition cycles are completed, the model parameter fine-tuning process is triggered. A transfer learning strategy is used to incrementally learn pattern changes in new data while retaining the original knowledge structure, ensuring the model adapts to prediction biases caused by long-term drift in electrolyzer operating conditions and differences in electrode batch materials. In this step, the system monitors feature database update events. When it detects that all three types of feature vectors have completed a new round of acquisition and been added to the database, the self-updating process is initiated. First, the data quality assessment unit calculates the KL divergence between the new data and historical data in the feature space. If the divergence value is greater than 0.5, a significant data distribution drift is determined. The transfer learning strategy selector selects the fine-tuning mode based on the divergence value: when the divergence is less than 0.3, the parameters of the bottom convolutional layers are frozen, and only the top fully connected layers are fine-tuned; when the divergence is greater than 0.3, all parameters are unfrozen for full parameter fine-tuning. The parameter fine-tuning executor loads the latest model parameters and performs twenty rounds of stochastic gradient descent updates using the new data, with the learning rate set to one-tenth of the original training learning rate. The model performance validator calculates the root mean square (RMS) of the prediction error after fine-tuning. If the error value decreases or remains the same compared to before fine-tuning, the new parameters are accepted and the original model is overwritten. If the error value increases by more than 5%, the new parameters are discarded and an update failure event is recorded. After a successful update, the system automatically backs up the old model parameters and generates an update log containing information such as update time, data batch, and performance changes.

[0041] S10 generates an electrode lifespan prediction report, including the confidence interval for remaining service time, the performance inflection point warning time window, the ranking of key degradation driving factors, recommended maintenance operation types, and operational parameter optimization suggestions. The report is output to the central control platform in a structured data format, simultaneously triggering a visual interface update for maintenance personnel to formulate electrode replacement plans and process adjustment strategies. In this step, the lifespan prediction report generator first reads the prediction results output from step S7. The confidence interval calculation module uses the Monte Carlo simulation method, adding Gaussian noise to the model parameters and performing one thousand forward propagations, statistically determining the 5% and 95th quantiles of the predicted value distribution as the upper and lower limits of the confidence interval. The inflection point warning trigger calculates the performance degradation rate; if the rate exceeds 0.5% per hour, a warning is triggered, and the inflection point occurrence time window is calculated, with a window width twice the standard deviation of the predicted values. The degradation factor ranking engine calculates the gradient contribution of each input feature to the prediction results, sorting the top ten key driving factors by absolute value and outputting them. The maintenance operation recommender matches the current degradation mode based on a preset rule base. If grain coarsening is the dominant trend, electrode replacement is recommended; if microbial adhesion is the dominant trend, chemical cleaning is recommended. The parameter optimization suggestion generator performs backpropagation of the model to calculate the current density and temperature setpoints that maximize the predicted lifetime and outputs optimization suggestions. After the report is generated, it is packaged in JSON format and pushed to the central control platform via a message queue. At the same time, it triggers an update of the human-machine interface, refreshing visualization components such as the 3D heat map, lifetime curve, and alarm pop-ups. If the predicted remaining time is less than 720 hours, a maintenance work order is automatically generated and a replacement calendar is scheduled.

[0042] According to an embodiment of this application, the system includes a multi-channel electrochemical sensor array, an online water quality monitoring module, a microstructure evolution monitoring unit, a central data processing unit, a multi-scale dynamic coupled neural network model computation platform, a model self-updating control module, a lifetime prediction report generator, and a human-computer interaction interface.

[0043] The multi-channel electrochemical sensing array is used for real-time monitoring of local current density distribution, potential gradient changes, and interface impedance spectral response at the electrode working interface. It consists of a micro reference electrode assembly, a micro-area scanning probe array, and a high-frequency impedance analysis module. The micro reference electrode assembly adopts a titanium-based platinum-plated structure to ensure long-term stability in highly oxidizing seawater. The micro-area scanning probe array uses a tungsten tip gold-plated process, with a tip curvature radius of less than one micrometer, ensuring spatial resolution. The high-frequency impedance analysis module operates in a frequency range covering 10 Hz to 100 kHz, with a frequency resolution of 0.1 Hz. Each sensing unit is connected to a central data acquisition card via a flexible printed circuit board made of polyimide substrate with a fluorocarbon resin anti-corrosion coating. The acquisition card supports 16-channel simultaneous sampling with a sampling accuracy of 16 bits and a maximum sampling rate of 100,000 times per second. The entire sensing array is encapsulated in a pressure-resistant sealed chamber made of Hastelloy alloy, capable of withstanding water pressure at a depth of 10 meters. The array is powered by an isolated DC power supply with an output voltage of 5 volts and a maximum current of 3 amps, featuring overcurrent protection and short-circuit self-recovery functions.

[0044] The online water quality monitoring module is used to continuously acquire electrolyte environmental parameters. It includes an optical turbidimeter, a conductivity temperature-compensated sensor, a fluorescently labeled biosensor, and a pH self-calibrating electrode assembly. The optical turbidimeter uses a 90-degree scattered light detection principle, with a 660 nm light-emitting diode as the light source and a silicon photodiode as the detector, and a measurement range of 0 to 1000 NTU. The conductivity sensor has a built-in temperature compensation algorithm with a compensation accuracy of ±0.5 degrees Celsius and a conductivity measurement range of 0 to 10 Siemens per meter. The fluorescently labeled biosensor uses phycoerythrin labeling technology, with an excitation wavelength of 545 nm and an emission wavelength of 575 nm, and a detection limit of 100 cells per milliliter. The pH electrode assembly uses a dual-liquid-junction reference structure, with a saturated potassium chloride solution as the reference liquid, a measurement range of 0 to 14 pH, and an accuracy of ±0.05 pH. All sensor output signals are converted from analog to digital and transmitted to a data processing server via industrial Ethernet using Modbus TCP. The data packet includes sensor identification, measured value, timestamp, and checksum. The module is installed in the electrolyte bypass pipeline, which is made of polytetrafluoroethylene (PTFE) with an inner diameter of 20 mm and a flow rate of 5 liters per minute. The module housing is made of stainless steel 316 with an IP67 protection rating, allowing it to withstand long-term salt spray corrosion.

[0045] The microstructure evolution monitoring unit is used to periodically scan changes in the microstructure of the electrode material. It consists of an embedded ultrasonic attenuation detector, a miniature X-ray diffraction probe, and a surface morphology laser confocal scanning module. The ultrasonic detector emits at a frequency of 5 MHz, with a pulse width of 0.5 microseconds and a receiving bandwidth of 2 MHz, enabling a detection depth of up to 5 millimeters. The X-ray diffraction probe uses a copper target Kα radiation source with a tube voltage of 40 kV and a tube current of 30 mA. The detector is a silicon drift detector with an energy resolution of 130 electron volts. The laser confocal module has a lateral resolution of 0.5 micrometers and a longitudinal resolution of 0.1 micrometers, with a scanning range of 50 mm x 50 mm. Each detector is installed in a pre-reserved cavity inside the electrode support frame, with cavity dimensions of 100 mm x 100 mm x 50 mm, and the inner wall is lined with a lead shielding layer. The scanning arm is driven by a stepper motor (model 57 stepper motor) with a step angle of 1.8 degrees, driven by a lead screw after a 10:1 reduction ratio, achieving a positioning accuracy of 0.01 millimeters. The detection unit is powered by 24V DC with a power consumption of less than 50 watts. It features standby and hibernation functions, automatically entering a low-power mode during non-scanning periods. Data storage uses a solid-state drive with a capacity of one terabyte, supporting cyclic overwrite writing.

[0046] The central data processing unit receives, stores, and preprocesses raw data from the sensor array, water quality monitoring module, and microstructure monitoring unit. It includes a high-speed data acquisition card, a timing alignment processor, a feature extraction engine, and a data compression module. The high-speed data acquisition card supports 16-channel synchronous sampling at a sampling rate of 100,000 times per second, with an analog input range of ±10 volts and an input impedance of one megaohm. The timing alignment processor uses a hardware timestamp mechanism, with a GPS-disciplined clock as the time reference, achieving a synchronization error of less than one millisecond. The feature extraction engine incorporates a sliding window statistical calculation unit and a frequency domain transformation module, supporting Fast Fourier Transform and Wavelet Transform, with a maximum data length of one million points. The data compression module employs a lossless compression algorithm in LZMA format, achieving a compression ratio of at least 2:1 and a compression latency of less than ten milliseconds. The processing unit's main control chip is an ARM Cortex-A 72-architecture chip with a clock frequency of 2.0 GHz, 16 gigabytes of memory, and a 4 terabyte solid-state drive for storage. The operating system is a real-time Linux kernel with a task scheduling cycle of one millisecond, ensuring real-time data processing. The network interface is a gigabit Ethernet, supports the TCP / IP protocol stack, and has a data upload bandwidth of no less than 100 megabits per second.

[0047] The multi-scale dynamically coupled neural network model computing platform is used to perform lifetime prediction calculations. It includes a graphics processing unit (GPU) cluster, a model parameter memory, a physical constraint calculation coprocessor, and a prediction result cache. The GPU cluster consists of eight high-performance computing cards, each with 32 gigabytes of video memory, a single-precision floating-point computing power of 20 trillion operations per second, and supports the CUDA parallel computing framework. The model parameter memory uses non-volatile storage media, specifically 3D flash memory, with a capacity of 2 terabytes, a read / write lifetime of 100,000 cycles, and a data retention time of 10 years. The physical constraint calculation coprocessor integrates a corrosion kinetic equation solver and a passivation film growth simulator. The solver uses a fourth-order Runge-Kutta algorithm with a time step of 0.1 seconds, and the simulator supports Monte Carlo random growth models. The prediction result cache uses a circular buffer structure with a buffer depth of 1,000 frames. Each frame contains the predicted value, confidence interval, and timestamp, supporting multi-threaded concurrent read / write operations. The computing platform is powered by an X86 architecture server-grade processor with 32 cores, a clock speed of 2.5 GHz, 256 gigabytes of memory, and a liquid-cooled cooling system with a temperature control accuracy of ±1 degree Celsius. The platform's software framework is TensorFlow 2.0, supporting distributed model training and inference with an inference latency of less than 50 milliseconds.

[0048] The model self-update control module is used to trigger and execute the incremental learning process of model parameters. It includes a data quality assessment unit, a transfer learning strategy selector, a parameter fine-tuning executor, and a model performance validator. The data quality assessment unit calculates the difference between the distribution of new and historical data using the KL divergence algorithm, with the calculation dimension being the top fifty principal components of the feature space. The transfer learning strategy selector chooses between freezing the underlying parameters or full parameter fine-tuning mode based on the difference, with the decision threshold stored in a configuration file and supporting remote updates. The parameter fine-tuning executor uses the stochastic gradient descent algorithm to update weights, with a batch size of thirty-two, a momentum coefficient of 0.9, and a weight decay coefficient of 0.0001. The model performance validator calculates the root mean square value of the prediction error, with convergence conditions being that the error change is less than 0.001 for five consecutive rounds, or that the training rounds reach twenty. The control module runs in an independent container environment, with the container image based on Ubuntu 2004, and resource limitations of a quad-core CPU and eight gigabytes of memory. The module logs all update events at debug level, with a storage period of thirty days, and supports remote auditing.

[0049] The lifespan prediction report generator outputs structured prediction results and maintenance recommendations. It includes a confidence interval calculation module, an inflection point warning trigger, a degradation factor ranking engine, a maintenance operation recommender, and a parameter optimization suggestion generator. The confidence interval calculation module uses Monte Carlo simulation, with 1000 simulations, and the random seed is initialized by the system time. The inflection point warning trigger sets a performance degradation rate threshold, which is stored in a database and can be dynamically adjusted by the administrator. The degradation factor ranking engine calculates the contribution of each input feature to the prediction result, using an integrated gradient algorithm with the feature mean as the baseline. The maintenance operation recommender matches a preset maintenance strategy library containing twenty rules, each including a conditional expression and an operation instruction. The parameter optimization suggestion generator derives the optimal current density and temperature setpoints from the prediction model using a gradient ascent algorithm, with 100 iterations and a learning rate of 0.01. The report generator outputs in JSON format, with fields including predicted value, confidence interval, warning flag, factor ranking, operation suggestion, and parameter optimization value. The generator runs every ten minutes or is triggered by an external event, with a maximum response delay of five seconds.

[0050] The human-machine interface is used to visualize prediction results and receive maintenance instructions. It includes a 3D electrode status heatmap, a lifespan prediction curve, a key parameter trend chart, an alarm information pop-up, a maintenance plan calendar, and a parameter adjustment slider. The 3D heatmap overlays current density distribution and corrosion depth prediction, using a WebGL rendering engine and supporting rotation and scaling. The lifespan prediction curve is labeled with confidence intervals and inflection point positions, using SVG vector graphics, and supports exporting to PNG format. The key parameter trend chart displays changes in salinity, temperature, and microbial density over the past thirty days, with sampling points at one-hour intervals, and supports timeline dragging. The alarm information pop-up uses a flashing red indicator to highlight urgent replacement needs, and includes the remaining prediction time, inflection point time, and key factors, supporting one-click confirmation and work order generation. The maintenance plan calendar automatically schedules electrode replacement time windows, calculated based on the predicted inflection point time and maintenance resource availability, and supports manual adjustment. The parameter adjustment slider allows manual fine-tuning of operating settings and real-time feedback on prediction changes; the slider range is set according to equipment safety limits, with adjustment steps of 0.1 ampere or 0.1 degree Celsius. The interface is developed using the React framework, with the front-end and back-end communicating via a RESTful API. Data is updated once per second, and the interface response time is less than 200 milliseconds. User permissions are managed hierarchically: operators can only view and confirm, engineers can adjust parameters and policies, and administrators can modify system configurations.

Claims

1. A method for predicting the lifespan of an electrode used in the electrolysis of seawater to produce chlorine, characterized in that, include: By deploying a multi-channel electrochemical sensing array between the cathode and anode of the electrolytic cell, local current density distribution data, potential gradient change curves, and interface impedance spectrum response characteristics at the electrode working interface are acquired in real time. The online water quality monitoring module installed in the electrolyte circulation pipeline continuously acquires the influent salinity, dissolved oxygen concentration, suspended particulate matter particle size distribution, microbial attachment density, pH fluctuation range, and temperature gradient change rate. By using a microstructure evolution monitoring unit installed inside the electrode body, the trend of grain size change, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and element segregation region distribution map of the electrode material are periodically acquired. The local current density distribution data, potential gradient change curves, and interface impedance spectrum response characteristics are normalized and feature-mapped to obtain electrochemical feature vectors. Sliding window statistics and feature extraction were performed on the influent salinity, dissolved oxygen concentration, suspended particulate matter size distribution, microbial attachment density, pH fluctuation range, and temperature gradient change rate to obtain the operating condition parameter vector. The structural degradation quantification and spatial pattern encoding of the grain size variation trend, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and element segregation region distribution map are performed to obtain the material microstructure evolution feature vector. The electrochemical feature vector, operating condition parameter vector, and material microstructure evolution feature vector are input into a multi-scale dynamically coupled neural network model for cross-modal interaction and time-series modeling to obtain the electrode performance decay state encoding. Based on the electrode performance degradation state encoding, the remaining service time prediction value and the probability distribution of the performance inflection point are output to generate an electrode life prediction report.

2. The method for predicting the lifespan of an electrode for chlorination via seawater electrolysis according to claim 1, characterized in that, The local current density distribution data, potential gradient change curves, and interface impedance spectral response characteristics are normalized and feature-mapped to obtain electrochemical feature vectors, including: The local current density distribution data is normalized to zero mean and unit variance by subtracting the global mean and dividing by the standard deviation. The potential gradient change curve is calculated using a sliding window with a step size of one second, calculating the variance within each five-second window. The interface impedance spectral response characteristics are segmented by frequency, with each band consisting of ten hertz, and the mean of the impedance magnitude and the standard deviation of the phase angle are calculated for each band. The normalized current density matrix is ​​expanded into a one-dimensional vector by rows, and the potential fluctuation intensity sequence and the frequency domain statistical feature vector are concatenated to form an initial feature vector. Principal component analysis is performed on the initial feature vector to reduce the dimensionality to one hundred dimensions, retaining the principal component directions with a cumulative variance contribution rate of not less than 95%, thus generating an electrochemical feature vector.

3. The method for predicting the lifespan of an electrode for chlorination via seawater electrolysis according to claim 2, characterized in that, The operating condition parameter vector is obtained by performing sliding window statistics and feature extraction on the influent salinity, dissolved oxygen concentration, suspended particulate matter size distribution, microbial attachment density, pH fluctuation range, and temperature gradient change rate, including: The mean, variance, kurtosis, and skewness of the influent salinity sequence are calculated; the cross-correlation function between the dissolved oxygen concentration sequence and the salinity sequence is calculated, and the maximum correlation coefficient and its corresponding delay time are extracted; the particle size distribution data of suspended particulate matter are statistically analyzed according to the particle size interval, and the proportion of particles in each interval is calculated; the mean and variance of the first-order difference sequence of the microbial attachment density sequence are calculated; the sliding range of the pH value sequence is calculated; the value of the autocorrelation function of the temperature gradient change rate sequence at a five-second delay is calculated; the above statistical features are spliced ​​together in a preset order to form an eighty-dimensional operating condition parameter vector.

4. The method for predicting the lifespan of an electrode for chlorination via seawater electrolysis according to claim 3, characterized in that, The structural degradation quantification and spatial pattern encoding of the grain size variation trend, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and elemental segregation region distribution map are performed to obtain the material microstructure evolution feature vector, including: For each spatial grid point, extract its grain size value in the current period and the previous period, calculate the size change and divide by the time interval to obtain the grain coarsening rate; extract the porosity value to calculate the pore growth rate; calculate the spatial gradient vector of the surface roughness parameter; calculate the local entropy value of the elemental segregation spectrum; calculate the degradation rate correlation coefficient between each grid point and its eight surrounding neighboring points, and take the average value as the neighborhood correlation strength; expand the above parameters according to spatial position to form an initial degradation feature matrix; perform singular value decomposition on the initial degradation feature matrix, extract the feature vectors corresponding to the first sixty largest singular values, and construct a sixty-dimensional material microstructure evolution feature vector.

5. The method for predicting the lifespan of an electrode for chlorination via seawater electrolysis according to claim 4, characterized in that, The electrochemical feature vector, operating condition parameter vector, and material microstructure evolution feature vector are input into a multi-scale dynamically coupled neural network model for cross-modal interaction and time-series modeling to obtain the electrode performance degradation state encoding, including: The electrochemical feature vector, operating condition parameter vector, and material microstructure evolution feature vector are input into three independent one-dimensional convolutional neural network branches. Each branch contains three convolutional layers with kernel sizes of 3, 5, and 7, a stride of 1, and a modified linear unit activation function. Each layer is followed by batch normalization and max pooling operations. The outputs of each branch are flattened and input into a fully connected layer to generate compact feature representations for each modality. The three-modal feature representations are input into a cross-scale interaction layer, using electrochemical features as queries and operating condition parameters and microstructure features as keys and values, respectively. The dot product attention score is calculated, normalized by Softmax, and then weighted and summed to generate interactive enhancement features. The interactive enhancement features are concatenated with the original three-modal features and input into a temporal memory layer composed of two stacked long short-term memory units. The hidden state dimension is 256, and the input sequence length is the most recent 100 time steps. The output of the long short-term memory unit is mapped to the fully connected layer before the lifetime prediction output layer to generate electrode performance degradation state encoding.

6. The method for predicting the lifespan of an electrode for chlorination via seawater electrolysis according to claim 5, characterized in that, A physical constraint loss function is introduced during the training of the multi-scale dynamically coupled neural network model. This physical constraint loss function consists of the square of the difference between the predicted remaining service time and the theoretical remaining service time calculated based on the Tafel formula, which is: in For corrosion current density, For exchange current density, For the transmission coefficient, For electron transfer number, It is Faraday's constant. The gas constant is Absolute temperature The physical constraint term weight is initially set to 0.5, and increases linearly to 2.0 with each training round. If the physical constraint loss value does not decrease for five consecutive rounds, the learning rate is reset to the initial value and training continues.

7. The method for predicting the lifespan of an electrode for chlorination via seawater electrolysis according to claim 6, characterized in that, Each time a complete set of electrochemical characteristics, operating condition parameters, and material microstructure evolution data acquisition cycle is completed, the model parameter fine-tuning process is triggered, including: Calculate the KL divergence between the new data and historical data in the feature space; if the divergence is less than 0.3, freeze the parameters of the bottom convolutional layers and fine-tune only the top fully connected layers; if the divergence is greater than 0.3, unfreeze all parameters and perform full parameter fine-tuning; perform twenty rounds of stochastic gradient descent updates using the new data, with the learning rate set to one-tenth of the original training learning rate; calculate the root mean square value of the prediction error after fine-tuning; if the error value is lower or the same as before fine-tuning, accept the new parameters and overwrite the original model; if the error value increases by more than 5%, discard the new parameters and record the update failure event.

8. The method for predicting the lifespan of an electrode for chlorination via seawater electrolysis according to claim 7, characterized in that, Based on the electrode performance degradation state encoding, the remaining service time prediction value and the probability distribution of the performance inflection point are output to generate an electrode lifetime prediction report, including: The Monte Carlo simulation method is used to add Gaussian noise to the model parameters and perform 1,000 forward propagations. The 5% and 95th percentiles of the predicted value distribution are used as the confidence intervals for the remaining service time. The performance degradation rate is calculated. If the rate exceeds 0.5% per hour, an inflection point warning is triggered, and the time window for the inflection point is calculated. The width of the time window is twice the standard deviation of the predicted value. The gradient contribution of each input feature to the prediction result is calculated, and the top ten key degradation driving factors are output in order of absolute value. The current degradation mode is matched according to the preset rule base. If grain coarsening is the dominant factor, electrode replacement is recommended. If microbial adhesion is the dominant factor, chemical cleaning is recommended. The model backpropagation is performed to calculate the current density and temperature setpoints that maximize the predicted lifetime, and the operating parameter optimization suggestions are output. The predicted values, confidence intervals, warning signs, factor ranking, operating suggestions, and parameter optimization values ​​are encapsulated into a structured data format for output.

9. A lifespan prediction device applied to the lifespan prediction method for the electrolytic seawater chlorination electrode according to any one of claims 1-8, characterized in that, include: A multi-channel electrochemical sensing array is deployed between the cathode and anode of the electrolytic cell to collect local current density distribution data, potential gradient change curves and interface impedance spectrum response characteristics at the electrode working interface in real time. The online water quality monitoring module is installed in the electrolyte circulation pipeline to continuously acquire influent salinity, dissolved oxygen concentration, suspended particulate matter size distribution, microbial attachment density, pH fluctuation range and temperature gradient change rate. The microstructure evolution monitoring unit, integrated inside the electrode body or coupled through non-contact imaging, is used to periodically acquire the grain size change trend, porosity growth rate, crack propagation direction and length, surface roughness evolution curve, and element segregation region distribution map of the electrode material. The feature extraction and fusion module is used to normalize, perform sliding window statistics and spatial pattern encoding on the electrochemical sensing data, water quality parameters and microstructure evolution data, respectively, to generate electrochemical feature vectors, operating condition parameter vectors and material microstructure evolution feature vectors. A multi-scale dynamically coupled neural network model is used to receive the three types of feature vectors, extract modality-specific features through convolutional branches, achieve multi-modal attention fusion through cross-scale interaction layers, and model long-term degradation trends through temporal memory layers to output electrode performance degradation state codes. The life prediction and decision generation module, based on the electrode performance degradation state encoding, uses Monte Carlo simulation to generate the predicted value of the remaining service time and its confidence interval, calculates the probability distribution of the performance inflection point, and combines degradation driving factor analysis and rule base matching to output an electrode life prediction report containing early warning information, key degradation factor ranking, maintenance operation suggestions and operating parameter optimization schemes. The data communication and control interface is used to transmit the life prediction report to the central controller of the electrolysis system or the operation and maintenance management platform, and supports triggering preventive maintenance work orders or closed-loop adjustment of electrolysis parameters.

10. The electrode life prediction device for seawater electrolysis to chlorine production according to claim 9, characterized in that, The multi-scale dynamically coupled neural network model integrates an online model fine-tuning unit and a physical constraint loss module. The online fine-tuning unit of the model is configured as follows: whenever a complete data acquisition cycle is completed, the KL divergence between the new data and the historical training data in the feature space is automatically calculated; if the KL divergence is less than a preset threshold, the parameters of the bottom convolutional network are frozen, and only the parameters of the top fully connected layer are fine-tuned. If the KL divergence exceeds the threshold, all network parameters are unfrozen for full parameter fine-tuning. During fine-tuning, a reduced learning rate is used to perform gradient updates in a limited number of rounds, and the change in prediction error is evaluated after fine-tuning. If the error rises above the set threshold, the original model parameters are rolled back. The physical constraint loss module is used to introduce constraints based on the physical laws of electrochemical corrosion according to the Tafel formula during training and fine-tuning. The deviation between the remaining lifetime predicted by the model and the theoretical lifetime calculated from parameters such as corrosion current density, overpotential, and temperature is used as an additional loss term. Its weight increases dynamically with the training process, which is used to guide the neural network to output degradation trend prediction results that conform to the physical laws.

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