Method and system for predicting the lifetime of oxygen-evolving titanium-based anodes

Abstract

The application relates to the technical field of electrochemistry, in particular to a life prediction method and system for an oxygen evolution type titanium-based anode. The method comprises the following steps: modeling an initial surface element distribution of the oxygen evolution type titanium-based anode to generate an initial element concentration matrix; collecting element change distribution data of the oxygen evolution type titanium-based anode when the oxygen evolution type titanium-based anode is put into electrolysis operation to generate an element migration index; generating a migration behavior evolution model and a migration hotspot analysis model to generate a fusion prediction model; generating a predicted critical life according to the fusion prediction model and a preset pre-failure threshold, and generating an anode life warning signal according to the predicted critical life. The application can improve the timeliness and spatial accuracy of anode life prediction, has significant advantages in capturing non-uniform degradation and early local failure, overcomes the limitations of existing methods which rely on a macroscopic single index and ignore microscopic evolution consistency, and has good engineering adaptability and promotion prospects.

Description

Technical Field

[0001] This application relates to the field of electrochemical technology, and in particular to a method and system for predicting the lifetime of an oxygen-evolving titanium-based anode. Background Technology

[0002] Oxygen evolution type titanium-based anode is a high-performance electrode specifically designed to promote the oxygen evolution reaction (OER) during electrolysis. It is widely used in industrial applications such as electrochemical oxidation, water treatment, and electroplating. Its core characteristic is that the "oxygen evolution reaction" mainly occurs in the anode reaction. During electrolysis, oxygen is released from the anode surface. Its service life has a direct impact on the operational stability and economy of the electrolysis system.

[0003] In existing technologies, lifetime prediction for current oxygen-evolving titanium-based anodes is crucial. Predicting the lifetime of these anodes allows for early replacement or maintenance, preventing equipment downtime and product quality fluctuations. Conventional lifetime prediction methods often rely on single-parameter estimations such as macroscopic voltage drift, single-point current density changes, or total electrolysis time statistics. These methods lack a systematic analysis of the microscopic evolution process of the anode material and cannot effectively address the nonlinear degradation behavior caused by coating peeling, element migration, and local structural instability during oxygen evolution.

[0004] In addition, some studies have attempted to introduce electrochemical models or multiphysics simulations to estimate lifetime, but they generally suffer from two core shortcomings: first, they fail to fully utilize the elemental concentration matrix information in the initial state of anode manufacturing, resulting in a lack of structural correlation in the model; second, they cannot identify and track the consistency of evolution paths between local regions on the anode surface, making it difficult to capture local premature aging or abnormal migration regions in a timely manner, resulting in delayed warnings or high false alarm rates, which limits the engineering adaptability of the model.

[0005] Therefore, there is an urgent need for a method and system for predicting the lifetime of oxygen-evolving titanium-based anodes. Summary of the Invention

[0006] Based on this, it is necessary to provide a method and system for predicting the lifetime of oxygen-evolving titanium-based anodes by fully utilizing the element concentration matrix, combining a migration behavior evolution model to dynamically characterize the migration direction of elements in different regions, identifying migration hotspot regions on the anode surface through a migration hotspot analysis model, and finally inferring and predicting critical lifetimes and triggering regional early warning mechanisms by analyzing the evolution trend of indicators. This achieves cross-scale dynamic prediction of micro-region behavior to global failure.

[0007] The technical solution of this invention is as follows:

[0008] A method for predicting the lifetime of an oxygen-evolving titanium-based anode, the method comprising:

[0009] Modeling of the initial surface elemental distribution of an oxygen-evolving titanium-based anode was performed to generate an initial elemental concentration matrix.

[0010] Collect elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation, and generate an elemental migration index based on the elemental variation distribution data;

[0011] A migration behavior evolution model and a migration hotspot analysis model are generated based on the element migration index, and a fusion prediction model is generated based on the migration behavior evolution model and the migration hotspot analysis model.

[0012] Based on the fusion prediction model and the preset pre-failure threshold, a predicted critical lifetime is generated, and an anode lifetime warning signal is generated based on the predicted critical lifetime.

[0013] Specifically, the element change distribution data includes multiple new element distribution matrices;

[0014] The elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation are collected, and an elemental migration index is generated based on the elemental variation distribution data, including:

[0015] After the anode is put into electrolytic operation, the surface elemental composition of the oxygen-evolving titanium-based anode is sampled and detected based on a preset working time, and a new elemental distribution matrix is ​​obtained. Each sampling and detection corresponds to a new elemental distribution matrix.

[0016] The differences between the distribution matrices of each new element and the initial element concentration matrix are compared, and an element migration index is generated.

[0017] Specifically, the distribution matrices of each new element are compared with the initial element concentration matrix to generate an element migration index, including:

[0018] The differences between the distribution matrices of each new element and the initial element concentration matrix are compared to obtain the concentration change of each micro-region.

[0019] Construct an element migration trajectory matrix based on the concentration changes described above;

[0020] Element migration index is generated based on the element migration trajectory matrix.

[0021] Specifically, a migration behavior evolution model and a migration hotspot analysis model are generated based on the element migration index, and a fusion prediction model is generated based on the migration behavior evolution model and the migration hotspot analysis model, including:

[0022] A migration behavior evolution model is generated based on the element migration index;

[0023] A migration hotspot analysis model is generated based on the new element distribution matrix in the element change distribution data.

[0024] A fusion prediction model is generated based on the migration behavior evolution model and the migration hotspot analysis model.

[0025] Specifically, the initial surface elemental distribution of the oxygen-evolving titanium-based anode is modeled to generate an initial elemental concentration matrix, including:

[0026] The oxygen-evolving titanium-based anode is scanned using a pre-set elemental scanning device to obtain the effective working surface of the anode;

[0027] The effective working surface of the anode is divided into grids, and multiple grid cells are obtained;

[0028] Element concentration detection is performed on each of the grid cells, and element concentration data is obtained;

[0029] Based on the element concentration data, the initial surface element distribution of the anode is modeled to generate an initial element concentration matrix.

[0030] Specifically, a predicted critical lifetime is generated based on the fusion prediction model and a preset pre-failure threshold, and an anode lifetime early warning signal is generated based on the predicted critical lifetime, including:

[0031] The critical lifetime time point is generated based on the prediction output of the fusion prediction model and the preset pre-failure threshold.

[0032] A predicted critical lifetime is generated based on the current time and the critical lifetime time point.

[0033] An anode life warning signal is generated based on the predicted critical life.

[0034] Specifically, generating an anode lifetime early warning signal based on the predicted critical lifetime includes:

[0035] The lifetime warning intensity is generated based on the predicted critical lifetime.

[0036] An anode life warning signal is generated based on the life warning intensity.

[0037] Specifically, a lifetime prediction system for an oxygen-evolving titanium-based anode is also provided, the system comprising:

[0038] The initial element matrix generation module is used to model the initial surface element distribution of oxygen-evolving titanium-based anodes and generate an initial element concentration matrix.

[0039] The element migration index generation module is used to collect the elemental variation distribution data of the oxygen evolution type titanium-based anode during electrolytic operation, and generate the elemental migration index based on the elemental variation distribution data.

[0040] The fusion prediction model generation module is used to generate a migration behavior evolution model and a migration hotspot analysis model based on the element migration index, and to generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model.

[0041] The lifetime warning signal generation module is used to generate a predicted critical lifetime based on the fusion prediction model and a preset pre-failure threshold, and to generate an anode lifetime warning signal based on the predicted critical lifetime.

[0042] Specifically, the elemental variation distribution data includes multiple new elemental distribution matrices, and the elemental migration index generation module is further used to: after the anode is put into electrolytic operation, to sample and detect the surface elemental composition of the oxygen-evolving titanium-based anode based on a preset working time, and to obtain new elemental distribution matrices, wherein one sampling detection corresponds to one new elemental distribution matrix; to compare the differences between each new elemental distribution matrix and the initial elemental concentration matrix, and to generate an elemental migration index.

[0043] Specifically, the element migration index generation module is further configured to: compare the differences between each of the new element distribution matrices and the initial element concentration matrix to obtain the concentration change of each micro-region element concentration; construct an element migration trajectory matrix based on each concentration change; and generate an element migration index based on the element migration trajectory matrix.

[0044] Specifically, the fusion prediction model generation module is further configured to: generate a migration behavior evolution model based on the element migration index; generate a migration hotspot analysis model based on the new element distribution matrix in the element change distribution data; and generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model.

[0045] Specifically, the initial element matrix generation module is further configured to: scan the oxygen-evolving titanium-based anode using a preset element scanning device and obtain the effective working surface of the anode; divide the effective working surface of the anode into grids and obtain multiple grid cells; detect the element concentration of each grid cell and obtain element concentration data; and model the element distribution of the initial surface of the anode based on the element concentration data to generate an initial element concentration matrix.

[0046] Specifically, the lifetime warning signal generation module is further configured to: generate a critical lifetime time point based on the prediction output result of the fusion prediction model and a preset pre-failure threshold; generate a predicted critical lifetime based on the current time and the critical lifetime time point; and generate an anode lifetime warning signal based on the predicted critical lifetime.

[0047] Specifically, the lifetime warning signal generation module is further configured to: generate a lifetime warning intensity based on the predicted critical lifetime; and generate an anode lifetime warning signal based on the lifetime warning intensity.

[0048] Optionally, a computer device is also provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps described in the above-described method for predicting the lifetime of oxygen-evolving titanium-based anodes.

[0049] Optionally, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for predicting the lifetime of an oxygen-evolving titanium-based anode.

[0050] This invention provides a method and system for predicting the lifetime of an oxygen-evolving titanium-based anode, which relates to smart sensor technology, and its technical advantages are as follows:

[0051] The aforementioned method and system for predicting the lifetime of oxygen-evolving titanium-based anodes sequentially involves: modeling the initial surface elemental distribution of the oxygen-evolving titanium-based anode to generate an initial elemental concentration matrix; collecting elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation and generating an elemental migration index based on the elemental variation distribution data; generating a migration behavior evolution model and a migration hotspot analysis model based on the elemental migration index, and generating a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model; generating a predicted critical lifetime based on the fusion prediction model and a preset pre-failure threshold, and generating an anode lifetime early warning signal based on the predicted critical lifetime. Therefore, this application makes full use of the element concentration matrix and combines it with the migration behavior evolution model to dynamically characterize the migration direction of elements in different regions. At the same time, it identifies the migration hotspot regions on the anode surface through the migration hotspot analysis model. Finally, by analyzing the evolution trend of the indicators, it infers and predicts the critical lifetime and triggers a regional early warning mechanism, realizing cross-scale dynamic prediction from micro-area behavior to global failure. This improves the timeliness and spatial accuracy of anode lifetime prediction, especially in capturing non-uniform degradation and early local failure. It overcomes the limitations of existing methods that rely on macroscopic single indicators and ignore the consistency of micro-evolution, and has good engineering adaptability and promotion prospects. Attached Figure Description

[0052] Figure 1 This is a flowchart illustrating a method for predicting the lifetime of an oxygen-evolving titanium-based anode in one embodiment.

[0053] Figure 2 This is a structural block diagram of a lifetime prediction system for an oxygen-evolving titanium-based anode in one embodiment. Detailed Implementation

[0054] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0055] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0056] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0057] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0058] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0059] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0060] In one embodiment, a terminal is provided, the terminal being used for: step S100: modeling the initial surface elemental distribution of an oxygen-evolving titanium-based anode to generate an initial elemental concentration matrix; collecting elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation, generating an elemental migration index based on the elemental variation distribution data; generating a migration behavior evolution model and a migration hotspot analysis model based on the elemental migration index, and generating a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model; generating a predicted critical lifetime based on the fusion prediction model and a preset pre-failure threshold, and generating an anode lifetime early warning signal based on the predicted critical lifetime.

[0061] The terminal may be, but is not limited to, various personal computers, laptops, smartphones, tablets, and portable wearable devices.

[0062] In one embodiment, such as Figure 1 As shown, a method for predicting the lifetime of an oxygen-evolving titanium-based anode is provided, the method comprising:

[0063] Step S100: Model the initial surface elemental distribution of the oxygen-evolving titanium-based anode to generate an initial elemental concentration matrix;

[0064] In this step, after the anode manufacturing is completed, a high-precision energy dispersive spectroscopy (EDS) analyzer is used to measure the elemental composition of the oxygen-evolving titanium-based anode surface, recording the initial elemental distribution characteristics. The measurement covers the entire working surface of the anode and is refined to the micro-scale, for example, using a millimeter-level grid. Simultaneously, a distribution uniformity evaluation index is introduced to quantify the spatial consistency of the initial elemental distribution, outputting the initial elemental concentration matrix, denoted as... This is used for subsequent comparison of changes on the anode surface, and is an initial elemental concentration matrix. Each cell records the element concentration data for the corresponding micro-region. Accuracy directly impacts the reliability of subsequent lifetime inference; therefore, it is essential to ensure that the initial modeling possesses high spatial resolution and high element detection accuracy.

[0065] Step S200: Collect elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation, and generate an elemental migration index based on the elemental variation distribution data;

[0066] Step S300: Generate a migration behavior evolution model and a migration hotspot analysis model based on the element migration index, and generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model;

[0067] Step S400: Generate a predicted critical lifetime based on the fusion prediction model and the preset pre-failure threshold, and generate an anode lifetime warning signal based on the predicted critical lifetime.

[0068] The method and system for predicting the lifetime of oxygen-evolving titanium-based anodes described in this application sequentially perform the following steps: Model the initial surface elemental distribution of the oxygen-evolving titanium-based anode to generate an initial elemental concentration matrix; collect elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation, and generate an elemental migration index based on the elemental variation distribution data; generate a migration behavior evolution model and a migration hotspot analysis model based on the elemental migration index, and generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model; generate a predicted critical lifetime based on the fusion prediction model and a preset pre-failure threshold, and generate an anode lifetime early warning signal based on the predicted critical lifetime. Therefore, this application makes full use of the element concentration matrix and combines it with the migration behavior evolution model to dynamically characterize the migration direction of elements in different regions. At the same time, it identifies the migration hotspot regions on the anode surface through the migration hotspot analysis model. Finally, by analyzing the evolution trend of the indicators, it infers and predicts the critical lifetime and triggers a regional early warning mechanism, realizing cross-scale dynamic prediction from micro-area behavior to global failure. This improves the timeliness and spatial accuracy of anode lifetime prediction, especially in capturing non-uniform degradation and early local failure. It overcomes the limitations of existing methods that rely on macroscopic single indicators and ignore the consistency of micro-evolution, and has good engineering adaptability and promotion prospects.

[0069] In one embodiment, step S100: Modeling the initial surface elemental distribution of the oxygen-evolving titanium-based anode to generate an initial elemental concentration matrix includes:

[0070] Step S110: Scan the oxygen-evolving titanium-based anode using a preset elemental scanning device and obtain the effective working surface of the anode;

[0071] Step S120: Divide the surface of the anode into a grid and obtain multiple grid cells;

[0072] Step S130: Perform element concentration detection on each of the grid cells and obtain element concentration data;

[0073] Step S140: Model the initial surface element distribution of the anode based on the element concentration data to generate an initial element concentration matrix.

[0074] In this embodiment, the element scanning equipment needs to meet certain conditions, specifically as follows: a high-precision scanning electron microscope-energy dispersive spectroscopy (SEM-EDS) system is used, providing a spatial resolution better than 1 micrometer and an element detection limit lower than 0.1 wt%. The measurement environment also has requirements: it must be performed in a low-vacuum environment, maintaining the sample temperature below 50°C to avoid element migration due to local overheating. The main elements detected include, but are not limited to, iridium (Ir), ruthenium (Ru), tin (Sn), tantalum (Ta), and titanium (Ti), and each element's local concentration value must be recorded independently. Next, the measurement grid is divided and the sampling points are arranged. Specifically, the effective working surface of the anode is divided into a regular square grid, with the side length of each grid cell set to 0.5 mm. Sampling points are then arranged, specifically, element concentration detection is performed at the center of each grid cell, and each sampling point corresponds to a unique spatial coordinate. The total number of grids is determined based on the anode size, ensuring full surface coverage without any missed areas. For example, if the working surface of the anode is 100 mm × 100 mm, it is divided into 40,000 grid cells, and each cell generates a set of element concentration data.

[0075] Furthermore, all the measured sampling data are arranged in row-major order to construct an initial elemental concentration matrix. , Each cell in the matrix corresponds to a micro-region on the anode surface, i.e., a grid cell, which records the local concentration value of each target element. Each matrix cell stores a vector containing the concentration values ​​of elements such as iridium, ruthenium, tin, tantalum, and titanium, ultimately forming a multidimensional matrix with a stable structure that facilitates subsequent processing.

[0076] Furthermore, the evaluation index for distribution uniformity is set as follows:

[0077] ,

[0078] in, As an index of distribution uniformity, The total number of sampling points. Let K be the detection concentration of a certain element at point k. This represents the average concentration of the element across all sampling points.

[0079] Distribution uniformity index The range is [0,1], and the index The closer it is to 1, the more uniform the initial distribution of elements. If If the value is less than 0.95, those skilled in the art should consider whether to re-fabricate the anode or remodel it.

[0080] In addition, it is important to note that each sampling point measurement should be repeated at least three times and the average value taken to eliminate random errors; standard deviation analysis should be performed on all measurement data, and if the standard deviation of element concentration at a single point exceeds ±3%, that point should be discarded and remeasured; finally, the initial element concentration matrix is ​​obtained. The global measurement error should be controlled within ±2%.

[0081] Furthermore, considering the natural element migration trends within micro-regions, a smoothing process is performed using two-dimensional high-order interpolation methods (such as bicubic interpolation) to generate a continuous element concentration field. Unlike traditional methods that only establish overall indicators at the macroscopic level of the anode, this step pioneers a concentration matrix modeling method at the micro-region scale, enabling the prediction system to perceive microscopic differences and providing an original reference surface for subsequent migration directions.

[0082] In one embodiment, the elemental variation distribution data includes multiple new elemental distribution matrices. Step S200: Collect the elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation, and generate an elemental migration index based on the elemental variation distribution data, including:

[0083] Step S210: After the anode is put into electrolysis operation, the surface elemental composition of the oxygen-evolving titanium-based anode is sampled and detected based on a preset working time, and a new elemental distribution matrix is ​​obtained, wherein one sampling detection corresponds to one new elemental distribution matrix;

[0084] Step S220: Compare the differences between the distribution matrices of each new element and the initial element concentration matrix, and generate element migration indices.

[0085] In this embodiment, based on the initial element concentration matrix, data on elemental changes during the anode service period are collected and trajectory is constructed, and an element migration index is introduced to quantify the degree of element migration. Here, the anode service period refers to the period during which the oxygen-evolving titanium-based anode is put into electrolytic operation.

[0086] Specifically, after the oxygen-evolving titanium-based anode is put into electrolytic operation, surface elemental composition is sampled and detected periodically according to a preset working time or charge throughput interval; after each detection, a new elemental distribution matrix is ​​obtained, denoted as the new elemental distribution matrix. , where i is the batch number being detected. Then, the distribution matrices of each new element are compared with the initial element concentration matrix to generate an element migration index.

[0087] In one embodiment, step S220: comparing the differences between each of the new element distribution matrices and the initial element concentration matrix, and generating an element migration index, includes:

[0088] Step S221: Compare the differences between the new element distribution matrices and the initial element concentration matrix to obtain the concentration change of each micro-region.

[0089] Step S222: Construct an element migration trajectory matrix based on the concentration changes described above;

[0090] Step S223: Generate element migration index based on the element migration trajectory matrix.

[0091] In this embodiment, the distribution matrix of each new element is... With the initial element concentration matrix A point-by-point difference comparison is performed to extract the change in element concentration in each micro-region, thereby constructing an element migration trajectory matrix to record the directionality of element migration, such as inward migration or outward loss. Finally, an element migration index is introduced to quantify the degree of element migration, which involves comparing the differences between each new element distribution matrix and the initial element concentration matrix to obtain the concentration change of each micro-region; constructing an element migration trajectory matrix based on each concentration change; and generating an element migration index based on the element migration trajectory matrix.

[0092] The specific model for the element migration index is as follows:

[0093] ,

[0094] in, Let be the element migration index in the i-th detection. This represents the change in element concentration at point (x, y) during the i-th detection. Let N be the gradient weights of the initial element distribution corresponding to point (x,y), N be the total number of sampling points in the detection region, and A be the set of all sampling points in the detection region.

[0095] Specifically, gradient weight is an important parameter used to measure the concentration distribution trend of an element at a certain location in its initial state. By calculating the difference in element concentration at multiple points around that location, it reflects whether that point is at a "driving site" or "stable site" for element migration at the initial moment. Those skilled in the art can determine this based on the rate of change of the initial element concentration in space; a larger value indicates a more significant concentration gradient at that point in its initial state, and it is more likely to be a critical path node for later migration. Preferably, the gradient weight is typically calculated using the central difference method to estimate the local gradient of the initial concentration distribution.

[0096] Compared to existing technologies that often only monitor single-point voltage or average concentration changes, thus neglecting the directional migration behavior of elements, this application constructs a spatial field of migration behavior through a model of the element migration index, making subsequent trend division more physically supported.

[0097] The element migration index model is used to integrate element concentration changes and initial distribution gradients to reflect the weighted intensity of element migration on the anode surface, serving as a basis for identifying subsequent evolution stages. Not only that... It also incorporates the gradient weights of each point in the original distribution, that is, it considers which regions are more likely to migrate, thus making the assessment of migration amount more physically reasonable. Ultimately, it serves as a comprehensive indicator of the current migration activity of the entire anode and is a quantitative basis for determining the stage of lifetime evolution.

[0098] Because different elements contribute differently to lifetime and exhibit different degradation mechanisms, it is necessary to detect multiple elements in order to capture the evolutionary differences of these elements in the anode material. The data for these elements will be independently modeled in subsequent steps and then weighted and integrated.

[0099] Therefore, in this application, multiple elements are first detected, and then a migration behavior evolution model and a migration hotspot analysis model are constructed for each element. The final fusion model will integrate the migration indicators of each element to form an overall prediction.

[0100] Finally, the final lifetime prediction analysis integrates the migration behavior results of all detected elements, taking into account the weight and coupling relationship of each element in lifetime evolution, so as to more comprehensively and accurately reflect the degradation state of the anode.

[0101] In one embodiment, step S300: generating a migration behavior evolution model and a migration hotspot analysis model based on the element migration index, and generating a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model, including:

[0102] Step S310: Generate a migration behavior evolution model based on the element migration index;

[0103] Step S320: Generate a migration hotspot analysis model based on the new element distribution matrix in the element change distribution data;

[0104] Step S330: Generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model.

[0105] In one embodiment, the migration behavior evolution model in step S310 is specifically as follows:

[0106] ,in, Let i be the migration behavior evolution index corresponding to the i-th detection. Let be the element migration index in the i-th detection. This is the migration index at the initial detection stage, which defaults to zero or near zero. Let be the running time from the initial detection to the i-th detection.

[0107] In this embodiment, the migration behavior evolution model is calculated... The evolution rate of element migration behavior in the oxygen-evolving titanium-based anode is defined to aid in the identification and division of lifetime stages, which are divided into three main stages. The first stage is a period of weak element migration, where the migration rate changes slowly. The second stage is a period of accelerated element migration, characterized by the emergence of local migration hotspots. The third stage is a period of uncontrolled element migration, where the overall migration rate increases sharply and its spatial distribution becomes severely uneven.

[0108] According to the migration behavior evolution index The magnitude and trend of changes are used to set segmented thresholds to determine the current lifespan stage of the anode. For example, when... When it is in a low-growth range, it is judged as the first stage. If it rises rapidly, it switches to the second stage. If the increase is rapid and exceeds the set threshold, the system will enter the third stage.

[0109] This embodiment avoids relying on manual experience for classification by using continuous mathematical judgment, improves the objectivity and repeatability of lifespan stage determination, and directly affects the formulation of subsequent remaining lifespan inference strategies.

[0110] Furthermore, after dividing the evolutionary stages, a migration hotspot analysis model is constructed to dynamically capture and track the evolution process of element migration hotspots. In step S320, migration hotspot regions will appear locally during element migration, characterized by an element migration rate that is significantly higher than that of the surrounding regions. To quantify this phenomenon, a migration hotspot analysis model is constructed, as shown below:

[0111]

[0112] in, Let represent the hotspot clustering degree in the i-th detection. Let be the set of hotspot regions identified in the i-th detection. The set of all regions within the detection range. This represents the average value of the elemental concentration variation across the entire region.

[0113] The hotspot concentration It reflects the degree of abnormal concentration of element migration in hotspot areas, when When the threshold is exceeded, the anode is considered to be at risk of localized accelerated failure. Compared to existing lifetime models that largely ignore spatial non-uniformity and lack early detection of sudden localized degradation, resulting in low warning sensitivity, this embodiment introduces... The indicators enable the prediction system to identify anomalies, thereby enhancing the system's early warning sensitivity and local sensitivity.

[0114] Furthermore, hotspots are typically identified based on changes in elemental concentration. Whether it exceeds a certain threshold. Those skilled in the art typically set a dynamic threshold based on the overall concentration change mean and standard deviation; when a certain position... When the value exceeds this threshold, for example, when it exceeds the mean plus twice the standard deviation, the location can be included in the hot spot region set. This can reflect areas with abnormally active migration behavior in real time and locally.

[0115] In one embodiment, in step S330, the fusion prediction model is specifically as follows:

[0116] ,

[0117] in, As a comprehensive forecasting indicator, As a weighting factor for the migration behavior evolution index, This is a weighting factor for the degree of hotspot clustering.

[0118] Specifically, the weighting factor for low migration behavior evolution index. Weighting factors for hotspot clustering By fitting and optimizing historical data, a dynamic balance between overall and local information is achieved. The comprehensive prediction index... Ultimately, it serves as a core comprehensive indicator, which is used throughout subsequent lifespan prediction and early warning triggering.

[0119] The fusion prediction model integrates two types of failure drivers at different scales. Based on migration behavior evolution indicators, it focuses on the average degradation trend in lifetime prediction, and can also reflect the risk of localized sudden degradation based on hotspot clustering. Compared with traditional methods that rely solely on average electrochemical performance parameters for lifetime estimation, this embodiment provides a dual-scale integrated approach, which can maintain prediction stability and response agility, especially when non-uniform degradation occurs at the anode.

[0120] In one embodiment, step S400: generating a predicted critical lifetime based on the fusion prediction model and a preset pre-failure threshold, and generating an anode lifetime warning signal based on the predicted critical lifetime, including:

[0121] Step S410: Generate the critical lifetime time point based on the prediction output of the fusion prediction model and the preset pre-failure threshold;

[0122] Step S420: Generate a predicted critical lifetime based on the current time and the critical lifetime time point;

[0123] Step S430: Generate an anode lifetime warning signal based on the predicted critical lifetime.

[0124] In this embodiment, a critical lifetime time point is generated based on the predicted output results of the fusion prediction model and a preset pre-failure threshold; a predicted critical lifetime is generated based on the current time and the critical lifetime time point; and an anode lifetime early warning signal is generated based on the predicted critical lifetime. This realizes an early warning mechanism driven by the model evolution trend, which has the technical characteristics of being able to anticipate, be graded, and be adjustable, and achieves a technical closed loop from data analysis to risk control.

[0125] Furthermore, the comprehensive prediction index is first obtained based on the continuous output of the fusion prediction model. Data points are used to predict the comprehensive predictive index using sliding window regression analysis. The evolution trend over a future period, and then a pre-failure threshold is preset. When the comprehensive prediction index Expected in the future The pre-failure threshold is reached or exceeded for the first time within a certain time period. At that time, it is determined that the anode is about to enter the irreversible failure range.

[0126] Specifically, the pre-failure threshold It is not a fixed global constant, but a dynamically set adjustable parameter. In practical applications, to adapt to different operating environments and changes in anode materials, the pre-failure threshold is... The pre-failure threshold is periodically updated or reassessed by those skilled in the art based on operational data over a period of time. For example, it can be adjusted by combining the distribution patterns of historical failure samples or the latest observed trends. Therefore, the pre-failure threshold... It has a certain degree of time sensitivity and varies with changes in usage conditions.

[0127] By estimating the comprehensive forecasting index The future will reach the pre-failure threshold. Time point This refers to the critical lifetime time point. The following prediction model is specifically designed to calculate the remaining anode lifetime:

[0128] ,

[0129] in, This is the critical lifetime time point. For the current detection time, Comprehensive forecast indicators for the current moment The rate of change.

[0130] Furthermore, the predicted critical lifetime is the time from the current moment to the critical lifetime point. The time difference.

[0131] Specifically, the comprehensive forecast indicators at the current moment The rate of change is typically based on sampled data from multiple consecutive time points, obtained by dividing the difference in the index between two or more adjacent time points by the time interval. Those skilled in the art can use a sliding window approach to extract historical sequences, ensuring that the trend estimate has temporal continuity and stability.

[0132] In addition, for special cases, specifically the comprehensive prediction index obtained from multiple samplings at the same time, The samples are very close, even identical. This can be understood as multiple samplings within a very short period. Because the indicator changes slowly, the resulting differences may be close to zero or equal, leading to inaccurate or invalid rates of change. Therefore, high-frequency sampling at a single moment is unsuitable for trend estimation. Based on this, in practice, those skilled in the art can use certain time intervals, such as every few hours or daily, to sample and obtain representative historical trajectory points, thereby truly reflecting the trend of the indicator's evolution over time and calculating a more effective rate of change.

[0133] Unlike existing technologies that only determine failure after the fact, this step uses a model evolution trend to drive the early warning mechanism, which has the technical characteristics of being able to anticipate, grade, and adjust, thus realizing a technical closed loop from data analysis to risk control.

[0134] Specifically, step S430: generating an anode lifetime warning signal based on the predicted critical lifetime includes:

[0135] Step S431: Generate a lifetime warning intensity based on the predicted critical lifetime;

[0136] Step S432: Generate an anode life warning signal based on the life warning intensity.

[0137] In this embodiment, different predicted critical lifetimes are pre-set to correspond to different lifetime warning intensities. Therefore, when the predicted critical lifetime is obtained, the lifetime warning intensity corresponding to the current predicted critical lifetime can be obtained according to the pre-set correspondence, and then an anode lifetime warning signal can be generated according to the lifetime warning intensity.

[0138] In general, existing lifetime prediction methods tend to focus on the average failure trend of the entire anode surface or the early warning of global hot zones, while ignoring the early island-like failure phenomenon that may be caused by differences in the evolution process between local areas.

[0139] To overcome this problem, this application constructs an elemental evolution trajectory consistency band to capture the dynamic synergy of multiple regions on the anode surface during the evolution process, identifying the degree of deviation of the evolution trajectory before the emergence of local failure signs. When significant abnormal evolution behavior occurs in a certain region of the oxygen-evolving titanium-based anode, a lifetime warning signal is immediately issued to other regions within the entire consistency band, even if the warning region does not currently show an obvious deterioration trend.

[0140] In step S100, an elemental concentration matrix based on the initial state of anode manufacturing is constructed. This matrix not only preserves the initial concentration differences between regions but also encodes the micro-regional distribution patterns of the anode during the manufacturing process. The initial identification basis for the consistency band comes from the initial isomorphic regions revealed by this matrix—that is, a set of micro-regions with similar elemental composition and manufacturing paths, which naturally exhibit convergent trends in subsequent evolution. In step S200, the directionality of element migration paths in different regions is recorded. One of the spatial delineation criteria for the consistency band is that the migration trajectory angles between regions maintain a relatively stable and consistent pattern throughout the evolutionary cycle, ensuring that the future migration behavior of the selected regions has trend coupling. The migration hotspot regions identified in step S300 are usually the most active segments of the evolutionary trajectory. The construction of the consistency band prioritizes radial trajectory clustering around the migration hotspots, searching for surrounding regions that exhibit similar migration initiation speed and direction changes within the same time scale, thereby establishing a "point-to-band" consistency classification model. The consistency band is defined by examining whether the divided regions exhibit consistent "deterioration stage transition nodes" on the lifetime evolution timeline, such as abrupt transitions from the stable region to the critical region. Only regions with highly synchronized evolution inflection points can be classified into the same consistency band. The introduction of the consistency band significantly improves the spatial sensitivity and proactive response of lifetime prediction, enabling the early identification of localized premature aging points caused by structural micro-defects, surface stress concentration, or uneven micro-area corrosion, thereby improving the actual efficiency of anode maintenance.

[0141] In one embodiment, such as Figure 2 As shown, a lifetime prediction system for an oxygen evolution type titanium-based anode is also provided, the system comprising:

[0142] The initial element matrix generation module is used to model the initial surface element distribution of oxygen-evolving titanium-based anodes and generate an initial element concentration matrix.

[0143] The element migration index generation module is used to collect the elemental variation distribution data of the oxygen evolution type titanium-based anode during electrolytic operation, and generate the elemental migration index based on the elemental variation distribution data.

[0144] The fusion prediction model generation module is used to generate a migration behavior evolution model and a migration hotspot analysis model based on the element migration index, and to generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model.

[0145] The lifetime warning signal generation module is used to generate a predicted critical lifetime based on the fusion prediction model and a preset pre-failure threshold, and to generate an anode lifetime warning signal based on the predicted critical lifetime.

[0146] In another embodiment, the elemental variation distribution data includes multiple new elemental distribution matrices, and the elemental migration index generation module is further configured to: after the anode is put into electrolytic operation, perform surface elemental composition sampling and detection on the oxygen-evolving titanium-based anode based on a preset working time, and obtain new elemental distribution matrices, wherein one sampling and detection corresponds to one new elemental distribution matrix; compare the differences between each new elemental distribution matrix and the initial elemental concentration matrix, and generate an elemental migration index.

[0147] In another embodiment, the element migration index generation module is further configured to: compare the differences between each of the new element distribution matrices and the initial element concentration matrix to obtain the concentration change of element concentration in each micro-region; construct an element migration trajectory matrix based on each concentration change; and generate an element migration index based on the element migration trajectory matrix.

[0148] In another embodiment, the fusion prediction model generation module is further configured to: generate a migration behavior evolution model based on the element migration index; generate a migration hotspot analysis model based on the new element distribution matrix in the element change distribution data; and generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model.

[0149] In another embodiment, the initial element matrix generation module is further configured to: scan an oxygen-evolving titanium-based anode using a preset element scanning device and obtain an effective working surface of the anode; divide the effective working surface of the anode into grids and obtain multiple grid cells; detect the element concentration of each grid cell and obtain element concentration data; and model the element distribution of the initial surface of the anode based on the element concentration data to generate an initial element concentration matrix.

[0150] In another embodiment, the lifetime warning signal generation module is further configured to: generate a critical lifetime time point based on the prediction output result of the fusion prediction model and a preset pre-failure threshold; generate a predicted critical lifetime based on the current time and the critical lifetime time point; and generate an anode lifetime warning signal based on the predicted critical lifetime.

[0151] In another embodiment, the lifetime warning signal generation module is further configured to: generate a lifetime warning intensity based on the predicted critical lifetime; and generate an anode lifetime warning signal based on the lifetime warning intensity.

[0152] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps described in the above-described method for predicting the lifetime of an oxygen-evolving titanium-based anode.

[0153] In one embodiment, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the steps described in the above-described method for predicting the lifetime of an oxygen-evolving titanium-based anode.

[0154] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0155] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0156] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0157] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0158] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0159] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the various method embodiments above.

[0160] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the above-described method embodiments.

[0161] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0162] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0163] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0164] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0165] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

[0167] One embodiment of this application also provides a computer device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above-described methods.

[0168] The computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above description is an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than described above, or a combination of certain components, or different components, such as input / output devices, network access devices, etc.

[0169] The processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0170] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0171] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for predicting the lifetime of an oxygen-evolving titanium-based anode, characterized in that, The method includes: Modeling of the initial surface elemental distribution of an oxygen-evolving titanium-based anode was performed to generate an initial elemental concentration matrix. Collect elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation, and generate an elemental migration index based on the elemental variation distribution data; A migration behavior evolution model and a migration hotspot analysis model are generated based on the element migration index, and a fusion prediction model is generated based on the migration behavior evolution model and the migration hotspot analysis model, including: A migration behavior evolution model is generated based on the element migration index; A migration hotspot analysis model is generated based on the new element distribution matrix in the element change distribution data. A fusion prediction model is generated based on the migration behavior evolution model and the migration hotspot analysis model; Based on the fusion prediction model and the preset pre-failure threshold, a predicted critical lifetime is generated, and an anode lifetime early warning signal is generated based on the predicted critical lifetime. The element change distribution data includes multiple new element distribution matrices; The elemental variation distribution data of the oxygen-evolving titanium-based anode during electrolytic operation are collected, and an elemental migration index is generated based on the elemental variation distribution data, including: After the anode is put into electrolytic operation, the surface elemental composition of the oxygen-evolving titanium-based anode is sampled and detected based on a preset working time, and a new elemental distribution matrix is ​​obtained. Each sampling and detection corresponds to a new elemental distribution matrix. The differences between the distribution matrices of each new element and the initial element concentration matrix are compared, and an element migration index is generated.

2. The method for predicting the lifetime of an oxygen-evolving titanium-based anode according to claim 1, characterized in that, The differences between the distribution matrices of each new element and the initial element concentration matrix are compared, and an element migration index is generated, including: The differences between the distribution matrices of each new element and the initial element concentration matrix are compared to obtain the concentration change of each micro-region. Construct an element migration trajectory matrix based on the concentration changes described above; Element migration index is generated based on the element migration trajectory matrix.

3. The method for predicting the lifetime of an oxygen-evolving titanium-based anode according to claim 1, characterized in that, Modeling the initial surface elemental distribution of the oxygen-evolving titanium-based anode was performed to generate an initial elemental concentration matrix, including: The oxygen-evolving titanium-based anode is scanned using a pre-set elemental scanning device to obtain the effective working surface of the anode; The effective working surface of the anode is divided into grids, and multiple grid cells are obtained; Element concentration detection is performed on each of the grid cells, and element concentration data is obtained; Based on the element concentration data, the initial surface element distribution of the anode is modeled to generate an initial element concentration matrix.

4. The method for predicting the lifetime of an oxygen-evolving titanium-based anode according to claim 1, characterized in that, Based on the fusion prediction model and a preset pre-failure threshold, a predicted critical lifetime is generated, and based on the predicted critical lifetime, an anode lifetime early warning signal is generated, including: The critical lifetime time point is generated based on the prediction output of the fusion prediction model and the preset pre-failure threshold. A predicted critical lifetime is generated based on the current time and the critical lifetime time point; An anode life warning signal is generated based on the predicted critical life.

5. The method for predicting the lifetime of an oxygen-evolving titanium-based anode according to claim 4, characterized in that, Based on the predicted critical lifetime, an anode lifetime early warning signal is generated, including: The lifetime warning intensity is generated based on the predicted critical lifetime. An anode life warning signal is generated based on the life warning intensity.

6. The system used in the lifetime prediction method for oxygen-evolving titanium-based anodes according to claim 1, characterized in that, The system includes: The initial element matrix generation module is used to model the initial surface element distribution of oxygen-evolving titanium-based anodes and generate an initial element concentration matrix. The element migration index generation module is used to collect the elemental variation distribution data of the oxygen evolution type titanium-based anode during electrolytic operation, and generate the elemental migration index based on the elemental variation distribution data. The fusion prediction model generation module is used to generate a migration behavior evolution model and a migration hotspot analysis model based on the element migration index, and to generate a fusion prediction model based on the migration behavior evolution model and the migration hotspot analysis model. The lifetime warning signal generation module is used to generate a predicted critical lifetime based on the fusion prediction model and a preset pre-failure threshold, and to generate an anode lifetime warning signal based on the predicted critical lifetime.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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