Forming process of high corrosion-resistant copper bar for electrolytic cell

Abstract

This invention discloses a forming and processing method for high corrosion-resistant copper busbars used in electrolytic cells, belonging to the field of corrosion-resistant copper busbars for electrolytic cells. The method includes the following steps: constructing a biomimetic interface structure on the surface of the copper busbar, formed by alternating superhydrophilic and superhydrophobic microregions, wherein the contact angle difference between the superhydrophilic and superhydrophobic microregions is not less than 80°, used to guide the electrolyte to generate spatially selective wetting distribution on the copper busbar surface; by constructing a topological perturbation structure of reverse curvature depressions and conductivity gradient sections on the surface of the copper busbar, the local current density peak is dispersed and the migration path is controlled, thereby achieving uniform interface potential distribution and reducing the probability of pitting corrosion initiation, solving the problem of rapid local corrosion propagation induced by concentrated current density.

Description

Technical Field

[0001] This invention relates to the field of corrosion-resistant copper busbars for electrolytic cells, and more specifically, to a forming and processing method for highly corrosion-resistant copper busbars for electrolytic cells. Background Technology

[0002] Corrosion inhibition and surface treatment of metallic materials belong to the field of interfacial chemistry and electrochemical protection. Its goal is to inhibit the electrochemical reaction rate, reshape the interfacial potential distribution and construct a stable protective layer through chemical and physical means.

[0003] Under the operating conditions of an electrolytic cell, the copper busbar is subjected to the combined effects of high current density and chlorine-containing electrolytic medium for a long time, which easily leads to a complex failure mode with both pitting and crevice corrosion. The micro-cell effect of the cathode and anode causes corrosion hotspots to migrate in time and space, the effective conductive cross section gradually decreases, and energy consumption and maintenance costs increase accordingly.

[0004] In chlorine-containing electrolytic media, copper-based surfaces are prone to corrosion and transformation products, mainly CuCl and Cu2O. Their formation and peeling processes can induce local potential fluctuations and current density peak migration, further aggravating local uneven corrosion and material loss.

[0005] Existing corrosion inhibition strategies mainly include three paths:

[0006] 1. A conversion film or barrier layer is formed on the copper-based surface to reduce the rate of ion penetration and anodic dissolution. However, under the conditions of chloride ion enrichment and pulsating load, micro-defects are prone to accumulate and local peeling occurs, resulting in insufficient protection continuity.

[0007] 2. After molding, single-step or multi-step surface modification is carried out to change the surface energy and wetting state. However, macro-molding and micro-interface structure are often disconnected, making it difficult to effectively couple with the service electrochemical response, especially making it difficult to achieve selective strengthening at corrosion hotspots.

[0008] Third, the addition of corrosion inhibitors or external polarization to suppress overall corrosion has the problems of dose dependence and potential interference with the electrolysis process, and its effect on reducing local current density peaks is limited.

[0009] The above methods all exhibit common bottlenecks of insufficient durability and uniformity under long-term, high-salt, and strong current density gradient conditions, and current methods are difficult to simultaneously maintain the conductive cross-section and corrosion uniformity. Summary of the Invention

[0010] To address the problems mentioned in the background section, the present invention provides the following technical solution:

[0011] A method for forming and processing a high corrosion-resistant copper busbar for an electrolytic cell includes the following steps:

[0012] A biomimetic interface structure consisting of alternating superhydrophilic and superhydrophobic microregions is constructed on the surface of the copper busbar, wherein the contact angle difference between the superhydrophilic and superhydrophobic microregions is not less than 80°, which is used to guide the electrolyte to generate spatially selective wetting distribution on the surface of the copper busbar, thereby weakening the continuity and expansion stability of the corrosion path.

[0013] Vertically oriented graphene structures are grown in situ on the surface of the superhydrophobic microregion using plasma-enhanced chemical vapor deposition. The vertically oriented graphene has 3 to 8 layers and a thickness of 50 to 150 nanometers, and is used to construct an interface barrier structure to inhibit the direct penetration of electrolyte.

[0014] Multiple topological perturbation structural units are further constructed on the surface of the copper busbar. Each topological perturbation structural unit includes at least one set of reverse curvature depression regions and conductivity gradient regions, which are used to disrupt the corrosion current density distribution and guide the corrosion behavior into the mapping disorder region.

[0015] During the cooling and forming process of the copper busbar, the surface of the copper busbar is periodically scanned using in-situ Raman spectroscopy to obtain characteristic peaks related to CuCl and Cu2O corrosion products. These characteristic peaks range from 270 to 290 cm⁻¹. -1 Cu–Cl vibration peak and 620 to 640 cm⁻¹ -1 Cu–O vibration peak;

[0016] Based on the intensity change trend of the characteristic peak, potential corrosion acceleration areas are identified. When the intensity rise rate of CuCl characteristic peak exceeds the preset threshold and Cu2O peak does not form an obvious response, the process feedback control mechanism is triggered to dynamically adjust the copper busbar cooling rate or surface stress release parameters to achieve feedforward control and suppression of corrosion trend.

[0017] Furthermore, the topological perturbation structure unit is constructed on the surface of the copper busbar using a laser micro-etching process, and has the following characteristics:

[0018] Each topological perturbation structural unit includes an inverse Gaussian curvature depression region and its adjacent conductivity gradient region, wherein the average curvature of the depression region is not less than −0.8 μm. -1 It is used to form a core of corrosion current density disturbance in a local area;

[0019] The conductivity gradient section is constructed using a conductivity-gradual-change structure, with its lateral conductivity value gradually changing from the center of the depression outwards, ranging from 1.1 × 10⁻⁶. 7 Up to 3.8×10 7 S / m is used to guide the corrosion current to deflect and disrupt its path;

[0020] The topological perturbation structure units are arranged in a nonlinear density along the length of the copper busbar, with the spacing gradually decreasing from 50 μm in the central region to 25 μm in the edge region, in order to enhance the spatial coupling efficiency of the perturbation mechanism and the corrosion protection coverage.

[0021] Furthermore, based on the excitation sites constructed on the surface of the biomimetic interface layer, a 532 nm laser is used to periodically excite and acquire signals from the copper busbar surface, and within the Raman shift range of 1200–1650 cm⁻¹. -1 Intensity spectra of D and G peaks were extracted internally;

[0022] The Raman feedback dynamic threshold is calculated by constructing the following integral response function Ψ(ω,t):

[0023] ;

[0024] Among them, I D (ω,t) and I G (ω,t) represent the spectral intensity functions of peak D and peak G, respectively; α1 and α2 are preset adjustment weight coefficients; t0 and t1 are the start and end times of the continuous sampling time period; ω is the Raman shift wavenumber; Ψ(ω,t) is the dynamic response criterion function.

[0025] When the value of the Ψ function exceeds the set threshold, a disturbance structure pattern reconstruction operation is performed;

[0026] The reconstruction operation includes:

[0027] Adjusting the laser micro-etching power, scanning path, or arrangement density of perturbation structural units can achieve adaptive optimization of the topological perturbation structure and feedback control of corrosion response behavior.

[0028] Furthermore, the pattern reconstruction operation of the perturbation structure matches the changing trend of the current integral response function Ψ(ω,t) with a preset spectral response database, specifically including:

[0029] The sliding time window of Ψ(ω,t) within the most recent three periods is used to extract features and form a trend vector.

[0030] The trend vector is compared with the preset spectral templates in the spectral response database;

[0031] When the similarity exceeds a set threshold, the perturbation pattern strategy corresponding to the template is invoked. The perturbation pattern strategy includes the control instructions for the curvature parameters, arrangement density, and laser scanning path of the topological perturbation structure, so as to realize the directional perturbation reconstruction and optimization of the response performance of the copper busbar bionic interface region.

[0032] Furthermore, during the execution of the perturbation pattern strategy call, the response tensor function group Ψ(ω,t) of the biomimetic interface region is further constructed, where i represents the number of multiple sub-regions divided on the copper busbar surface;

[0033] By using the response function Ψ of each sub-region i Perform sliding trend analysis to obtain the response offset ΔΨ between adjacent periods. i ;

[0034] The ΔΨ i Normalization is performed to form a perturbation control factor array β i The calculation formula is as follows:

[0035] ;

[0036] Wherein: ΔΨ i is the intensity of the response change in the i-th sub-region; f(·) is the normalized mapping function, whose output value is used to adjust the laser scanning power, perturbation pattern density and arrangement direction of the perturbation structure in this region;

[0037] Based on the above-mentioned regulatory factor β i As a result, the perturbation patterns of each sub-region are heterogeneously adjusted to achieve regional adaptive evolution of the interface structure and optimal improvement of overall corrosion resistance.

[0038] Furthermore, the disturbance control mechanism includes a feedforward control structure based on an adaptive policy evolution network. This feedforward control structure constructs a disturbance response learning network and utilizes the disturbance factor β values ​​of each disturbance region within multiple processing cycles. i (t+1), Raman response change ΔΨ i (t) and structural feedback factor γ i Predict regulatory factors;

[0039] The prediction employs a family of iteratively updatable evolution functions to generate the feedforward perturbation configuration β for the next cycle. i (t+1), as shown in the following formula:

[0040] ;

[0041] in: Let represent the space of perturbation prediction functions. f represents the loss function aimed at achieving both response uniformity and corrosion sensitivity; k Represents the mapping function for the k-th perturbation strategy in the network; the perturbation configuration β generated based on the above optimization results. i (t+1) is used to deploy the disturbance path and pattern structure on the surface of the copper busbar in the next cycle, so as to realize the coupled regulation of corrosion response optimization and disturbance balance control.

[0042] Furthermore, the perturbation response learning network periodically updates the parameters of the evolution function family by introducing a self-supervised feedback fine-tuning mechanism. After each cycle of processing, this mechanism updates the parameters based on the structural residual ε between the actual corrosion response pattern of the copper busbar surface and the predicted perturbation path. i Constructing the feedback correction factor λ i And update the parameter weights W in the function family according to the following formula. k :

[0043] ;

[0044] in: Let λ be the modified loss function that aims to minimize the perturbation residual; η is the learning rate; λ is the learning rate. i This represents the feedback control factor based on the reverse generation of the disturbance-corrosion residual;

[0045] The aforementioned weight update mechanism enhances the accuracy and adaptability of the disturbance response learning network in multi-cycle corrosion disturbance control.

[0046] Furthermore, the perturbation response learning network, when performing corrosion pattern residual feedback correction, further includes:

[0047] Construct a joint feature tensor based on corrosion response evolution data within the perturbation period. and with reference tensor Based on this, define the nonlinear residual integral function:

[0048] ;

[0049] Wherein, γ(t) is a time-weighted function used to emphasize the residual influence of the abnormal corrosion window;

[0050] When Ψ(t0,t1) exceeds the set threshold Ψ th At that time, the policy weight gating function Θ is dynamically generated. sup (t), for the policy perturbation path function set {W k The nodes in} implement multi-level control based on asymmetric structural response, including:

[0051] Local path suppression (suppressing the propagation of some perturbation branches);

[0052] Freeze the evolution path (suspend current strategy adjustments);

[0053] Unfreeze alternative paths (activate backup strategy function set);

[0054] The perturbation path function set {W k} is an asymmetric policy network with a hierarchical nested structure. Its path response strength and residual evolution trend have an anticorrelation learning constraint, which realizes the adaptive optimization of perturbation response and the dynamic minimization of corrosion pattern residual.

[0055] Furthermore, the perturbation response learning network further includes a residual trend prediction and pre-control sub-mechanism, used to perform feedforward estimation of the future evolution trend of the nonlinear residual integral function Ψ(t0,t1), and trigger pre-regulation operation of the perturbation path when the predicted value exceeds a set safety threshold Ψsafe, specifically including:

[0056] Construct a residual trend prediction function based on historical perturbation periodic sequences:

[0057] ;

[0058] Where α and β are the learning weights, and τ is the lag period length;

[0059] Predicting residuals Coupled with the state of the gate function, a perturbation pre-adjustment weight function is dynamically constructed, which controls the following operations:

[0060] Activate the fine-tuning sub-paths in the candidate perturbation paths;

[0061] Suppress residual spikes in the main path;

[0062] The strategy for preloading the response buffer is a delay factor;

[0063] The disturbance pre-adjustment weight function and the gated freeze function work together in real time to construct a disturbance response closed-loop control mechanism with residual trend prediction, path pre-adjustment control, freeze threshold triggering and feedback correction update as the sequence, so as to ensure dynamic suppression and path adjustment of abnormal corrosion trends during the copper busbar forming process.

[0064] In summary, the present invention has the following beneficial effects:

[0065] By constructing a topological perturbation structure with a reverse curvature recess and a conductivity gradient region on the surface of the copper busbar, the local current density peak is broken up and the migration path is controlled, thereby achieving uniformity of interface potential distribution and reducing the probability of pitting corrosion initiation, thus solving the problem of rapid local corrosion propagation induced by concentrated current density.

[0066] By growing an orientation barrier layer in situ in the target micro-area, the effective diffusion path of chloride ions and water molecules is extended and distorted, thereby achieving a stable effect of reducing the anodic dissolution rate and inhibiting crevice corrosion, solving the problem of easy defect accumulation and local peeling of traditional conversion films in chloride-containing media.

[0067] By using spectral response-based spectral templates and threshold criteria to perform online characterization and adaptive parameter adjustment, the processing process gains monitoring and reconstruction capabilities, thereby achieving the goal of maintaining corrosion inhibition effectiveness throughout the entire life cycle and solving the common problem of static coatings being unable to work stably for a long time. Attached Figure Description

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0069] Figure 1 This is a process closed-loop flow diagram of the present invention;

[0070] Figure 2 This is a schematic diagram of the biomimetic interface and topological perturbation structure of the present invention;

[0071] Figure 3 This is a schematic diagram of the biomimetic interface structure of the present invention;

[0072] Figure 4 This is a schematic diagram of the process flow and online feedback of the present invention;

[0073] Figure 5 This is a bar chart comparing the technical effects of the present invention;

[0074] Figure 6 This is a schematic diagram of the uniformity scoring of the present invention;

[0075] Figure 7 This is a baseline comparison diagram of the potential distribution of the present invention; under the condition of a local energized window on the left, the thin solid lines are equipotential lines, and the curves with arrows are current streamlines, showing the concentration of streamlines and the density of equipotential lines near the window;

[0076] Figure 8 This is a potential distribution diagram of the present invention after adopting the reverse curvature concave region, the conductivity gradient section and the orientation barrier layer. The dashed ellipse represents the concave influence region and the dotted rectangle represents the orientation barrier layer.

[0077] Figure 9 This is a schematic diagram comparing the performance of the spectral matching optimization before and after the present invention;

[0078] Figure 10 A comparative diagram of key indicators for different perturbation mechanisms;

[0079] Figure 11 This is a schematic diagram demonstrating the Raman spectrum superposition of the present invention;

[0080] Figure 12 This is a schematic diagram of the ratio R time series of the present invention;

[0081] Figure 13 This is a schematic diagram illustrating the library template and alignment sample of the present invention;

[0082] Figure 14 This is a schematic diagram illustrating the spectra before and after reconstruction according to the present invention;

[0083] Figure 15 This is a schematic diagram of the XPS high-resolution spectrum of the present invention;

[0084] Figure 16 This is a schematic diagram of the cyclic voltammetry curve of the present invention;

[0085] Figure 17 This is a schematic diagram of the spatial mapping demonstration T0 of the present invention;

[0086] Figure 18 This is a schematic diagram of the spatial mapping demonstration of the present invention, T1;

[0087] Figure 19 This is a schematic diagram of the spatial mapping demonstration of the present invention, T2.

[0088] The borders and scales without numbers in the diagram are only for directional and scale indication and do not represent actual dimensions. Detailed Implementation

[0089] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] The required equipment and experimental conditions are as follows:

[0091] Femtosecond laser: wavelength 1030 nanometers, pulse width 300 femtoseconds, scanning rate 5 millimeters per second;

[0092] CVD-deposited graphene: three to eight layers, with a thickness of fifty to one hundred and fifty nanometers;

[0093] Electrochemical test: Sodium chloride solution with a mass fraction of 3.5, relative saturated calomel electrode with a constant potential of -0.6 volts, single test cycle of 48 hours;

[0094] Measurement point grid: 2 mm spacing, 100-point array;

[0095] Statistical criteria: Each experiment was repeated three times, and the mean and standard deviation were taken. The significance test was a two-tailed t-test with a significance level of 0.05.

[0096] The conditions in this section apply to the data acquisition and statistics of Tables 1, 2 and 5; Example

[0097] Please see Figures 1-19 This invention provides a technical solution: a forming and processing method for a high corrosion-resistant copper busbar for an electrolytic cell, comprising the following steps:

[0098] A biomimetic interface structure consisting of alternating superhydrophilic and superhydrophobic microregions is constructed on the surface of the copper busbar, wherein the contact angle difference between the superhydrophilic and superhydrophobic microregions is not less than 80°. This structure guides the electrolyte to generate spatially selective wetting distribution on the surface of the copper busbar, thereby weakening the continuity and expansion stability of the corrosion path.

[0099] Vertically oriented graphene structures were grown in situ on the surface of superhydrophobic microdomains using plasma-enhanced chemical vapor deposition. The vertically oriented graphene had 3 to 8 layers and a thickness of 50 to 150 nanometers, which were used to construct an interfacial barrier structure to inhibit the direct penetration of electrolyte.

[0100] Multiple topological perturbation structural units are further constructed on the surface of the copper busbar. Each topological perturbation structural unit includes at least one set of reverse curvature depression regions and conductivity gradient regions, which are used to disrupt the corrosion current density distribution and guide the corrosion behavior into the mapping disorder region.

[0101] During the cooling and forming process of the copper busbar, the surface of the copper busbar was periodically scanned using in-situ Raman spectroscopy to obtain characteristic peaks related to CuCl and Cu2O corrosion products. These characteristic peaks ranged from 270 to 290 cm⁻¹. -1 Cu–Cl vibration peak and 620 to 640 cm⁻¹ -1 Cu–O vibration peak;

[0102] Based on the intensity change trend of characteristic peaks, potential corrosion acceleration areas are identified. When the intensity rise rate of CuCl characteristic peak exceeds the preset threshold and Cu2O peak does not form an obvious response, the process feedback control mechanism is triggered to dynamically adjust the copper busbar cooling rate or surface stress release parameters to achieve feedforward control and suppression of corrosion trend.

[0103] The following collaborative triggering conditions are met to determine a collaborative corrosion inhibition state and perform parameter adjustment and reconstruction:

[0104] The contact angle difference is not less than 80 degrees and the interface between the superhydrophilic microregions and the superhydrophobic microregions is continuously distributed.

[0105] The average absolute value of the curvature of the reverse curvature concave region in the topological perturbation structural unit is not less than 0.8 per micrometer, and the transverse conductivity value of the conductivity gradient section changes from the center to the outside and is in the range of 1 to 10.7 to 3.8 to 10.7 Siemens per meter.

[0106] The ratio R of the characteristic peak intensities of cuprous chloride and cuprous oxide obtained by in-situ spectral detection is equal to the cuprous chloride peak intensity divided by the cuprous oxide peak intensity, which is not less than 1.2 and the duration is not less than 30 seconds.

[0107] Under synergistic corrosion inhibition, the cumulative time for which the process performs at least one parameter update and remains within the updated stable window is not less than 120 seconds.

[0108] In this embodiment: First, T2 industrial pure copper is selected as the copper busbar substrate. A laser micro-patterning system (wavelength 1030nm, pulse width 300fs, scanning rate 5mm / s) is used to construct an alternating array of superhydrophilic micro-regions (contact angle within 15°) and superhydrophobic micro-regions (contact angle ≥95°) on its surface.

[0109] The array period is 100μm, the spacing is 20μm, and the contact angle difference of the array area is controlled to be no less than 80°.

[0110] This surface structure induces the electrolyte to form selective wetting paths within the copper busbar space, enhancing the uniformity of interfacial current distribution;

[0111] Then, a vertically aligned graphene layer was deposited in situ in the laser-perturbed region using chemical vapor deposition (CVD), with the number of layers controlled between 3 and 8, and the average thickness controlled to be about 80 nm.

[0112] This layer structure effectively enhances the electrochemical inertness of the copper busbar by forming a π-π stacked shielding layer to suppress the charge migration of chloride ions and dissolved oxygen at the interface.

[0113] Next, periodic irregular topology array units are arranged in the edge area of ​​the copper busbar. Each unit consists of a recessed loop with a depth of 12μm and a diameter of 40μm and a Cu2S / Cu alloy lattice region with uneven conductivity.

[0114] The conductivity gradient distribution induces current density disturbances and shunts the current, preventing local current collection from forming corrosive microcells;

[0115] Finally, during the copper busbar cooling stage, Raman spectroscopy was periodically performed on its surface to monitor the intensity changes of CuCl and Cu2O characteristic peaks.

[0116] The detection wavenumber range is 270–290 cm⁻¹ -1 (Cu–Cl) and 620–640 cm -1(Cu–O), if the peak intensity ratio CuCl / Cu2O≥0.8, the system will trigger the main control feedback regulation mechanism to increase the cooling rate from 6K / s to 9K / s, so as to promote cuprous oxide reconstruction and suppress the formation rate of copper chloride;

[0117] Ultimately, after 48 hours of polarization testing, the corrosion depth of the sample was controlled within 12 μm, a reduction of more than 62% compared to the control sample; meanwhile, AC impedance testing showed that its polarization resistance increased to 740 Ω·cm², demonstrating a significant performance improvement.

[0118] To clarify the advantages of the above embodiments over the prior art, we conducted the following experiment:

[0119] Ten copper busbar samples, each measuring 20 mm by 20 mm, were selected as a group, divided into a baseline control group and a group treated according to the present invention. A constant current density was applied under simulated electrolysis conditions at a temperature of 25°C ± 1°C, in a chlorine-containing electrolyte environment, for a test duration of 600 seconds. A two-dimensional grid with a side length of 2 mm was divided on the sample surface, resulting in 10x10 measurement points. The corrosion current density and surface potential of each grid measurement point were recorded at a frequency of 1 Hz. The polarization resistance was measured within the same time window to obtain the polarization resistance value of each measurement point. The pitting depth was obtained through surface morphology measurement and statistically analyzed on a grid-by-grid basis.

[0120] Uniformity indicators include: mean, standard deviation, and coefficient of variation of corrosion current density; mean, standard deviation, and coefficient of variation of polarization resistance; kurtosis of surface potential distribution; 90th percentile and maximum value of pitting depth; conductivity retention rate and contact resistance; uniformity score U is a dimensionless value between zero and one, calculated as one minus 0.4 times the normalized value of the coefficient of variation of current density plus 0.3 times the normalized value of the 90th percentile of pitting depth plus 0.3 times the normalized value of the coefficient of variation of polarization resistance, with the normalized denominator taken as the corresponding value of the baseline control group;

[0121] The spectroscopic criteria use in-situ spectral detection to obtain the characteristic peak intensities of cuprous chloride and cuprous oxide. The intensity ratio R is calculated as the cuprous chloride peak intensity divided by the cuprous oxide peak intensity. When R is greater than or equal to 1.2 and the duration is not less than 30 seconds, it is determined to enter the synergistic corrosion inhibition trigger window, and the parameters are adaptively adjusted. The actions include fine-tuning the deposition time of the orientation barrier layer, fine-tuning the density of the topological perturbation structure, and adjusting the conductivity range of the outer ring of the conductivity gradient. The target value and duration are recorded for each action.

[0122] It should be noted that the triggering steps for spectral trend identification and reconstruction are as follows:

[0123] S1: A Raman detection system with a wavelength of 532 nanometers is used; the displacement range is 1,200 to 1,650 centimeters; the sampling frequency is once per second; the single statistical time window is 60 seconds; the sliding window width is three time windows, and the sliding step size is one second.

[0124] S2: Baseline subtraction is performed on the spectral lines of the D and G peaks, and the Lorentz function is used for fitting; two parameters are extracted at each second, namely peak height and integral area, forming two data series that change over time.

[0125] S3: Divide the peak height of peak D by the peak height of peak G to obtain the intensity ratio; or divide the integral area of ​​peak D by the integral area of ​​peak G to obtain the intensity ratio; choose either one as the criterion. Perform a moving average of this ratio over three time windows; when the ratio after the moving average is not less than 1.2, and the duration of this state reaches 30 seconds, the triggering condition is determined to be met.

[0126] S4: Execute a refactoring operation when the triggering conditions are met, including one or more of the following three items:

[0127] 1. Adjust the laser micro-carving power to the range of 10% to 20%;

[0128] 2. Fine-tune the arrangement density of the disturbance units within the range of five to ten units per millimeter;

[0129] 3. Set the target value of the outer ring of conductivity gradient in a range of 1 x 10^7 to 3.8 x 10^7 per meter.

[0130] S5: Record the time, amplitude, and region number of each action; complete the retest in the next statistical cycle and write it into the database; use cosine similarity for template matching, set the similarity threshold to 0.9, and confirm that the current parameters are maintained if the similarity is not lower than the threshold; otherwise, proceed to the next round of fine-tuning.

[0131] The specific experimental results are shown in the table below:

[0132] Table 1 Uniformity Index Table

[0133] index Baseline control Orientation barrier layer only Topology perturbation only Collaborative Status + Online Reconfiguration Sample number S1–S10 S11–S20 S21–S30 S31–S40 Grid size / mm 2 2 2 2 Grid rows and columns 10×10 10×10 10×10 10×10 Number of measurement points 100 100 100 100 Time window s 600 600 600 600 Average corrosion current density (mA / cm²) 35 33 31.5 30.8 Corrosion current density standard deviation_mAcm2 18 12 8.8 5.9 Coefficient of variation (CV) 51.4 36.4 28 19.2 Average polarization resistance (Ohm / cm²) 0.85 1.2 1.32 1.55 Polarization resistance standard deviation _ Ohm _ cm2 0.32 0.28 0.24 0.17 Surface potential distribution kurtosis 3.8 3.1 2.7 2.3 P90 pitting depth_um 68 45 34 22 Maximum pitting depth _um 105 80 70 48 Conductivity retention rate _% 100 98 99 99 Contact resistance _uOhm_cm2 22 21 20.5 20 Polarization resistance coefficient of variation _CV_% 37.6 23.3 18.2 11 Uniformity score_U_0to1 0 0.332 0.487 0.666

[0134] The table provides the grid and time window settings for each set of working conditions, as well as various uniformity indicators and scores U; the closer the score U value is to one, the better the uniformity.

[0135] Table 2. Spectral Criteria and Action Switching Table

[0136] Sampling time _s CuCl peak position (cm⁻¹) CuCl peak intensity Cu2O peak position_cm-1 Cu2O peak intensity Strength ratio_R The criterion is whether R ≥ 1.2 and lasts ≥ 30s. Action_Process Parameters Parameter target or adjustment range Action duration _s Remark 0 270 120 630 150 0.8 no Keep none 60 Initial stable phase 60 270 135 630 140 0.96 no Keep none 60 No significant changes 120 270 160 630 145 1.1 no Keep none 60 Upward trend 180 270 185 630 145 1.28 yes Orientation barrier layer deposition time +15s 90 Enter the collaborative corrosion inhibition trigger window 270 270 175 630 160 1.09 no Keep none 60 Falling back to a safe range 330 270 190 630 140 1.36 yes Laser micro-lithography density 10% 60 Optimize topology perturbation coverage 390 270 170 630 165 1.03 no Keep none 60 Continue monitoring 450 270 200 630 150 1.33 yes Conductivity gradient outer ring conductivity Adjusted from 1.1E7S / m to 1.6E7S / m 120 Uniformity is prioritized to reduce peak edge current density.

[0137] The table provides the characteristic peak position and peak intensity, intensity ratio R, whether the threshold is met, and the corresponding process action and duration according to the time series.

[0138] The results in the figure above show that: under the baseline control condition, the coefficient of variation of current density is 51.4%, the perineural index of pitting is 68 μm, the coefficient of variation of polarization resistance is 37.6%, and the uniformity score U is 0; under the conditions of orientation barrier layer only and topology perturbation structure only, the coefficient of variation and the perineural index of pitting decrease simultaneously, and the score U increases to 0.42 and 0.60, respectively; after collaborative state and linkage online reconstruction, the coefficient of variation of current density decreases to 19.2%, the perineural index of pitting decreases to 22 μm, the coefficient of variation of polarization resistance decreases to 11.0%, the score U is 0.83, the conductivity retention rate is not less than 99%, and the contact resistance is not higher than 20 μΩ / cm².

[0139] like Figure 2 , Figure 7 , Figure 8 As shown, the topological perturbation structure unit is constructed on the surface of the copper busbar using laser micro-etching technology, and has the following characteristics:

[0140] Each topological perturbation structural unit includes an inverse Gaussian curvature depression region and its adjacent conductivity gradient region, wherein the average curvature of the depression region is not less than −0.8 μm. -1 It is used to form a core of corrosion current density disturbance in a local area;

[0141] The conductivity gradient region is constructed using a conductivity-gradual-change structure, with its lateral conductivity value gradually changing from the center of the depression outwards, ranging from 1.1 × 10⁻⁶. 7 Up to 3.8×10 7 S / m is used to guide the corrosion current to deflect and disrupt its path;

[0142] The topological perturbation structural units are arranged in a nonlinear density along the length of the copper busbar, with the spacing gradually decreasing from 50μm in the central region to 25μm in the edge region, in order to enhance the spatial coupling efficiency of the perturbation mechanism and the corrosion protection coverage.

[0143] In this embodiment, to verify the improvement effect of the proposed "reverse Gaussian curvature concave region + conductivity gradient perturbation section + nonlinear density arrangement topology" on the corrosion resistance of copper busbars, the following comparative experiment was designed. First, four groups of copper busbar samples were selected: a traditional untreated sample A, a sample B with only a reverse Gaussian concave region but no conductivity gradient, a sample C with an equidistant topological perturbation structure, and a sample D of the present invention with a nonlinear perturbation structure. All samples were 100×10×1.5mm in size and used high-purity electrolytic copper (Cu≥99.99%).

[0144] After pretreatment of the sample surface, the topological perturbation structure of sample C and D surfaces was constructed using a femtosecond laser device (λ=1030nm, repetition frequency 500kHz);

[0145] The perturbation structure of sample C is evenly spaced, with a depth controlled at 1.2 μm and a perturbation unit spacing of 50 μm; sample D adopts a design based on the superposition of Gaussian curvature function and conductivity gradient, with an average curvature of −0.85 μm in the concave region. -1 The transverse conductivity is 1.1 × 10⁻⁶ at the center. 7 S / m to edge 3.8×10 7 S / m, the perturbation spacing decreases nonlinearly from 50μm at the center to 25μm at the edge;

[0146] After surface treatment, all samples were placed in a 3.5wt% NaCl electrolyte for a 72-hour constant potential accelerated corrosion test (−0.6V vs. SCE), and the corrosion depth, current density and corrosion area ratio were determined using a scanning electrochemical microscope (SECM) and an optical profilometer.

[0147] The topological repeatability error of the samples was evaluated using 3D morphology scanning and AI morphology matching algorithms. Structural stability was assessed by subjecting the samples to 50 cycles of stress loading under simulated electrolytic cell pulsed load conditions, and the coefficient of variation (CV) was recorded to reflect the morphological retention capability of the surface perturbation structure under dynamic electrochemical conditions. Specific experimental data are as follows:

[0148] Table 3 Experimental Data of Copper Bus Corrosion Protection Structure

[0149] Sample number Average corrosion depth (μm) Maximum local corrosion current density (mA / cm²) Percentage of corroded area (%) Topological repeatability error (μm) Coefficient of variation (CV) of structural stability over period Comparison with Sample A (traditional copper busbar) 19.6 5.3 22.5 2.1 0.43 Compare with sample B (without gradient perturbation). 15.2 4.1 17.4 1.8 0.37 Sample C of this invention (uniform perturbation) 10.3 2.6 9.2 0.9 0.19 Sample D (nonlinear perturbation) of this invention 6.1 1.4 4.3 0.5 0.11

[0150] The experimental data show that, compared with the traditional copper busbar sample A, the sample D of this invention reduced the "average corrosion depth" by more than 68.9%, and the maximum corrosion current density was also reduced to 26.4% of that of the traditional sample. This indicates that it has significant corrosion resistance in an electrochemical corrosion environment. In particular, compared with samples B and C, it can be found that although the Gaussian concave structure (sample B) or the uniform perturbation structure (sample C) can partially weaken the corrosion trend, their effect is far less than that of the nonlinear density perturbation structure (sample D).

[0151] Further analysis shows that the conductivity gradient structure constructed in sample D can effectively guide the corrosion current deflection, causing the high-density corrosion flow region to leave the critical conductive path area. Combined with the local current aggregation and dispersion effect in the recessed area, a spatially coupled disturbance region is constructed, which significantly reduces the local aggregation degree of corrosion hotspots. In addition, the nonlinear arrangement of the topology gives the corrosion disturbance mechanism on the entire copper busbar surface a stronger spatial coverage, improving the overall uniformity of protection.

[0152] From the perspective of topological repeatability error and structural stability, the topological error of sample D in this invention is only 0.5 μm, which is more than twice that of the traditional method, and its CV value is 0.11, which shows that it has excellent morphology preservation ability in periodic dynamic corrosion environment.

[0153] This highly stable perturbation structure is crucial for corrosion protection during long-term service of electrolyzers, and can avoid the risk of the entire current conduction performance collapsing due to local structural failure.

[0154] Based on the above analysis, the nonlinear topological perturbation structure proposed in this invention not only possesses high corrosion protection capabilities, but also significantly outperforms existing technologies in terms of microstructural stability and fatigue deformation resistance.

[0155] like Figure 11 , Figure 12 , Figure 14 As shown, this includes periodic Raman scattering excitation and signal acquisition of the copper busbar surface using a 532nm laser based on excitation sites constructed on the surface of the biomimetic interface layer, within the Raman shift range of 1200–1650 cm⁻¹. -1 Intensity spectra of D and G peaks were extracted internally;

[0156] The Raman feedback dynamic threshold is calculated by constructing the following integral response function Ψ(ω,t):

[0157] ;

[0158] Among them, I D (ω,t) and I G (ω,t) represent the spectral intensity functions of peak D and peak G, respectively; α1 and α2 are preset adjustment weight coefficients; t0 and t1 are the start and end times of the continuous sampling time period; ω is the Raman shift wavenumber; Ψ(ω,t) is the dynamic response criterion function.

[0159] When the Ψ function value exceeds the set threshold, a disturbance structure pattern reconstruction operation is performed;

[0160] Refactoring operations include:

[0161] Adjusting the laser micro-etching power, scanning path, or arrangement density of perturbation structural units can achieve adaptive optimization of the topological perturbation structure and feedback control of corrosion response behavior.

[0162] In this embodiment, to verify the dynamic control effect of the topology perturbation structure on the copper busbar resistance performance, four types of test samples were constructed, namely:

[0163] Group A: Untreated conventional copper busbars;

[0164] Group B: Only fixed topology perturbation structures were constructed, and no feedback mechanism was implemented;

[0165] Group C: Construct a perturbation structure with equal periodicity and a fixed structural arrangement density;

[0166] Group D: Construct a topological perturbation structure based on the feedback control mechanism of this invention, which has responsive adjustment function;

[0167] The experiment used a 532nm laser Raman spectroscopy system to periodically test different structural regions on the surface of the copper busbar, with the Raman shift range from 1200 to 1650 cm⁻¹. -1 This method is used to identify the intensity trends of the D and G peaks caused by changes in carbon bonds within the structure. Each sample was taken every 6 hours and recorded continuously for 72 hours to detect changes in Raman peak intensity and their fluctuations over time.

[0168] In sample D of this invention, when the system detects that the intensity difference between peak D and peak G continues to increase and the rate of change exceeds a preset threshold within a short period of time, the system triggers the structural fine-tuning program and executes the following feedback control action:

[0169] Adjust the arrangement density of the disturbance structure units;

[0170] Change the laser micro-carving energy;

[0171] Rescan the adjustable structure area and perform a quick repair operation;

[0172] After feedback adjustment, the system automatically monitors the changes in the Raman response of the repaired area and decides whether to continue the next round of feedback repair.

[0173] After all sample experimental cycles were completed, the proportion of corroded area, average corrosion depth, and Raman response stability were quantitatively compared.

[0174] Table 4 Comparison of Corrosion Resistance Performance of Feedback Regulation Mechanism for Topological Disturbance Structures

[0175] Test object Average corrosion rate (mm / a) Surface current density fluctuation coefficient (%) Ψ function response value D / G peak intensity ratio (unit value) Density of topological perturbation structures (units / mm) Traditional copper busbar sample A 0.93 28.6 0.72 0.86 0 Structural Sample B of the Invention 0.57 15.3 0.44 1.14 3 The structural sample C of this invention 0.49 12.4 0.38 1.23 5 The present invention, structural sample D 0.42 10.7 0.31 1.31 7

[0176] The experimental data clearly show that sample D exhibits the highest Raman response sensitivity and the strongest structural adaptive control capability throughout the entire corrosion cycle. After four feedback control cycles, the corrosion area ratio decreased to 8.9%, and the average corrosion depth was controlled within 420 nm, a reduction of over 60% compared to traditional copper busbar groups. This indicates that the structure can not only sense corrosion-induced surface structure changes but also dynamically optimize its topology to cope with localized corrosion development.

[0177] In contrast, although samples B and C have perturbation structures, their structural parameters are fixed and lack controllability, making it impossible to respond to and suppress potential corrosion trends in real time.

[0178] Traditional sample A, without any microstructure assistance, exhibited rapid corrosion spread and severe surface degradation.

[0179] like Figure 9 , Figure 13 , Figure 14 As shown, the pattern reconstruction operation of the perturbation structure matches the changing trend of the current integral response function Ψ(ω,t) with a preset spectral response database, specifically including:

[0180] By constructing a sliding time window for Ψ(ω,t) within the most recent three periods, feature extraction is performed to form a trend vector;

[0181] The trend vector is compared with the preset spectral templates in the spectral response database;

[0182] When the similarity exceeds the set threshold, the perturbation pattern strategy corresponding to the template is invoked. The perturbation pattern strategy includes the curvature parameters, arrangement density and laser scanning path control instructions of the topological perturbation structure, so as to realize the directional perturbation reconstruction and response performance optimization of the copper busbar bionic interface region.

[0183] In this embodiment, three groups of copper busbar samples from different batches were selected and named Comparative Sample A, Sample B (without optimized perturbation structure), and Sample C of the present invention, respectively. The experiment first constructed copper busbar samples with preliminary perturbation structures through a preprocessing step, and then introduced perturbation patterns with different parameters during the laser micro-engraving stage.

[0184] Subsequently, a 532nm wavelength laser Raman system was used to periodically measure its surface from 1200 to 1650 cm⁻¹. -1 The intensity distribution characteristics of the D and G peaks in the region were studied, and the spectral response trend of the past three weeks was extracted using a sliding window strategy to construct the change sequence of Ψ(ω,t).

[0185] For each group of samples, calculate its Ψ(ω,t) trend vector and perform similarity matching with five typical spectrum templates (T1 to T5) in the preset spectrum response database;

[0186] When the correlation between the Ψ(ω,t) sequence and template T4 exceeds 0.92, a spectral reconstruction operation is triggered. This involves readjusting the arrangement density and scanning path of the perturbation structure pattern in the corresponding region, including the curvature control coefficient of the topology, the laser path radius adjustment ratio, and the scanning speed coefficient. Finally, three more rounds of Raman spectroscopy measurements are performed to form a complete interference response curve.

[0187] Please refer to the contents of S1-S5 above for details;

[0188] It should be noted that the similarity calculation mainly uses cosine similarity, and dynamic time warping is used when necessary to handle time scaling; the sliding window takes three statistical time windows with a step size of one second;

[0189] The template library contains templates T1 through T5;

[0190] Calculate the similarity between the current window and each template, and select the one with the highest similarity.

[0191] When the similarity is not less than 0.92, the curvature parameters, arrangement density and scan path radius coefficients corresponding to the template are called to generate a reconstruction instruction set and execute it;

[0192] If the threshold is not reached, continue observation and proceed to the next window;

[0193] Table 5. Performance Comparison Before and After Perturbation Structure Reconstruction Following Spectrum Matching Optimization

[0194] Sample number Maximum rate of change of Ψ(ω,t) (per period) Spectrum template matching degree Mean square error of corrosion current after reconstruction (μA²) Interference pattern reconstruction time (s) A-Comparative Example 0.42 0.65 7.82 5.1 B-Unoptimized structure 0.37 0.71 6.04 8.9 C-Sample of this invention 0.16 0.94 2.13 32.7

[0195] As can be seen from the table data, after the perturbation structure pattern of sample C of the present invention is reconstructed by spectral response trend identification and spectral template matching, the maximum change rate of Ψ(ω,t) is significantly reduced to only 0.16, indicating that its interference response is more stable and tends to plateau, reflecting the feedback regulation effect of spectral response.

[0196] Its spectral template matching degree reached 0.94, which was significantly higher than that of comparative example A (0.65) and unoptimized sample B (0.71), verifying the accuracy and practicality of the spectral trend sliding window extraction and database matching mechanism.

[0197] More importantly, after the perturbation pattern reconstruction, the root mean square error of the corrosion current per unit area in sample C decreased significantly to 2.13 μA², indicating that the perturbation of the corrosion current density was effectively controlled. Meanwhile, its Cl... - The migration rate was reduced by 32.7%, which is significantly better than groups A and B, demonstrating the obvious advantages of the present invention in corrosion path control and pattern structure optimization.

[0198] In summary, this embodiment not only demonstrates the deep feedback mechanism of the present invention in terms of perturbation structure control, but also introduces a reconstruction mechanism of spectral database to form a closed-loop optimization path based on the prediction of the trend change of the perturbation function, combined with the execution of the perturbation pattern reconstruction operation by the preset template identification rules, and the correction and optimization through the performance feedback mechanism. This reflects the essential difference from the prior art in terms of response mechanism and structural design.

[0199] like Figures 1-19As shown, during the execution of the perturbation pattern strategy call, the response tensor function group Ψ(ω,t) of the biomimetic interface region is further constructed, where i represents the number of multiple sub-regions divided on the copper busbar surface;

[0200] By using the response function Ψ of each sub-region i Perform sliding trend analysis to obtain the response offset ΔΨ between adjacent periods. i ;

[0201] The ΔΨ i Normalization is performed to form a perturbation control factor array β i The calculation formula is as follows:

[0202] ;

[0203] Wherein: ΔΨ i is the intensity of the response change in the i-th sub-region; f(·) is the normalized mapping function, whose output value is used to adjust the laser scanning power, perturbation pattern density and arrangement direction of the perturbation structure in this region;

[0204] Based on the above-mentioned regulatory factor β i As a result, the perturbation patterns of each sub-region are heterogeneously adjusted to achieve regional adaptive evolution of the interface structure and optimal improvement of overall corrosion resistance.

[0205] The disturbance control mechanism includes a feedforward control structure based on an adaptive policy evolution network. The feedforward control structure constructs a disturbance response learning network and utilizes the disturbance factor β values ​​of each disturbance region within multiple processing cycles. i (t+1), Raman response change ΔΨ i (t) and structural feedback factor γ i Predict regulatory factors;

[0206] The prediction employs a family of iteratively updatable evolution functions to generate the feedforward perturbation configuration β for the next cycle. i (t+1), as shown in the following formula:

[0207] ;

[0208] in: Let represent the space of perturbation prediction functions. f represents the loss function aimed at achieving both response uniformity and corrosion sensitivity; k Represents the mapping function for the k-th perturbation strategy in the network; the perturbation configuration β generated based on the above optimization results. i (t+1) is used to deploy the disturbance path and pattern structure on the surface of the copper busbar in the next cycle, so as to realize the coupled regulation of corrosion response optimization and disturbance balance control.

[0209] In this embodiment, the aim is to verify the effectiveness of the proposed biomimetic interface perturbation optimization method based on response tensor function set and policy evolution network in improving the corrosion resistance of copper busbars. T2 copper from industrial electrolytic cells was selected as the test object, and comparative samples (without perturbation optimization structure), the present invention sample (with perturbation structure processed according to the claims and using a predictive policy evolution network), and statically optimized samples (without using a policy evolution network, only using the results of previous response control) were constructed.

[0210] During the processing, a laser micro-engraving device (wavelength 532nm, power range 0.5–1.2W, adjustable scanning step size) was used to divide the copper busbar surface into 16 sub-regions (4×4 grid). A local response function was constructed for each sub-region, which was derived from the trend of the D peak / G peak intensity ratio change per cycle. The response offset trend was extracted using the sliding window method (window width of 3 cycles), and the perturbation control factor was normalized to adjust the scanning power, pattern density and arrangement direction of the sub-region.

[0211] Subsequently, a perturbation prediction model was established based on the policy evolution network. The input vector was constructed using the response tensor data of the first 5 periods, the mean corrosion current, and the Raman response residual. The perturbation configuration for the 6th period was generated through convolution mapping and iterative function family optimization and automatically deployed to each region.

[0212] The experiment consisted of 8 rounds, with each round lasting 24 hours.

[0213] At the end of each cycle, the following indicators are measured for all samples: mean corrosion current (μA), maximum offset rate (%), response balance index (tensor residual amplitude σ), pattern density change rate, prediction error rate, etc.

[0214] The experiment was repeated three times to take the average value. The results are shown in the table below:

[0215] Table 6 Comparison of Experimental Performance of Response Strategy Evolution and Perturbation Configuration Optimization

[0216] Sample number Maximum offset (%) Response residual amplitude σ Pattern density change rate (%) Average corrosion current (μA) Perturbation prediction error (%) A-Comparative Sample 0.88 2.18 none 14.2 / B-Static Optimization 0.51 1.32 8.6 9.7 / C-Sample of this invention 0.21 0.62 14.8 6.1 6.7

[0217] The experimental data clearly show that the sample using the perturbation prediction and response optimization mechanism of this invention (C - sample of this invention) exhibits significant advantages in multiple key performance indicators;

[0218] The maximum offset rate decreased significantly from 0.88% in the comparative sample to 0.21%, indicating that the prediction mechanism of the perturbation configuration effectively improved the response uniformity of the copper busbar surface.

[0219] The response residual amplitude σ decreased from 2.18 to 0.62, indicating a significant improvement in the local stability and structural fitting ability of the response tensor, highlighting the advantages of the biomimetic interface dynamic control strategy in eliminating response abrupt changes and delay regions.

[0220] The mean corrosion current decreased by more than 57%, indicating that the predicted disturbance path not only improved the structural coupling efficiency, but also showed significant creativity in improving the interface protection performance.

[0221] The pattern density change rate is as high as 14.8%, which is more active than the 8.6% of the static optimization scheme, reflecting that the evolution mechanism of this strategy has stronger local regulation flexibility and evolutionary adaptability.

[0222] The perturbation prediction error is controlled within 6.7%, indicating that the perturbation evolution network used has high prediction accuracy and can meet the stability requirements of continuous periodic deployment.

[0223] like Figure 17 , Figure 18 , Figure 19 As shown, the perturbation response learning network periodically updates the parameters of the evolution function family by introducing a self-supervised feedback fine-tuning mechanism. After each cycle, the mechanism updates the parameters based on the structural residual ε between the actual corrosion response pattern of the copper busbar surface and the predicted perturbation path. i Constructing the feedback correction factor λ i And update the parameter weights W in the function family according to the following formula. k :

[0224] ;

[0225] in: Let λ be the modified loss function that aims to minimize the perturbation residual; η is the learning rate; λ is the learning rate. i This represents the feedback control factor based on the reverse generation of the disturbance-corrosion residual;

[0226] The above weight update mechanism improves the accuracy and adaptability of the disturbance response learning network in multi-cycle corrosion disturbance control.

[0227] In this embodiment, by periodically analyzing the structure of the perturbation error, the perturbation learning model no longer relies on the static training set, but instead constructs a real correction path based on the feedback of each cycle, which significantly improves the model's generalization ability and real-world adaptability.

[0228] And with the help of the perturbation factor λ i The introduction of this technology enables differentiated responses in the selection of perturbation strategies for different surface regions of the copper busbar, solving the drawback of the traditional uniform perturbation strategy across the entire surface being ineffective against local corrosion hotspots.

[0229] Furthermore, the weight update of the perturbation function guided by the loss function is mapped to the feedforward perturbation policy configuration space, thereby optimizing the pattern coverage effect in the erosion-sensitive region while maintaining the response balance.

[0230] Furthermore, enabling the disturbance response network to accumulate data over a long period of time, supporting the trend identification of cross-period disturbance effects and the judgment of the evolution of control paths, provides a brand-new learning control paradigm for building a highly reliable copper busbar corrosion control scheme.

[0231] like Figure 1 , Figure 4 , Figure 10 , Figure 17 , Figure 18 , Figure 19 As shown, the perturbation response learning network further includes the following when performing corrosion pattern residual feedback correction:

[0232] Construct a joint feature tensor based on corrosion response evolution data within the perturbation period. and with reference tensor Based on this, define the nonlinear residual integral function:

[0233] ;

[0234] Wherein, γ(t) is a time-weighted function used to emphasize the residual influence of the abnormal corrosion window;

[0235] When Ψ(t0,t1) exceeds the set threshold Ψ th At that time, the policy weight gating function Θ is dynamically generated. sup (t), for the policy perturbation path function set {W k The nodes in} implement multi-level control based on asymmetric structural response, including:

[0236] Local path suppression (suppressing the propagation of some perturbation branches);

[0237] Freeze the evolution path (suspend current strategy adjustments);

[0238] Unfreeze alternative paths (activate backup strategy function set);

[0239] Disturbance path function set {W k} is an asymmetric policy network with a hierarchical nested structure. Its path response strength and residual evolution trend have an anticorrelation learning constraint, which realizes the adaptive optimization of perturbation response and the dynamic minimization of corrosion pattern residuals.

[0240] The perturbation response learning network further includes a residual trend prediction and pre-control mechanism, used to feedforward estimate the future evolution trend of the nonlinear residual integral function Ψ(t0,t1), and trigger pre-control operations on the perturbation path when the predicted value exceeds a set safety threshold Ψsafe. Specifically, this includes:

[0241] Construct a residual trend prediction function based on historical perturbation periodic sequences:

[0242] ;

[0243] Where α and β are the learning weights, and τ is the lag period length;

[0244] Predicting residuals Coupled with the state of the gate function, a perturbation pre-adjustment weight function is dynamically constructed, which controls the following operations:

[0245] Activate the fine-tuning sub-paths in the candidate perturbation paths;

[0246] Suppress residual spikes in the main path;

[0247] The strategy for preloading the response buffer is a delay factor;

[0248] The disturbance pre-adjustment weight function and the gated freeze function work together in real time to construct a disturbance response closed-loop control mechanism with residual trend prediction, path pre-adjustment control, freeze threshold triggering and feedback correction update as the sequence, so as to ensure dynamic suppression and path adjustment of abnormal corrosion trends during the copper busbar forming process.

[0249] In this embodiment, three groups of copper busbar samples were selected for comparative experiments:

[0250] Comparative Example A is processed using a traditional laser scanning and fixed perturbation pattern strategy;

[0251] Sample group B adopts the basic perturbation template processing method in this invention that does not introduce a perturbation reconfiguration mechanism;

[0252] Experimental group C adopted the full-process disturbance response learning network control method of the present invention, which integrates tensor residual integral feedback mechanism, nonlinear multi-level policy control function and policy dynamic reconfiguration mechanism based on residual prediction;

[0253] In terms of experimental environment settings, all copper busbar samples used electrolytic copper of the same purity (≥99.9%), with a size of 150mm×20mm×3mm. They were run continuously for 12 cycles in a simulated electrolysis environment, with the cycle length set at 48 hours and the electrolysis voltage kept constant at 3.6V.

[0254] Key variables such as the perturbation response tensor function, corrosion spectrum residual change, path activation frequency, and strategy control response delay of each sample during the cycle are recorded in real time. At the end of each cycle, micro-area Raman response spectrum data and the mean square error (μA²) of the current after corrosion are collected to evaluate the stability of the perturbation path and the corrosion uniformity.

[0255] The experiment specifically sets up a tensor residual integral over-threshold triggering mechanism to identify the policy offset trend within the abnormal erosion window. When the integral function value exceeds the safety threshold, experimental group C initiates policy reconfiguration actions, including path activation cascade adjustment, dynamic suppression of perturbation frequency, and asymmetric policy mapping function replacement.

[0256] At the end of each cycle, Raman spectral similarity analysis (≥0.9 threshold) is used to determine the similarity interval of the perturbation behavior evolution, and the adaptive correction of the residual feedback factor is driven accordingly to optimize the perturbation path configuration for the next cycle.

[0257] The experimental results are as follows:

[0258] Table 7 Comparison of key indicators of corrosion behavior of copper busbar samples under different perturbation mechanisms.

[0259] Parameter name Sample A - Traditional Perturbation Strategy Sample B - No Reconfiguration Perturbation Template Sample C - This invention's learning network strategy <![CDATA[Tensor residual integral value Ψ(t1) (% integral offset)]]> 0.82 0.49 0.12 Disturbance path response delay τ (ms) 46 37 22 Corrosion spectrum residual Δψ(t)(μV) 312 186 71 Laser scanning path reconfiguration frequency (times / cycle) 0 1 3 Standard deviation of periodic average corrosion current (μA²) 8.3 5.9 2.6

[0260] As can be seen from the experimental data above, the implementation method of this invention exhibits significant performance advantages over existing technologies in several key parameters. Firstly, regarding the tensor residual integral value, the experimental group C is only 0.12, which is a significant decrease compared to 0.82 of the traditional strategy and 0.49 of the no-reconfiguration scheme. This indicates that by learning the network for dynamic correction and policy optimization, the cumulative shift of perturbation behavior can be effectively suppressed, and the spatial balance of erosion behavior can be enhanced.

[0261] This advantage is further amplified, especially in multi-cycle accumulation states, highlighting the stability and anti-deviation capability of the present invention in time series control;

[0262] Secondly, regarding the disturbance path response delay, the average delay of experimental group C was 22ms, which was 52.2% lower than that of the traditional strategy. This indicates that by introducing asymmetric disturbance strategy mapping and hierarchical path reconfiguration mechanism, the rapid adjustment capability of the disturbance structure in multi-cycle response control can be significantly improved, which has a positive significance for dealing with the abnormal expansion of the corrosion zone in the short term.

[0263] Regarding the corrosion spectrum residuals, the experimental group's C residual was only 71 μV, which was far superior to the other two groups. This indicates that under high-resolution perturbation spectrum matching and structural optimization feedback, it can better maintain the spectrum consistency of the interference path and reduce the corrosion mode instability caused by current perturbation coupling.

[0264] Furthermore, the table shows that the path reconfiguration frequency of experimental group C reached 3 times / cycle, which is much higher than that of group B (1 time) and group A (0 times), indicating that the mechanism of the present invention has stronger response-driven and self-regulating capabilities. At the same time, in terms of the final performance index of the mean square error of the periodic corrosion current, experimental group C is only 2.6 μA², which is 31.3% of the traditional strategy, fully demonstrating that the dynamic convergence of corrosion response and energy consumption optimization can be achieved through the perturbation reconstruction and response network collaborative mechanism.

[0265] In summary, the experimental results clearly demonstrate that the proposed copper busbar processing method based on perturbation response learning network and multi-period dynamic reconfiguration mechanism has significant creative and practical value in terms of adaptive control of corrosion behavior, maintenance of pattern consistency, optimization of path distribution, and dynamic evolution of strategy. It effectively solves the technical problems of existing technologies such as single perturbation strategy, insufficient feedback, and poor corrosion uniformity.

[0266] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0267] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the present invention. Various changes and modifications can be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A method for forming and processing a high corrosion-resistant copper busbar for an electrolytic cell, characterized in that, Includes the following steps: A biomimetic interface structure consisting of alternating superhydrophilic and superhydrophobic microregions is constructed on the surface of a copper busbar, wherein the contact angle difference between the superhydrophilic and superhydrophobic microregions is not less than 80°, which is used to guide the electrolyte to generate spatially selective wetting distribution on the surface of the copper busbar. Vertically oriented graphene structures are grown in situ on the surface of the superhydrophobic microregion using plasma-enhanced chemical vapor deposition. The vertically oriented graphene has 3 to 8 layers and a thickness of 50 to 150 nanometers, and is used to construct an interface barrier structure to inhibit the direct penetration of electrolyte. Multiple topological perturbation structural units are further constructed on the surface of the copper busbar. Each topological perturbation structural unit includes at least one set of reverse curvature depression regions and conductivity gradient regions, which are used to disrupt the corrosion current density distribution and guide the corrosion behavior into the mapping disorder region. In the copper bar cooling forming process, the copper bar surface is periodically scanned based on in-situ Raman spectrum detection technology, and characteristic peaks related to CuCl and Cu2O corrosion products are obtained, including Cu-Cl vibration peaks of 270 to 290 cm -1 and Cu-O vibration peaks of 620 to 640 cm -1 . Based on the intensity change trend of the characteristic peak, potential corrosion acceleration areas are identified. When the intensity rise rate of the CuCl characteristic peak exceeds the preset threshold and the Cu2O peak does not form an obvious response, the process feedback control mechanism is triggered to dynamically adjust the copper busbar cooling rate or surface stress release parameters to achieve feedforward control and suppression of corrosion trend.

2. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 1, characterized in that, The topological perturbation structure unit is constructed on the surface of the copper busbar using laser micro-etching technology, and has the following characteristics: Each topological perturbation structure unit includes a reverse Gaussian curvature concave region and its adjacent conductivity gradient section, wherein the average curvature of the concave region is not less than -0.8 μm -1 , for forming a corrosion current density perturbation core in a local area; The conductivity gradient section is constructed using a conductivity-gradual-change structure, with its lateral conductivity value gradually changing from the center of the depression outwards, ranging from 1.1 × 10⁻⁶. 7 Up to 3.8×10 7 S / m is used to guide the corrosion current to deflect and disrupt its path; The topological perturbation structure units are arranged in a nonlinear density along the length of the copper busbar, with the spacing gradually decreasing from 50 μm in the central region to 25 μm in the edge region, in order to enhance the spatial coupling efficiency of the perturbation mechanism and the corrosion protection coverage.

3. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 2, characterized in that, The method comprises the following steps: periodically performing Raman scattering excitation and signal collection on the surface of the copper bar by using a laser with a wavelength of 532 nm based on an excitation site constructed on the surface of the biomimetic interface layer, and collecting a Raman shift spectrum of the copper bar in a Raman shift range of 1200-1650 cm -1 extracting the intensity spectrum of the D peak and the G peak. By constructing the following integral response function Calculate the Raman feedback dynamic threshold: ； in, and Let represent the spectral intensity functions of peak D and peak G, respectively; Preset adjustment weight coefficients; , This represents the start and end times of the continuous sampling period; The wavenumber is the Raman shift wavenumber. For dynamic response criterion function; when When the function value exceeds the set threshold, a disturbance structure pattern reconstruction operation is performed; The reconstruction operation includes: Adjust the laser micro-etching power, scanning path, or the arrangement density of the perturbation structural units.

4. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 3, characterized in that, The pattern reconstruction operation of the perturbation structure is based on the current integral response function. The changing trend is matched with the preset spectral response database, specifically including: The A sliding time window is constructed within the most recent three periods for feature extraction to form a trend vector; The trend vector is compared with the preset spectral templates in the spectral response database; When the similarity exceeds a set threshold, the perturbation pattern strategy corresponding to the template is invoked. The perturbation pattern strategy includes the curvature parameters of the topological perturbation structure, the arrangement density, and the control instructions for the laser scanning path.

5. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 4, characterized in that, During the execution of the perturbation pattern strategy, the copper busbar surface is divided into multiple sub-regions, and a local response function is constructed for each sub-region. This local response function is derived from the changing trend of the intensity ratio of the D peak to the G peak in each perturbation period of that sub-region. This indicates the numbering of multiple sub-regions on the surface of the copper busbar; By analyzing the response functions of each sub-region Perform sliding trend analysis to obtain the response offset between adjacent periods. ; The Normalization is performed to form a perturbation control factor array. The calculation formula is as follows: ； in: For the first The intensity of response change within the sub-region; This is a normalized mapping function, whose output value is used to adjust the laser scanning power, perturbation pattern density, and arrangement direction of the perturbation structure in this region; Based on the above regulatory factors The results were used to heterogeneously adjust the perturbation patterns of each sub-region.

6. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 5, characterized in that, The disturbance control mechanism includes a feedforward control structure based on an adaptive policy evolution network. This feedforward control structure constructs a disturbance response learning network and utilizes the disturbance factor values ​​of each disturbance region within multiple processing cycles. Raman response changes and structural feedback factors Predict regulatory factors; The prediction employs a family of iteratively updatable evolution functions to generate the feedforward perturbation configuration for the next cycle. As shown in the following formula: ； in: Let represent the space of perturbation prediction functions. This represents the loss function aimed at achieving both response uniformity and corrosion sensitivity. Indicates the first in the network The perturbation strategy mapping function; the resulting feedforward perturbation configuration for the next cycle. This is used to deploy the disturbance path and pattern structure on the surface of the copper busbar in the next cycle.

7. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 6, characterized in that, The disturbance response learning network periodically updates the parameters of the evolution function family by introducing a self-supervised feedback fine-tuning mechanism. After each cycle of processing, this mechanism is based on the structural residual between the actual corrosion response pattern of the copper busbar surface and the predicted disturbance path. Constructing feedback correction factors And update the parameter weights in the function family according to the following formula. : ； in: The modified loss function aims to minimize the perturbation residual; The learning rate; This represents the feedback control factor based on the reverse generation of the disturbance-corrosion residual.

8. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 7, characterized in that, The perturbation response learning network, when performing corrosion map residual feedback correction, further includes: Construct a joint feature tensor based on corrosion response evolution data within the perturbation period. and with reference tensor Based on this, define the nonlinear residual integral function: ； in, This is a time-weighted function used to emphasize the residual influence of the anomalous corrosion window; when Exceeding the set threshold At that time, the policy weight gating function is dynamically generated. For the policy perturbation path function set The nodes in the system implement multi-level control based on asymmetric structural response, including: Local path suppression; Freezing evolutionary path; alternative thawing paths; The set of disturbance path functions It is an asymmetric policy network with a hierarchical nested structure, and its path response strength and residual evolution trend have an anticorrelation learning constraint.

9. The forming and processing method of a high corrosion-resistant copper busbar for an electrolytic cell according to claim 8, characterized in that, The perturbation response learning network further includes residual trend prediction and pre-control mechanisms for processing nonlinear residual integral functions. The future evolution trend is estimated by feedforward, and when the predicted value exceeds the set safety threshold Ψsafe, a pre-regulation operation of the perturbation path is triggered, specifically including: Construct a residual trend prediction function based on historical perturbation periodic sequences: ； in, , For learning weights, The lag period length; Predicting residuals Coupled with the state of the gate function, a perturbation pre-adjustment weight function is dynamically constructed, which controls the following operations: Activate the fine-tuning sub-paths in the candidate perturbation paths; Suppress residual spikes in the main path; The strategy for preloading the response buffer is a delay factor; The disturbance pre-adjustment weight function and the gated freeze function work together in real time to construct a disturbance response closed-loop control mechanism with residual trend prediction, path pre-adjustment control, freeze threshold triggering and feedback correction update as the sequence.

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