A composite electrode state detection method, system, device and storage medium

Abstract

The present invention discloses a method, system, device, and storage medium for detecting the state of a composite electrode, relating to the field of electrochemical detection technology. The method includes mapping a micro-area current signal to a quantum anomalous Hall state and calculating the topological index of the quantum Hall state transition; obtaining the electrode surface potential distribution and calculating the Shannon entropy-Kirchhoff field gradient in combination with the micro-area current value; locating the active material shedding position based on the spatial distribution of the topological index, while tracing the thermodynamically irreversible source point based on the direction of the Shannon entropy-Kirchhoff field gradient; and fusing the active material shedding position with the thermodynamically irreversible source point to generate a three-dimensional electrode degradation thermogram, dynamically outputting the health status level. By fusing the topological index and the Shannon entropy-Kirchhoff field gradient, the present invention constructs a dynamic weighted composite risk factor, synchronously capturing the topological anomaly of electrode active material shedding and the entropy change characteristics of the thermodynamically irreversible process, thereby achieving collaborative perception of electrode degradation risk.

Description

Technical Field

[0001] The present invention relates to the field of electrochemical detection technology, and in particular to a method, system, device and storage medium for detecting the state of a composite electrode. Background Art

[0002] In recent years, significant progress has been made in electrochemical analysis and signal processing technologies for monitoring the health of composite electrodes. Advanced methods such as micro-area current signal extraction and surface potential distribution detection have been widely applied, combined with quantum Hall effect theory and information entropy algorithms to improve the accuracy of condition assessment. Existing solutions often employ multi-parameter sensing and data fusion strategies, such as non-destructive detection techniques based on electrochemical impedance spectroscopy and dynamic modeling methods combined with artificial intelligence, striving to achieve early warning and quantitative characterization of electrode degradation processes.

[0003] While existing technologies offer some monitoring capabilities, they struggle to identify localized thermodynamically irreversible risk zones within composite electrodes in real time, making it difficult to precisely locate the source of active material shedding. Especially when processing high-dimensional, heterogeneous data, existing methods suffer from dynamic response delays and insufficient spatial resolution. This results in low accuracy and specificity in active material shedding risk prediction, making it ineffective for guiding preventive maintenance. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for detecting the state of a composite electrode to solve the problems of insufficient prediction accuracy of the risk of active material shedding in the composite electrode and fuzzy positioning of the thermodynamic irreversible source point.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In the first aspect, the present invention provides a method for detecting the state of a composite electrode, which includes mapping a micro-area current signal to a quantum anomalous Hall state, and calculating the topological index of the quantum Hall state conversion; obtaining the electrode surface potential distribution, and calculating the Shannon entropy-Kirchhoff field gradient in combination with the micro-area current value; generating a composite risk factor by dynamically weighted fusion of the topological index and the Shannon entropy-Kirchhoff field gradient, and when the topological index exceeds a first threshold and the Shannon entropy-Kirchhoff field gradient exceeds a second threshold, triggering an active substance shedding warning; otherwise, maintaining the current monitoring state and continuously updating the dynamic weighting coefficient; locating the active substance shedding position according to the spatial distribution of the topological index, and tracing the thermodynamically irreversible source point based on the direction of the Shannon entropy-Kirchhoff field gradient; fusing the active substance shedding position and the thermodynamically irreversible source point to generate a three-dimensional electrode degradation heat map, and dynamically outputting the health status level.

[0008] As a preferred embodiment of the method for detecting the state of the composite electrode according to the present invention, wherein:

[0009] The quantum anomalous Hall state mapping is performed on the micro-area current signal to obtain the state density distribution characteristics;

[0010] Extract the quantized step extreme value points in the state density distribution characteristics and count the number of quantized step extreme value points;

[0011] Based on the correspondence between the number of quantized step extreme points and the topological invariant of the quantum Hall effect, the topological index of the quantum Hall state transition is calculated.

[0012] As a preferred solution of the method for detecting the state of the composite electrode of the present invention, wherein: the amplitude of the micro-area current signal is extracted and the micro-area current value is output;

[0013] The Shannon entropy-Kirchhoff field gradient is calculated by combining the micro-area current value, and the specific steps are as follows:

[0014] Obtain the spatial gradient vector of the electrode surface potential distribution;

[0015] The micro-area current value and the spatial gradient vector are normalized, and the Shannon entropy change rate of the local area is calculated based on the normalized micro-area current value;

[0016] The Shannon entropy-Kirchhoff field gradient component is determined by multiplying the direction of the spatial gradient vector by the rate of change of the Shannon entropy in the local area;

[0017] The spatial distribution of each Shannon entropy-Kirchhoff field gradient component is integrated to obtain the Shannon entropy-Kirchhoff field gradient.

[0018] As a preferred embodiment of the method for detecting the state of the composite electrode of the present invention, the steps of generating the composite risk factor are as follows:

[0019] Get the topological index and Shannon entropy-Kirchhoff field gradient at the current moment;

[0020] Training dynamic weighting coefficients based on historical monitoring data;

[0021] The topological index and Shannon entropy-Kirchhoff field gradient are enhanced by dynamic weighting coefficients to form quantitative weighted index components.

[0022] The quantitative weighted indicator components are fused using linear superposition to output a composite risk factor.

[0023] As a preferred solution of the method for detecting the state of the composite electrode described in the present invention, wherein: the first threshold is the critical point of the risk of active material shedding; the second threshold is the critical point of the risk of thermodynamic irreversible process; maintaining the current monitoring state and continuously updating the dynamic weighting coefficient means that when the active material shedding warning is not triggered, the first weighting coefficient and the second weighting coefficient of the next cycle are adjusted according to the deviation between the current composite risk factor and the historical average value.

[0024] As a preferred embodiment of the method for detecting the state of the composite electrode of the present invention, the following steps are performed: locating the shedding position of the active material according to the spatial distribution of the topological index, and tracing the thermodynamic irreversible source point based on the direction of the Shannon entropy-Kirchhoff field gradient.

[0025] The spatial distribution of the topological index is divided into two-dimensional grids, and the areas in the grid where the topological index exceeds the local mean are marked to determine the location where the active material falls off;

[0026] The direction vector of the Shannon entropy-Kirchhoff field gradient is traced in reverse, integrated in the opposite direction of the gradient to the starting point of the gradient, and the thermodynamic irreversible source point is determined.

[0027] As a preferred solution of the method for detecting the state of the composite electrode of the present invention, the steps of dynamically outputting the health status level are as follows:

[0028] Establish health status classification standards;

[0029] Based on the statistical distribution range of the composite risk factors in historical monitoring data, the critical interval boundaries corresponding to each level are divided, and the current composite risk factors are compared with the critical interval boundaries of each level to determine the initial health level;

[0030] The initial health level is corrected based on the number of active substance shedding locations and the thermodynamic intensity of the thermodynamically irreversible source point, and the corrected health status level is output.

[0031] In the second aspect, the present invention provides a detection system for the state of a composite electrode, including a quantum mapping module, an entropy field calculation module, a risk warning module, a traceability module and a heat map generation module; the quantum mapping module is used to map the micro-area current signal to the quantum anomalous Hall state and calculate the topological index of the quantum Hall state conversion; the entropy field calculation module is used to obtain the electrode surface potential distribution and calculate the Shannon entropy-Kirchhoff field gradient in combination with the micro-area current value; the risk warning module is used to generate a composite risk factor by dynamically weighted fusion of the topological index and the Shannon entropy-Kirchhoff field gradient. When the topological index exceeds a first threshold and the Shannon entropy-Kirchhoff field gradient exceeds a second threshold, an active substance shedding warning is triggered; otherwise, the current monitoring state is maintained and the dynamic weighting coefficient is continuously updated; the traceability module is used to locate the active substance shedding position according to the spatial distribution of the topological index, and at the same time trace the thermodynamically irreversible source point based on the direction of the Shannon entropy-Kirchhoff field gradient; the heat map generation module is used to fuse the active substance shedding position and the thermodynamically irreversible source point to generate a three-dimensional electrode degradation heat map and dynamically output the health status level.

[0032] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, any step of the method for detecting the state of a composite electrode as described in the first aspect of the present invention is implemented.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for detecting the state of a composite electrode as described in the first aspect of the present invention is implemented.

[0034] The beneficial effects of the present invention are: by fusing the topological index of quantum Hall state transition and the Shannon entropy-Kirchhoff field gradient, a dynamically weighted composite risk factor is constructed, which can simultaneously capture the topological anomaly characteristics of the shedding of electrode active materials and the entropy change gradient characteristics of the thermodynamically irreversible process, thereby realizing the collaborative perception of electrode degradation risks; the weighting coefficient is adaptively adjusted based on historical monitoring data to ensure that the risk factor dynamically adapts to the degradation mode under different working conditions; the dual threshold trigger mechanism (the first threshold is for the topological index, and the second threshold is for the entropy change gradient) significantly improves the accuracy of the active material shedding warning and avoids the misjudgment of a single indicator; the dynamic weighted fusion method of the composite risk factor provides a highly reliable quantitative criterion for the subsequent precise positioning of the shedding position and tracing of the thermodynamically irreversible source point, forming a closed-loop analysis basis from risk warning to root cause diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 Flowchart of the method for detecting the state of a composite electrode.

[0037] Figure 2 Flowchart of the topological index calculation process.

[0038] Figure 3 Schematic diagram of Shannon entropy-Kirchhoff field gradient calculation.

[0039] Figure 4 This is a flow chart for dynamic determination of health status level. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0043] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides a method for detecting the state of a composite electrode, comprising the following steps:

[0044] S1: Map the micro-area current signal to the quantum anomalous Hall state and calculate the topological index of the quantum Hall state transition.

[0045] S1.1: Perform quantum anomalous Hall state mapping on the micro-area current signal to obtain the state density distribution characteristics.

[0046] Specifically, a quantum point contact sensor array is used to collect micro-area current signals on the surface of the composite electrode;

[0047] The collected micro-area current signal is sampled in time series to obtain a discrete current data point sequence, which is then processed using a fast Fourier transform and converted into a frequency domain representation to obtain a current spectrum.

[0048] Based on the current spectrum, the Hall conductance value is calculated using the quantum Hall effect conductance formula, which is expressed as:

[0049] ;

[0050] Where, represents the Hall conductance value, Indicates the direction of current flow (along a certain axis on the electrode surface, such as the horizontal direction), Indicates the measurement direction of the Hall voltage (the axis perpendicular to the current direction, such as the vertical direction), represents the topological index, represents the electron charge, represents Planck's constant;

[0051] The Hall conductance value is inversely calculated and combined with the Berry phase calculation to output the state density distribution characteristics at each position on the electrode surface.

[0052] Among them, the state density distribution characteristics include the state density values ​​corresponding to the spatial coordinates

[0053] For example: at the electrode position At the micro-area current signal sampling value is ; After fast Fourier transform, the main peak frequency of the spectrum is ; Substitute into the quantum Hall effect conductance formula to calculate , the corresponding state density value is deduced by the state density formula .

[0054] Derivation process of state density formula: state density value The relationship with conductivity is related to the Landauer-Büttiker formula, which is expressed as:

[0055] ;

[0056] Where, represents the energy variable centered on the Fermi level, the Fermi velocity The main peak frequency of the spectrum The expression is calculated as:

[0057] ;

[0058] in, represents Planck's constant, represents the Fermi level subscript identifier, represents the electron rest mass;

[0059] like , substitute into the calculation and get:

[0060] ;

[0061] S1.2: Extract the quantized step extreme points in the state density distribution characteristics and count the number of quantized step extreme points.

[0062] Specifically, input state density distribution characteristics;

[0063] The density of states distribution is characterized by a two-dimensional grid matrix, where each grid cell contains the position coordinates (for example: , in millimeters or micrometers) and state density values.

[0064] The central difference method is used to calculate the partial derivative of the state density value of each grid cell with respect to the spatial position, and the position of the derivative zero is identified. The expression is:

[0065] ;

[0066] ;

[0067] Where, Represents the density of states distribution exist The spatial rate of change of direction, represents the partial differential operator, Represented at the grid point Right adjacent grid point The density of states at Represented at the grid point Left adjacent grid point The density of states at and The denominator acts as a normalization factor for the difference approximation, Indicates that the grid is The spacing of the direction, The density of states is distributed in The spatial rate of change of direction, detecting longitudinal non-uniformity, and Represents grid points exist The state density values ​​at the adjacent points before and after the direction indicate that the grid is The spacing of the direction, Represents the first Center point of the column grid cell Coordinate position, used to identify the spatial position of the calculation point in the horizontal direction, Represents the first The center point of the row grid cell Coordinate position, used to identify the spatial position of the calculation point in the vertical direction;

[0068] Mark all satisfied and The grid cell is defined as the derivative zero position;

[0069] It should be noted that is the state density gradient convergence threshold, which is determined by experimentally calibrating the state density change rate of the quantized step area on the electrode surface. The specific value range is usually to (unit: ), the typical value is .

[0070] At the derivative zero point, we further detect the local extreme value of the state density value. If If the state density is greater than the state density of its eight neighboring grid cells, it is marked as a local maximum point; if If the density of states is smaller than that of its eight neighboring grid cells, it is marked as a local minimum point;

[0071] The local maximum and minimum points are uniformly defined as quantized step extreme points, and the entire two-dimensional grid matrix is ​​traversed to count the total number of all quantized step extreme points. , the output is an integer.

[0072] S1.3: Calculate the topological index of quantum Hall state transition based on the correspondence between the number of quantized step extreme points and the topological invariant of the quantum Hall effect.

[0073] Specifically, input the total number of quantized step extreme points .

[0074] Based on the correspondence between Chern number and quantized conductivity in quantum Hall effect, we can directly establish and topological index The mapping expression is:

[0075] ;

[0076] Where, is the proportionality factor (usually ,Right now );

[0077] It should be noted that the total number of quantized step extreme points Directly reflects the number of non-trivial topological states of the system (such as the Landau level degeneracy), which is linearly related to the Chern number, and outputs the calculated topological index (for example: if ,but ).

[0078] Among them, the Chern number is the core topological invariant of the quantum Hall effect. Direct Mapping Topology Index ,because Both the Chern number and the Landau number reflect the degeneracy of the Landau level in the electrode material, and therefore can be used to equivalently characterize the topological state of the composite electrode.

[0079] S2: Obtain the electrode surface potential distribution and calculate the Shannon entropy-Kirchhoff field gradient based on the micro-area current value.

[0080] The micro-area current value is derived from extracting the amplitude of the micro-area current signal and outputting the micro-area current value.

[0081] S2.1: Obtain the spatial gradient vector of the electrode surface potential distribution.

[0082] Specifically, input electrode surface potential distribution data;

[0083] It should be noted that the electrode surface potential distribution data is a two-dimensional grid matrix (consistent with the grid division in step S1.1), and each grid cell contains the position coordinates and potential value (unit: volt);

[0084] The central difference method is used to calculate the spatial gradient vector of the potential distribution and output the spatial gradient vector of each grid cell. .

[0085] S2.2: Normalize the micro-area current value and the spatial gradient vector, and calculate the Shannon entropy change rate of the local area based on the normalized micro-area current value.

[0086] It should be noted that the Shannon entropy change rate is used to reflect the dynamic changes in the information entropy of the current and potential distribution in the local micro-area.

[0087] Specifically, the micro-area current value and the spatial gradient vector are subjected to minimum-maximum normalization processing;

[0088] Furthermore, we define a local area (e.g., a 3×3 neighborhood) with each grid cell as the center and calculate the Shannon entropy in the local area at the current moment. The expression is:

[0089] ;

[0090] ;

[0091] Where, Indicates the current moment (time ) is used to quantify the dynamic disorder of the current and potential distribution in the micro region. represents the index of the discretization interval, represents the total number of discretization intervals, Indicates that the normalized micro-area current value falls within The probability of an interval, Represents the logarithm operation with base 2, ensuring that the unit of entropy is bit;

[0092] According to the local Shannon entropy at the last monitoring moment , calculate the local Shannon entropy change rate, the expression is:

[0093] ;

[0094] Where, It represents the rate of change of local Shannon entropy (unit: bit / s), reflecting the dynamic rate of change of information entropy in the local area. Indicates the time interval between two monitorings;

[0095] Output the local Shannon entropy change rate of each grid cell .

[0096] S2.3: Determine the Shannon entropy-Kirchhoff field gradient component by multiplying the direction of the spatial gradient vector by the rate of change of the Shannon entropy in the local region.

[0097] Specifically, the unit direction vector of the spatial gradient vector is calculated as follows:

[0098] ;

[0099] Where, is a unit direction vector (dimensionless), representing The normalized direction of is the modulus (Euclidean norm) of the gradient vector;

[0100] Project the Shannon entropy change rate to the unit direction vector and output the Shannon entropy-Kirchhoff field gradient component of each grid cell. The expression is:

[0101] ;

[0102] Where, represents the Shannon entropy-Kirchhoff field gradient component.

[0103] S2.4: Integrate the spatial distribution of each Shannon entropy-Kirchhoff field gradient component to obtain the Shannon entropy-Kirchhoff field gradient.

[0104] Specifically, the Shannon entropy-Kirchhoff field gradient components of all grid cells are input ;

[0105] Integrate the components by spatial position, construct component matrices in the horizontal and vertical directions, and output the Shannon entropy-Kirchhoff field gradient distribution dataset.

[0106] S3: A composite risk factor is generated by dynamically weighting the topological index and the Shannon entropy-Kirchhoff field gradient. When the topological index exceeds the first threshold and the Shannon entropy-Kirchhoff field gradient exceeds the second threshold, an active substance shedding warning is triggered. Otherwise, the current monitoring state is maintained and the dynamic weighting coefficient is continuously updated.

[0107] Among them, the first threshold is the critical point of active substance shedding risk; the second threshold is the critical point of thermodynamic irreversible process risk; maintaining the current monitoring status and continuously updating the dynamic weighting coefficient means that when the active substance shedding warning is not triggered, the first weighting coefficient and the second weighting coefficient of the next cycle are adjusted according to the deviation between the current composite risk factor and the historical average value.

[0108] It should be noted that the process of obtaining the critical point of the active material shedding risk is: through experimental calibration of the correspondence between the topological index of the quantum Hall state transition and the shedding phenomenon of active materials on the electrode surface, the actual shedding state of the electrode under different topological indices is recorded in long-term monitoring, and the minimum topological index threshold that can reliably predict the shedding of active materials is determined by combining microscopic observations and electrochemical performance decay data.

[0109] The process of obtaining the critical point of the risk of the thermodynamically irreversible process is as follows: based on the correlation analysis between the Shannon entropy-Kirchhoff field gradient and the thermodynamically irreversible damage of the electrode, by comparing the entropy change rate gradient amplitude of the electrode under different working conditions with the changes in the microstructure of the electrode material and the change laws of the interface reaction kinetic parameters, a critical gradient threshold reflecting the starting point of the thermodynamically irreversible process is established.

[0110] S3.1: Get the topological index and Shannon entropy-Kirchhoff field gradient at the current moment.

[0111] Specifically, based on the current topological index and the Shannon entropy-Kirchhoff field gradient, the global modulus average of the Shannon entropy-Kirchhoff field gradient is extracted as a scalar value , the calculation formula is all grids in the whole domain The arithmetic mean of

[0112] S3.2: Train dynamic weighting coefficients based on historical monitoring data.

[0113] Among them, the weighting coefficient is adaptively adjusted over time; historical monitoring data refers to the topological index sequence, Shannon entropy-Kirchhoff field gradient sequence and actual degradation state record of the electrode generated by the composite electrode during operation, which are collected and stored over a long period of time.

[0114] Specifically, based on historical monitoring data and scalar values , for each historical monitoring period, calculate the weighted fusion value (i.e. composite risk factor ), the expression is:

[0115] ;

[0116] Where, represents the composite risk factor (dimensionless scalar), represents the dynamic weighting coefficient of the topological index, represents the dynamic weighting coefficient of the Shannon entropy-Kirchhoff field gradient;

[0117] It should be noted that the dynamic weighting coefficient of the topological index and the dynamic weighting coefficient of the Shannon entropy-Kirchhoff field gradient It is obtained through training with historical monitoring data and dynamically adjusted based on the actual degradation state records of the composite electrode and the risk factor deviation. The value range is usually limited to to between, The value range of to , and always keep and The sum is equal to 1.

[0118] Count all the cycles without shedding (marked 0) The average value, recorded as ;

[0119] If the active substance loss warning is not triggered, calculate the composite risk factor for the current cycle and The relative deviation is expressed as:

[0120] ;

[0121] Where, Indicates the relative deviation of the current composite risk factor from the historical baseline;

[0122] Adjust the next cycle coefficient according to the deviation, for example: :Maintain the coefficient unchanged; if : Increase , reduce ;like : reduce , Increase ; Coefficient constraint range: .

[0123] S3.3: The topological index and Shannon entropy-Kirchhoff field gradient are enhanced separately through dynamic weighting coefficients to form quantitative weighted indicator components, which are then fused using linear superposition to output a composite risk factor.

[0124] Specifically, the two weighted indicator components are added together to generate a composite risk factor.

[0125] Enter the current topology index and the Shannon entropy-Kirchhoff field gradient norm average, calculated using the weighting coefficients of the current cycle;

[0126] like >first threshold and >Second threshold, outputting active substance shedding warning;

[0127] Otherwise, the output maintains the monitoring state and starts the weighted coefficient update (return to S3.2), and outputs the composite risk factor of the current period. .

[0128] S4: The shedding position of active substances is located based on the spatial distribution of topological indices, and the thermodynamic irreversible source point is traced based on the direction of the Shannon entropy-Kirchhoff field gradient.

[0129] S4.1: Divide the spatial distribution of the topological index into a two-dimensional grid, mark the areas within the grid where the topological index exceeds the local mean, and determine the location of active material shedding;

[0130] Among them, the spatial distribution of the topological index is the numerical distribution of the topological index of the quantum Hall state conversion at different positions on the electrode surface, in the form of a two-dimensional array, and the array elements correspond to the topological index values ​​of the positions on the electrode surface.

[0131] Specifically, a two-dimensional grid is established on the electrode surface area. The two-dimensional grid divides the electrode surface into equal-sized grid cells. The grid cell size is set according to the experimental data resolution to ensure that the entire electrode surface is covered.

[0132] Calculate the average value of the internal topological index for each grid cell. Specifically, traverse all grid cells and perform arithmetic averaging on the topological index values ​​of all points within the grid cell to obtain the average value of the internal topological index of the grid cell.

[0133] Calculate the local mean of each grid cell. Specifically, the local mean is defined as the average of the topological index values ​​of the current grid cell itself and the directly adjacent grid cells. Directly adjacent grid cells include grid cells in the four directions of up, down, left, and right. If there are no adjacent grid cells, that direction is skipped. After the calculation is completed, save the local mean value of each grid cell.

[0134] For each grid cell, compare the internal topological index average value with the local mean; if the internal average value is greater than the local mean, mark the corresponding grid cell as an abnormal area;

[0135] Connect all grid cells marked as abnormal areas and identify the boundaries of the continuously marked areas; the area within the boundary is the location where the active material has fallen off, and the output position coordinates are mapped to the actual physical location on the electrode surface.

[0136] S4.2: Trace the direction vector of the Shannon entropy-Kirchhoff field gradient in reverse, integrate it in the opposite direction of the gradient to the starting point of the gradient, and determine the source of thermodynamic irreversibility.

[0137] Among them, the Shannon entropy-Kirchhoff field gradient direction vector is the spatial directional quantity data obtained by vector synthesis of the spatial change rate of Shannon entropy and the Kirchhoff field gradient component. It is in the form of a vector field, and each position point has a direction vector value.

[0138] Specifically, the gradient starting point is the position of the current gradient point, specifically starting from the center point of the calculated thermodynamic risk region, and the center point is determined based on the position with the highest gradient amplitude.

[0139] Calculate the direction vector value at the starting point of the gradient and obtain the opposite direction of the direction vector; move to the next position in the opposite direction; repeat this calculation and movement process, calculate the direction vector at the new position and obtain the opposite direction to continue moving; set the integration step size to the grid cell size of the electrode surface;

[0140] During the path integration process, the gradient amplitude change is continuously monitored; when the gradient amplitude decreases to near zero or changes steadily, the integration is stopped; the final position point is determined to be the thermodynamically irreversible source point, and the output source point coordinates are mapped to the actual physical position on the electrode surface.

[0141] S5: The three-dimensional electrode degradation thermodynamic map is generated by integrating the active material shedding position and the thermodynamic irreversible source point, and the health status level is dynamically output.

[0142] S5.1: Establish criteria for classifying health status.

[0143] Specifically, five health status levels are defined: health status level 1 indicates that the electrode is completely healthy; health status level 2 indicates that the electrode is slightly degraded; health status level 3 indicates that the electrode is moderately degraded; health status level 4 indicates that the electrode is significantly degraded; health status level 5 indicates that the electrode is severely degraded.

[0144] Each health status level is assigned a visual identifier: health status level 1 uses a green identifier; health status level 2 uses a blue identifier; health status level 3 uses a yellow identifier; health status level 4 uses an orange identifier; and health status level 5 uses a red identifier.

[0145] Create text descriptions for each health status level: health status level 1 is described as "no signs of degradation"; health status level 2 is described as "initial degradation stage"; health status level 3 is described as "reversible degradation stage"; health status level 4 is described as "irreversible degradation stage"; health status level 5 is described as "severe degradation state".

[0146] S5.2: Based on the statistical distribution range of the composite risk factors in the historical monitoring data, divide the critical interval boundaries corresponding to each level, compare the current composite risk factors with the critical interval boundaries of each level, and determine the initial health level.

[0147] Specifically, the composite risk factor sequence in the historical monitoring data set is input, wherein the composite risk factor sequence is derived from the composite risk factor outputted historically;

[0148] Sort the historical composite risk factors and determine the minimum value, maximum value and distribution density;

[0149] The composite risk factor value range is equally divided into five intervals, each interval corresponds to a health status level, and the interval boundary value is the critical interval boundary.

[0150] Obtain the composite risk factor of the current monitoring period, compare the current composite risk factor with the critical interval boundary, and determine the interval position of the current composite risk factor in order of numerical value.

[0151] The health status level corresponding to the interval of the current composite risk factor is the initial health level, and the initial health level is output.

[0152] S5.3: Based on the number of active material shedding locations and the thermodynamic intensity of the thermodynamically irreversible source point, correct the initial health level and output the corrected health status level.

[0153] Specifically, the data of the shedding positions of the active substances are input, and the number of the shedding positions of the active substances is counted; the thermodynamic intensity of the thermodynamically irreversible source point (derived from the source point gradient amplitude) is input;

[0154] Set the position quantity correction threshold and thermodynamic intensity correction threshold;

[0155] It should be noted that the method for setting the position quantity correction threshold is: by analyzing the correspondence between the number of active material shedding positions and the actual degree of electrode degradation in historical monitoring data, statistically analyzing the typical position quantity distribution range in different degradation stages, and selecting the position quantity dividing point that can distinguish adjacent health status levels as the position quantity correction threshold.

[0156] The method for setting the thermodynamic intensity correction threshold is: based on the correlation analysis between the gradient amplitude of the thermodynamic irreversible source point in the historical monitoring data and the degree of electrode material damage, by comparing the typical distribution characteristics of the source point gradient amplitude under different health states, the intensity dividing point that can reflect the significant intensification of the thermodynamic irreversible process is determined as the position quantity correction threshold.

[0157] When the number of positions exceeds the position number correction threshold, the initial health level is reduced by one level. When the thermodynamic strength exceeds the thermodynamic strength correction threshold, the current health level is reduced by another level. The two correction results are combined to output the corrected health status level.

[0158] On the digital three-dimensional expression surface of the electrode's physical structure, the electrode surface is rendered according to the visual identifier corresponding to the health status level, and the active material shedding position and thermodynamic irreversible source point area are specially marked in dark red.

[0159] It should be noted that the digital three-dimensional expression surface refers to the conversion of the electrode's physical structure into a virtual three-dimensional model through high-precision modeling technology, and the dynamic mapping of detection data such as micro-area current and potential distribution, and ultimately the use of color gradients (such as health status level color codes) and special marks (such as deep red damage areas) to achieve visual analysis of the electrode degradation process.

[0160] This embodiment also provides a detection system for the state of a composite electrode, including: a quantum mapping module, an entropy field calculation module, a risk warning module, a traceability module and a heat map generation module; the quantum mapping module is used to map the micro-area current signal to the quantum anomalous Hall state and calculate the topological index of the quantum Hall state conversion; the entropy field calculation module is used to obtain the electrode surface potential distribution and calculate the Shannon entropy-Kirchhoff field gradient in combination with the micro-area current value; the risk warning module is used to generate a composite risk factor by dynamically weighted fusion of the topological index and the Shannon entropy-Kirchhoff field gradient, and when the topological index exceeds the first threshold and the Shannon entropy-Kirchhoff field gradient exceeds the second threshold, the active material shedding warning is triggered; otherwise, the current monitoring state is maintained and the dynamic weighting coefficient is continuously updated; the traceability module is used to locate the active material shedding position according to the spatial distribution of the topological index, and at the same time trace the thermodynamically irreversible source point based on the direction of the Shannon entropy-Kirchhoff field gradient; the heat map generation module is used to fuse the active material shedding position and the thermodynamically irreversible source point to generate a three-dimensional electrode degradation heat map and dynamically output the health status level.

[0161] This embodiment also provides a computer device suitable for the case of the method for detecting the state of a composite electrode, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the method for detecting the state of a composite electrode as proposed in the above embodiment.

[0162] The computer device may be a terminal, comprising a processor, memory, a communication interface, a display, and an input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores an operating system and computer programs. The internal memory provides an environment for the operating system and computer programs stored in the non-volatile storage media. The communication interface of the computer device is used to communicate with external terminals via wired or wireless communication. Wireless communication may be achieved via Wi-Fi, a carrier network, NFC (near-field communication), or other technologies. The display of the computer device may be a liquid crystal display or an electronic ink display. The input device may be a touchscreen overlay on the display, buttons, a trackball, or a touchpad on the computer device housing, or an external keyboard, touchpad, or mouse.

[0163] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for detecting the state of a composite electrode as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0164] In summary, the present invention constructs a dynamically weighted composite risk factor by integrating the topological index of quantum Hall state transition and the Shannon entropy-Kirchhoff field gradient, which can simultaneously capture the topological anomaly characteristics of the shedding of electrode active materials and the entropy change gradient characteristics of the thermodynamically irreversible process, thereby realizing the collaborative perception of electrode degradation risks; the weighting coefficient is adaptively adjusted based on historical monitoring data to ensure that the risk factor dynamically adapts to the degradation mode under different working conditions; the dual threshold trigger mechanism (the first threshold is for the topological index, and the second threshold is for the entropy change gradient) significantly improves the accuracy of the active material shedding warning and avoids the misjudgment of a single indicator; the dynamic weighted fusion method of the composite risk factor provides a highly reliable quantitative criterion for the subsequent precise positioning of the shedding position and tracing of the thermodynamically irreversible source point, forming a closed-loop analysis basis from risk warning to root cause diagnosis.

[0165] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for detecting the state of a composite electrode, characterized in that: include, Map the micro-area current signal to the quantum anomalous Hall state and calculate the topological index of the quantum Hall state transition; Obtain the electrode surface potential distribution and calculate the Shannon entropy-Kirchhoff field gradient based on the micro-area current value; A composite risk factor is generated by dynamically weighting the topological index and the Shannon entropy-Kirchhoff field gradient. When the topological index exceeds the first threshold and the Shannon entropy-Kirchhoff field gradient exceeds the second threshold, an active substance shedding warning is triggered. Otherwise, the current monitoring state is maintained and the dynamic weighting coefficient is continuously updated. The shedding position of active substances is located based on the spatial distribution of topological indices, and the thermodynamic irreversible source point is traced based on the direction of the Shannon entropy-Kirchhoff field gradient. The active material shedding location and thermodynamic irreversible source point are integrated to generate a three-dimensional electrode degradation heat map, and the health status level is dynamically output.

2. The method for detecting the state of a composite electrode according to claim 1, wherein: The specific steps of calculating the topological index of the quantum Hall state transition are as follows: The quantum anomalous Hall state mapping is performed on the micro-area current signal to obtain the state density distribution characteristics; Extract the quantized step extreme value points in the state density distribution characteristics and count the number of quantized step extreme value points; Based on the correspondence between the number of quantized step extreme points and the topological invariant of the quantum Hall effect, the topological index of the quantum Hall state transition is calculated.

3. The method for detecting the state of a composite electrode according to claim 1, wherein: The Shannon entropy-Kirchhoff field gradient is calculated by combining the micro-area current value, and the specific steps are as follows: Obtain the spatial gradient vector of the electrode surface potential distribution; The micro-area current value and the spatial gradient vector are normalized, and the Shannon entropy change rate of the local area is calculated based on the normalized micro-area current value; The Shannon entropy-Kirchhoff field gradient component is determined by multiplying the direction of the spatial gradient vector by the rate of change of the Shannon entropy in the local area; The spatial distribution of each Shannon entropy-Kirchhoff field gradient component is integrated to obtain the Shannon entropy-Kirchhoff field gradient.

4. The method for detecting the state of a composite electrode according to claim 1, wherein: The specific steps of generating the composite risk factor are as follows: Get the topological index and Shannon entropy-Kirchhoff field gradient at the current moment; Training dynamic weighting coefficients based on historical monitoring data; The topological index and Shannon entropy-Kirchhoff field gradient are enhanced by dynamic weighting coefficients to form quantitative weighted index components. The quantitative weighted indicator components are fused using linear superposition to output a composite risk factor.

5. The method for detecting the state of a composite electrode according to any one of claims 1 to 4, wherein: The first threshold is the critical point of active substance shedding risk; the second threshold is the critical point of thermodynamic irreversible process risk; Maintaining the current monitoring state and continuously updating the dynamic weighting coefficient means that when the active substance shedding warning is not triggered, the first weighting coefficient and the second weighting coefficient of the next cycle are adjusted according to the deviation of the current composite risk factor from the historical average value.

6. The method for detecting the state of a composite electrode according to claim 5, wherein: The shedding position of the active substance is located according to the spatial distribution of the topological index, and the thermodynamic irreversible source point is traced based on the direction of the Shannon entropy-Kirchhoff field gradient. The specific steps are as follows: The spatial distribution of the topological index is divided into two-dimensional grids, and the areas in the grid where the topological index exceeds the local mean are marked to determine the location where the active material falls off; The direction vector of the Shannon entropy-Kirchhoff field gradient is traced in reverse, integrated in the opposite direction of the gradient to the starting point of the gradient, and the thermodynamic irreversible source point is determined.

7. The method for detecting the state of a composite electrode according to claim 1, wherein: The specific steps of dynamically outputting the health status level are as follows: Establish health status classification standards; Based on the statistical distribution range of the composite risk factors in historical monitoring data, the critical interval boundaries corresponding to each level are divided, and the current composite risk factors are compared with the critical interval boundaries of each level to determine the initial health level; The initial health level is corrected based on the number of active substance shedding locations and the thermodynamic intensity of the thermodynamically irreversible source point, and the corrected health status level is output.

8. A composite electrode status detection system based on the composite electrode status detection method according to any one of claims 1 to 7, characterized in that: Including quantum mapping module, entropy field calculation module, risk warning module, traceability and positioning module and heat map generation module; The quantum mapping module is used to map the micro-area current signal to the quantum anomalous Hall state and calculate the topological index of the quantum Hall state conversion; The entropy field calculation module is used to obtain the electrode surface potential distribution and calculate the Shannon entropy-Kirchhoff field gradient based on the micro-area current value; The risk warning module is used to generate a composite risk factor by dynamically weighting the topological index and the Shannon entropy-Kirchhoff field gradient, and trigger an active substance shedding warning when the topological index exceeds a first threshold and the Shannon entropy-Kirchhoff field gradient exceeds a second threshold; Otherwise, maintain the current monitoring state and continuously update the dynamic weighting coefficient; The traceability and positioning module is used to locate the shedding position of the active substance based on the spatial distribution of the topological index, and to trace the thermodynamic irreversible source point based on the Shannon entropy-Kirchhoff field gradient direction; The heat map generation module is used to fuse the active material shedding position and the thermodynamic irreversible source point to generate a three-dimensional electrode degradation heat map and dynamically output the health status level.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for detecting the state of the composite electrode according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for detecting the state of a composite electrode according to any one of claims 1 to 7 are implemented.

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