A method and system for evaluating cable buffer layer structure features

Abstract

The embodiment of the application provides a kind of cable buffer layer structure feature evaluation method and system, comprising: using QSGS algorithm to construct multiple groups of initial buffer layer porous structure model;To multiple groups of initial buffer layer porous structure model secondary optimization, obtain multiple groups of target buffer layer porous structure model;Using finite element method, on multiple groups of target buffer layer porous structure model, under operating condition, partial pressure is applied, and based on electrothermal coupling model, the electric field intensity in buffer layer, current density is calculated;Maximum electric field intensity and maximum current density are obtained;Based on feasible region discrimination rule, feasibility analysis is carried out, and the ablation risk assessment result of buffer layer is obtained.The method can accurately analyze whether buffer layer will have ablation risk, provide theoretical basis and method support for buffer layer structure optimization design and cable engineering selection.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to a method and system for evaluating the structural characteristics of a cable buffer layer in the field of data processing technology. Background Technology

[0002] In related technologies, frequent localized ablation of high-voltage buffer layers has become a significant hidden danger affecting the safe and stable operation of cables. To address this issue, various improvement and repair solutions have been proposed, including buffer layer repair fluids and repair gas treatment technologies. Simultaneously, some research has attempted to improve local current distribution and heat accumulation characteristics by altering the sheath structure (such as replacing traditional corrugated aluminum sheaths with flat aluminum sheaths). However, all of the above methods have limitations in their applicability to varying degrees: on the one hand, repair technologies primarily target localized defects in already operational cables, making it difficult to fundamentally change the structural failure mechanism; on the other hand, structural replacement solutions exhibit varying degrees of adaptability under different laying environments and mechanical constraints, with corrugated aluminum sheaths still showing superior overall performance under certain operating conditions. Therefore, existing solutions cannot fully meet the needs of buffer layer structure optimization and selection under complex engineering conditions. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for evaluating the structural characteristics of cable buffer layers, and the specific technical solution adopted is as follows: In a first aspect, embodiments of the present invention provide a method for evaluating the structural features of a cable buffer layer, the method comprising: The Latin hypercube sampling method was used to sample multiple sets of design parameter combinations by taking the water-blocking powder porosity, fiber porosity, water-blocking powder thickness and fiber thickness of the buffer layer as initial design variables. Each set of design parameters is used as the target constraint parameters. The QSGS algorithm is used to construct multiple initial porous structure models of the buffer layer that satisfy the target constraint parameters and are used to characterize the random porosity of the buffer layer. Multiple initial porous buffer layer models were optimized in a secondary manner to obtain multiple target porous buffer layer models. Using the finite element method, partial pressure under operating conditions is applied to multiple target buffer layer porous structure models, and the electric field intensity and current density inside the buffer layer are calculated based on the electric field control equation and thermal field control equation in the electrothermal coupling model. Among the electric field strength and current density corresponding to multiple sets of target buffer layer porous structure models, the maximum electric field strength and maximum current density are obtained. Using the structural features of each sample as input and the feasibility category as output, a classification regression tree is constructed and the feasible region discrimination rules for low ablation risk of the buffer layer are extracted. Feasibility analysis was conducted on multiple sets of target buffer layer porous structure models based on the feasible region discrimination rule, and the corresponding ablation risk assessment results were obtained. Each sample includes feasible samples and infeasible samples. Among them, the target buffer layer porous structure models with a maximum electric field strength less than the electric field strength threshold and a maximum current density less than the current density threshold are feasible samples, and the remaining target buffer layer porous structure models are infeasible samples. The electric field strength threshold and the current density threshold are the critical electric field strength and critical current density corresponding to the ablation failure of the buffer layer.

[0004] Secondly, a system for evaluating the structural characteristics of a cable buffer layer is provided, the system comprising: The module is used to sample multiple sets of design parameter combinations by using the Latin hypercube sampling method with the water-blocking powder porosity, fiber porosity, water-blocking powder thickness, and fiber thickness of the buffer layer as initial design variables; and each set of design parameter combinations is used as target constraint parameters. The QSGS algorithm is used to construct multiple initial porous structure models of the buffer layer that meet the target constraint parameters and are used to characterize the random porosity characteristics of the buffer layer. The optimization module is used to perform secondary optimization on multiple sets of initial buffer layer porous structure models to obtain multiple sets of target buffer layer porous structure models. The calculation module is used to apply the partial pressure under operating conditions to multiple sets of target buffer layer porous structure models using the finite element method, and calculate the electric field strength and current density inside the buffer layer based on the electric field control equation and thermal field control equation in the electrothermal coupling model. The acquisition module is used to obtain the maximum electric field intensity and the maximum current density among the electric field intensity and current density corresponding to multiple sets of target buffer layer porous structure models. The analysis module is used to construct a classification regression tree and extract feasible domain discrimination rules for low ablation risk of buffer layers, taking the structural characteristics of each sample as input and the feasibility category as output. Based on the feasible domain discrimination rules, it performs feasibility analysis on multiple groups of target buffer layer porous structure models and obtains the corresponding ablation risk assessment results. Each sample includes feasible and infeasible samples. Target buffer layer porous structure models with a maximum electric field strength less than the electric field strength threshold and a maximum current density less than the current density threshold are considered feasible samples, while the remaining target buffer layer porous structure models are considered infeasible samples. The electric field strength threshold and current density threshold are the critical electric field strength and critical current density corresponding to the ablation failure of the buffer layer.

[0005] Thirdly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the method described in the first aspect.

[0006] Fourthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the method described in the first aspect.

[0007] This invention offers the following advantages: It employs the Latin hypercube sampling method QSGS (Quartet Structure Generation Set, a four-parameter random growth method) to sample the water-blocking powder porosity, fiber porosity, water-blocking powder thickness, and fiber thickness of the buffer layer as initial design variables, obtaining multiple sets of design parameter combinations. Each set of design parameter combinations is then used as a target constraint parameter. The QSGS algorithm is used to construct multiple initial porous structure models of the buffer layer that satisfy the target constraint parameters and characterize the random porosity features of the buffer layer. This Latin hypercube sampling method stratifies the value ranges of each parameter and distributes the samples more evenly across the multidimensional parameter space, thus reflecting the influence of different structural feature combinations on the electric field characteristics of the buffer layer with fewer samples. Subsequently, the multiple initial porous structure models of the buffer layer undergo secondary optimization to obtain multiple target porous structure models of the buffer layer, thereby improving the stability of the target porous structure models. Using the finite element method, partial voltages under operating conditions are applied to multiple sets of target porous buffer layer models. Based on the electric field control equations and thermal field control equations in the electrothermal coupling model, the electric field strength and current density inside the buffer layer are calculated. This allows for accurate calculation of the electric field strength and current density inside the buffer layer through the electrothermal coupling model. Finally, based on the electric field strength and current density, the maximum electric field strength and maximum current density are obtained from multiple sets of target porous buffer layer models. Using the structural characteristics of each sample as input and the feasibility category as output, a classification regression tree is constructed, and feasible region discrimination rules for low ablation risk of the buffer layer are extracted. Based on the feasible region discrimination rules, a feasibility analysis is performed on multiple sets of target porous buffer layer models to obtain the ablation risk assessment results. Target porous buffer layer models with a maximum electric field strength less than the electric field strength threshold and a maximum current density less than the current density threshold are considered feasible samples, while the remaining target porous buffer layer models are considered infeasible samples. Thus, by conducting feasibility analysis on multiple sets of target buffer layer porous structure models through feasible region discrimination rules, we can reveal the influence mechanism of pore structure and water-blocking powder distribution on the electrothermal coupling behavior of the buffer layer, determine the range where ablation will not occur, and accurately analyze whether the buffer layer will have ablation risk. This provides theoretical basis and methodological support for the optimized design of buffer layer structure and cable engineering selection. Attached Figure Description

[0008] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 This is a schematic diagram illustrating the implementation process of a cable buffer layer structural feature evaluation method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of another implementation process of a cable buffer layer structural feature evaluation method provided in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the process of generating and optimizing the porous structure model of the target buffer layer provided in this embodiment of the invention; Figure 4 This is a schematic diagram illustrating an application scenario of a cable buffer layer structural feature evaluation method provided in an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating another application scenario of the cable buffer layer structural feature evaluation method provided in this embodiment of the invention; Figure 6 This is a schematic diagram of the composition structure of a cable buffer layer structure feature evaluation system provided in an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0010] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a cable buffer layer structural feature evaluation method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0011] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0012] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0013] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0014] This invention provides a method for evaluating the structural characteristics of cable buffer layers. The method employs the QSGS algorithm to construct a porous structure model of the buffer layer. While maintaining structural consistency, it introduces key parameters such as fiber porosity and the spatial distribution of water-blocking powder (e.g., water-blocking powder porosity and thickness). The system analyzes the multi-physics response characteristics of the buffer layer under different structural characteristics during operation, including electric field strength and current density. Through comparative simulation studies, the method reveals the influence mechanism of structural characteristics on electrothermal coupling behavior, thus providing theoretical basis and methodological support for the optimized design of buffer layer structures and cable engineering selection.

[0015] The specific scheme of the cable buffer layer structural feature evaluation method provided by the present invention is described below with reference to the accompanying drawings. Since localized ablation of high-voltage buffer layers is frequent, it can be solved by replacing the buffer layer. However, there are various types of buffer layer materials, each with its own parameter range that makes it less prone to ablation. Therefore, this embodiment of the invention evaluates buffer layers of various material types to determine the parameter range for each material type. Please refer to... Figure 1 The diagram illustrates a flowchart of an implementation of a cable buffer layer structural feature evaluation method according to an embodiment of the present invention. The method includes: 101. The Latin hypercube sampling method was adopted, and the porosity of the water-blocking powder, the porosity of the fiber, the thickness of the water-blocking powder, and the thickness of the fiber in the buffer layer were used as initial design variables to obtain multiple sets of design parameter combinations. Each set of design parameter combinations was used as the target constraint parameters, and the QSGS algorithm was used to construct multiple initial porous structure models of the buffer layer that meet the target constraint parameters and are used to characterize the random porosity characteristics of the buffer layer.

[0016] Here, the target constraint parameters of the QSGS algorithm may include: water-blocking powder porosity, fiber porosity, water-blocking powder thickness, fiber thickness, fractal dimension, and the set growth kernel distribution probability and growth probability, etc.

[0017] In some possible implementations, firstly, cross-sectional images of the buffer layer in both dry and damp states are acquired using an X-ray micro-CT scanning system; secondly, the cross-sectional images are segmented and identified into water-blocking powder and fiber regions; thirdly, the porosity of the water-blocking powder, the porosity of the fiber, the thickness of the water-blocking powder, the thickness of the fiber, and the fractal dimension are extracted from the identified water-blocking powder and fiber regions; finally, the porosity of the water-blocking powder, the porosity of the fiber, the thickness of the water-blocking powder, the thickness of the fiber, the fractal dimension, as well as the set growth kernel distribution probability and growth probability, are used as target constraint parameters for QSGS algorithm modeling. This enriches the target constraint parameters for QSGS algorithm modeling, thereby improving the accuracy of modeling.

[0018] After determining the target constraint parameters, sampling was carried out using the Latin hypercube sampling method. The porosity of the water-blocking powder, the porosity of the fiber, the thickness of the water-blocking powder, and the thickness of the fiber were used as initial design variables. Forty representative parameter combinations were selected, and multiple initial porous structure models of the buffer layer were generated accordingly.

[0019] In some possible implementations, the initial porous structure model of the buffer layer can be constructed through the following process: The first step is to establish a computational domain on a preset grid to determine the model size.

[0020] The second step is to randomly distribute fiber growth nuclei within the computational domain based on the growth nuclei distribution probability, and the growth nuclei distribution probability is less than or equal to the difference between 1 and the fiber porosity.

[0021] The third step involves randomly expanding the fiber growth nucleus into the neighborhood of the selected fiber growth nucleus according to a set growth probability, thus forming a fiber skeleton structure.

[0022] The fourth step involves introducing a water-blocking powder functional layer into a localized area of ​​the buffer layer within the fiber structure skeleton, and reassigning the local fiber porosity in that area to generate a fiber-water-blocking powder hybrid structure, thereby simulating the filling characteristics of water-blocking powder particles.

[0023] The fifth step involves repeating the fiber skeleton structure formation process and simulating the filling characteristics of water-blocking powder particles, while calculating the current fiber porosity and fractal dimension in real time. The iteration stops once both the current fiber porosity and fractal dimension reach the set targets, resulting in an initial porous buffer layer structure model that meets the statistical requirements. The set targets are the predetermined fiber porosity threshold and fractal dimension threshold.

[0024] In the first to fifth steps above, (1) a computational domain is established on an N×M×L grid, where N, M, and L are the model dimensions. (2) Initial fiber growth nuclei are set: based on the growth nuclei distribution probability. Fiber growth nuclei are randomly distributed within the computational domain, and satisfy the following conditions: . (3) Select fiber growth nuclei, and grow them in a set direction with a certain probability. Randomly expand towards the neighborhood to gradually form a fiber skeleton structure. (4) Generate water-blocking powder region: Based on the generation of fiber skeleton, introduce water-blocking powder functional layer in local area of ​​buffer layer, and generate fiber water-blocking powder hybrid structure by reassigning local porosity to simulate the filling characteristics of water-blocking powder particles. (5) Implement iterative modeling: Repeat steps (3) and (4), and calculate the current fiber porosity and fractal dimension in real time; stop iteration when both reach the set target to obtain the initial porous structure model of buffer layer that meets the statistical characteristics requirements.

[0025] In some possible implementations, the fractal dimension of the current model is calculated in real time during the construction of the initial porous structure model of the buffer layer; wherein, the fractal dimension of the current model... , For box counting, the box dimensions are... The number of non-empty boxes required to cover the structure is determined; then, the fractal dimension of the current model is compared with the target fractal dimension. If the fractal dimension of the current model is less than the target fractal dimension, the growth probability is increased to enhance connectivity; if the fractal dimension of the current model is greater than the target fractal dimension, the growth probability is decreased to suppress overgrowth.

[0026] Here, the target fractal dimension can be obtained from the fractal dimension in the interface image. A three-dimensional porous structure model of the buffer layer is constructed based on the QSGS algorithm. The spatial distribution characteristics of fibers, pores, and functional fillers are simulated through a random growth mechanism, and the structure is controlled by combining porosity and fractal dimension. Furthermore, morphological optimization and downsampling are employed to improve model connectivity and computational efficiency, ultimately generating a refined buffer layer model suitable for multiphysics simulations.

[0027] The buffer layer is a porous medium composed only of fibers, air, and water-blocking powder. The QSGS method sets the growth nucleus distribution probability. Growth probability (i=1 8) Fiber porosity and fractal dimension D Parameters are used to generate an initial porous structure model of the buffer layer with consistency in randomness and statistical characteristics. The fiber porosity is calculated as shown in formula (1): (1); in: The fiber porosity; It represents the pore volume fraction; This represents the total volume of the initial porous structure model of the buffer layer.

[0028] The fractal dimension is calculated using the box-counting method, as shown in formula (2): (2); in: D It is the fractal dimension; r Box dimensions; For a side length of r The number of non-empty boxes required when covering a structure with boxes. During the calculation, this is done at different scales. and The slope of the linear fit is used to determine the fractal dimension.

[0029] In the initial generation of the porous structure model of the buffer layer, the fibrous phase is used as the main growth phase, and air is used as the non-growth phase. The water-blocking powder region is constructed after the fibrous skeleton is generated through a local secondary assignment method, forming a functional filling layer with a specific fiber porosity. Fiber porosity is used to control the overall ratio of pores to solids in the structure, and fractal dimension is used to characterize the structural complexity and space-filling characteristics. During the modeling process, the fractal dimension of the current structure is calculated in real time using the box counting method and compared with the target value: when the fractal dimension is too low, the growth probability is appropriately increased to enhance structural connectivity; when the fractal dimension is too high, the growth probability is decreased to suppress excessive growth, thus forming a dual-parameter feedback control mechanism for porosity and fractal dimension.

[0030] 102. Secondary optimization processing is performed on multiple initial porous buffer layer models to obtain multiple target porous buffer layer models.

[0031] Here, although the initial porous structure model of the buffer layer generated by the QSGS algorithm can well characterize the random porosity of the buffer layer, its local regions may still contain isolated fragments, sharp boundaries, narrow necks, and tiny closed pores, which can easily lead to a decrease in the quality of subsequent finite element meshes and affect the stability and convergence of multiphysics calculations. Therefore, after the initial porous structure model of the buffer layer is generated, it needs to be optimized secondaryly. For example... Figure 2 As shown, this can be achieved through the following steps: 201. For each group of initial buffer layer porous structure models, isolated fragments with a voxel count less than a preset threshold are removed, and the structural boundaries are smoothed.

[0032] Here, to reduce the impact of isolated noise generated during the random generation process on the structural features of the initial porous buffer layer model, a three-dimensional connected component screening method is used to preprocess the initial porous buffer layer model. First, the cross-sectional image is binarized, and a three-dimensional 26-neighborhood connected component screening method is used to identify each connected region. Then, the number of voxels contained in each connected region is counted, and isolated small regions with a voxel count below a preset threshold are identified as discrete fragments and removed, thereby obtaining an initial porous buffer layer model with connectivity that more closely matches the actual microstructural features. Subsequently, small-scale three-dimensional spherical structural elements are selected and closed and opened sequentially to fill local narrow gaps, remove small protrusions and burrs, and improve the continuity of the fiber skeleton and the smoothness of the fiber-pore interface in the initial porous buffer layer model. Further, a majority voting method based on the proportion of local voxels is adopted for downsampling, and the porosity, fractal dimension and connectivity characteristics before and after processing are compared. Under the premise that the deviation of the main pore morphology and statistical parameters is within the allowable range, the number of model voxels is reduced, thereby improving the subsequent mesh generation quality and finite element calculation efficiency, and thus making the target buffer layer porous structure model more accurate.

[0033] The preset threshold can be a custom threshold, which removes isolated fragments with a small number of voxels. To reduce the impact of jagged edges and isolated noise at the structural boundaries on the calculation results of electric field strength and current density, mathematical morphology opening and closing operations are used to smooth the structural boundaries. First, the initial porous structure model of the buffer layer is converted into a binary voxel matrix, using spherical structural elements as neighborhood templates. Then, opening operations are used to remove small protrusions and isolated fragments smaller than the structural elements at the boundaries, and closing operations are used to fill local small holes and depressions. The main morphology and pore connectivity characteristics of the processed initial porous structure model of the buffer layer remain basically unchanged, but the phase interface is smoother. Opening and closing operations can weaken the jagged edges of voxel boundaries and reduce the mesh quality degradation and numerical errors caused by local sharp corners, narrow slits, and isolated fragments.

[0034] 202. The smoothed porous structure model is subjected to a pore filling operation, and the connecting neck is thickened by an expansion operation to obtain the corresponding target buffer layer porous structure model.

[0035] Here, in the smoothed porous structure model, the non-existent closed cavities are first filled in, and then the excessively thin connecting parts are slightly thickened. This treatment can reduce the impact of local narrow regions and isolated micropores on mesh generation and finite element solution stability, while preserving the overall pore morphology and connectivity characteristics of the original structure as much as possible.

[0036] In steps 201 and 202 above, firstly, a connected component filtering method is used to remove isolated fragments with a small number of voxels to reduce the impact of random noise on the overall structure. Then, opening and closing operations in mathematical morphology are used to smooth the structural boundaries, improving the continuity of the pore and fiber interfaces. Furthermore, a hole-filling operation is used to eliminate locally closed cavities, and a moderate expansion operation is used to thicken the small connected necks to improve structural connectivity and mesh stability. Finally, the optimized target buffer layer porous structure model is downsampled to reduce the number of voxels while maintaining the main pore morphology and statistical characteristics, thereby improving the efficiency of subsequent finite element calculations.

[0037] 103. Using the finite element method, partial pressure under operating conditions is applied to multiple sets of target buffer layer porous structure models, and the electric field strength and current density inside the buffer layer are calculated based on the electric field control equation and thermal field control equation in the electrothermal coupling model.

[0038] Here, the partial pressure under operating conditions includes both the partial pressure under dry and damp conditions. The electric field strength and current density of the porous structure model of the target buffer layer are calculated using an electrothermal coupling model, thus obtaining the electric field strength and current density inside the buffer layer.

[0039] 104. Among the electric field strength and current density corresponding to multiple sets of target buffer layer porous structure models, obtain the maximum electric field strength and maximum current density.

[0040] Here, the maximum electric field strength and maximum current density can be calculated using the electrothermal coupling model. The electrothermal coupling model is constructed based on the porous structure model of the target buffer layer through the electric field control equation and the thermal field control equation. It is used to calculate the electric field strength, current density, etc. of the porous structure model of the target buffer layer, that is, to calculate the electric field strength, current density, etc. inside the buffer layer.

[0041] 105. Using the structural characteristics of each sample as input and the feasibility category as output, a classification regression tree is constructed and the feasible region discrimination rule for low ablation risk of the buffer layer is extracted. Based on the feasible region discrimination rule, the feasibility analysis of multiple groups of target buffer layer porous structure models is carried out to obtain the corresponding ablation risk assessment results.

[0042] Each sample includes feasible and infeasible samples. Feasible samples are those with a maximum electric field strength less than the electric field strength threshold and a maximum current density less than the current density threshold; the remaining porous target buffer layer models are infeasible. The electric field strength threshold and current density threshold are the critical electric field strength and critical current density corresponding to the ablation failure of the buffer layer. The maximum electric field strength and maximum current density, as well as the corresponding electric field strength threshold and current density threshold, are calculated using an electrothermal coupling model.

[0043] In some possible implementations, the generation and optimization process of the buffer layer structure based on the QSGS algorithm, such as Figure 3 As shown, firstly, parameter settings (i.e., target constraint parameters) are performed, including: computational domain, target porosity (i.e., water-blocking powder porosity and fiber porosity in the target constraint parameters), target fractal dimension, etc.; secondly, the growth kernel distribution probability and the neighborhood random growth probability (i.e., growth probability) are initialized. Next, calculate statistical parameters (including porosity and fractal dimension); then, determine whether the set target porosity and target fractal dimension have been reached; finally, if the set parameters are reached, generate the original two-phase structure (which includes fibers and pores). If the set parameters are not reached, return to the step of initializing the growth nucleus probability.

[0044] like Figure 3 As shown, the buffer layer structure optimization process includes: extracting the fiber phase, screening connected components, closing operation, opening operation, and downsampling, thereby obtaining the optimized two-phase structure (which includes fibers and pores).

[0045] like Figure 3 As shown, the water-blocking powder construction process includes: defining the water-blocking powder region, randomly generating powder voids, removing isolated powder fragments, closing operation, opening operation, and porosity correction to obtain the final three-phase structure (which includes fibers, pores, and water-blocking powder, thus obtaining the target buffer layer porous structure model).

[0046] In some possible implementations, after obtaining feasible and infeasible samples, a classification regression tree can be constructed through the following process: First, obtain the pore volume fraction, pore connectivity, interconnectivity, number of pore components, maximum pore size, and average pore size of multiple sets of samples, and use them as input feature variables for constructing the classification regression tree, with sample feasibility and infeasibility as the output categories; where multiple sets of samples include feasible and infeasible samples; Second, sequentially traverse each input feature variable and its candidate split points, select the feature variable that maximizes the reduction of node Gini impurity and its corresponding value range for sample splitting, and repeat this process until the set maximum number of splits or the minimum number of samples for a leaf node is reached; Finally, extract classification paths from leaf nodes with a high proportion of feasible samples, and combine the feature splitting conditions on the classification paths into feasible region discrimination rules to construct the classification regression tree.

[0047] Here, the Classification and Regression Tree (CART) establishes a mapping relationship between input features and output categories through recursive binary partitioning. For classification problems, CART uses the Gini index as the node splitting criterion. By traversing each structural feature and its candidate split points, it selects the feature and threshold that maximizes the class purity of the child node, thus progressively constructing the classification and regression tree. For continuous structural features, CART first sorts the feature values ​​in all samples in ascending order and uses the midpoint between two adjacent different values ​​as a candidate split point. Subsequently, the algorithm sequentially traverses all input features and their candidate split points, dividing the sample into left and right child nodes and calculating the weighted Gini impurity after the split. Finally, it selects the feature and its split point that maximizes the reduction in Gini impurity as the optimal splitting condition for the current node.

[0048] The Gini index can be expressed as: Where D is the sample set in the current node, and K is the number of categories. p k Let be the proportion of samples of class k in this node. CART constructs classification rules by traversing different structural features and their candidate split points, selecting the features and thresholds that reduce the Gini impurity most after splitting. For a given structural feature A and its candidate split point s, the sample set D is divided into two sub-segments. D1 and D2 The weighted Gini index after division is: CART enables selection The smallest structural features and candidate segmentation points complete the node partitioning. As the partitioning process proceeds recursively, samples are gradually assigned to different leaf nodes, thus forming classification rules with clear threshold conditions.

[0049] In the dry state of the buffer layer, the structural features of the buffer layer are used as input feature variables for the classification regression tree, and whether the buffer layer satisfies the non-ablation condition is used as the output category. When the sample simultaneously satisfies... E max ≤3.0×10 6 V / m and J max ≤1.02×10 2 A / m 2 If the condition is met, it is defined as a feasible sample; otherwise, it is defined as an infeasible sample. Based on this, CART is used to classify and determine the feasibility of different combinations of structural features.

[0050] The CART method demonstrates good applicability in evaluating the range of values ​​for buffer layer structural features. On one hand, the buffer layer is composed of multiphase components such as water-blocking powder, fibers, and pores. Its structural features exhibit significant nonlinear relationships and threshold effects with local electric field strength and current density, making it difficult for linear correlation analysis in related techniques to directly provide clear feasible domain boundaries. On the other hand, CART does not require pre-assuming the functional form between variables and can automatically identify key features that significantly influence the ablation state and their segmentation thresholds under the coupling effect of multiple structural features. Furthermore, CART has strong interpretability; its classification results can be transformed into intuitive decision paths. By extracting the segmentation conditions corresponding to leaf nodes with a high proportion of feasible samples, the range of structural feature combinations that satisfy electric field strength and current density constraints can be further obtained, providing a basis for the optimized design of the buffer layer structure and the determination of non-ablation conditions.

[0051] In a specific example, the structural characteristics of 40 sample groups—water-blocking powder porosity, fiber porosity, water-blocking powder layer thickness, fiber layer thickness, pore volume fraction, number of pore components, pore connectivity, interconnectivity, maximum pore size, and average pore size—were used as target constraint parameters. Based on the finite element analysis results, the samples were classified: samples that simultaneously met the electric field strength threshold and current density threshold were marked as feasible samples, and the rest were marked as infeasible samples. Among the 40 sample groups, there were 11 feasible samples and 19 infeasible samples.

[0052] Next, a classification model was established using the CART classification algorithm based on Gini impurity. The algorithm sequentially traverses each objective constraint parameter and its candidate split points, selecting the features and thresholds that maximize the reduction in Gini impurity at each node for sample splitting, and repeating this process until the set maximum number of splits or the minimum number of samples per leaf node is reached. To avoid overgrowth of the decision tree when the number of samples is small, CART sets the maximum number of splits to 4 and the minimum number of samples per leaf node to 3, and prunes the generated classification tree. Simultaneously, leave-one-out cross-validation is used to evaluate the model's classification ability on samples not used in training, and feature importance is calculated based on the reduction in impurity caused by each feature during node splitting. Finally, classification paths are extracted from leaf nodes with a high proportion of feasible samples, and the feature splitting conditions on these paths are combined into feasible region discrimination rules.

[0053] For the buffer layer in the dry state, CART analysis results show that porosity is the most critical factor affecting structural feasibility, significantly more important than other characteristics, followed by the maximum equivalent pore size and the volume-weighted average equivalent pore size. When the porosity is below 0.9825, all samples are deemed infeasible; when the porosity is not lower than 0.9825, it is further affected by pore size characteristics. Specifically, the volume-weighted average equivalent pore size needs to be greater than 0.6715 mm, and the maximum equivalent pore size less than 0.7482 mm for the structure to more easily meet the current density constraint requirements. The results indicate that in the dry state, a continuous conductive medium has not yet formed inside the buffer layer, and current transmission is mainly controlled by the overall pore network connectivity. High connectivity is beneficial for homogenizing the electric field intensity, while excessively large local pore sizes can easily cause current concentration.

[0054] After the buffer layer has been exposed to moisture, the test results above show that the current density at which it undergoes ablation decreases. Therefore, when the sample meets the requirements... J max ≤1.02×10 2 A / m 2 and E max ≤3.0×10 6When the density is V / m, it is defined as a feasible sample; otherwise, it is defined as an infeasible sample. For the buffer layer under damp conditions, the influencing factors changed significantly. Feature importance analysis showed that the maximum equivalent pore size, fiber porosity, and the number of pore connected domains became the most important discriminant features, while the importance of pore connectivity decreased significantly. Among them, the maximum equivalent pore size had the highest importance, indicating that local macropore defects after dampness play a decisive role in current density. When the fiber porosity is greater than 0.6855 and the number of pore connected domains is less than 1950, the structure is more likely to meet the current density constraint; for samples with low fiber porosity, only when the maximum equivalent pore size is in the range of 0.7219–0.8531 mm is it still highly feasible. This result indicates that after moisture enters the pores, it forms a conductive medium, and the overall interconnected network is no longer a limiting factor; local pore defects and fiber layer structure begin to dominate the formation process of conductive channels.

[0055] The analysis results under both dry and damp conditions reveal significant differences in the current density control mechanism. Under dry conditions, current transport is primarily controlled by the overall pore network structure, with pore connectivity being the core parameter determining structural performance. However, under damp conditions, the maximum equivalent pore size, fiber porosity, and the number of connected pore domains become the key factors determining current density. Therefore, in optimizing the buffer layer structure, the focus should be on the connectivity of the pore network under dry conditions, while under damp conditions, the focus should be on controlling localized large-pore defects and the pore structure of the fiber layer to reduce the risk of current concentration and discharge ablation.

[0056] In some possible implementations, a buffer layer ablation test platform is built to test the electric field strength and current density of the buffer layer ablation. Based on the buffer layer ablation test platform, during the test, the buffer layer sample is placed between the upper and lower electrodes, the applied current is gradually increased, and the electric field strength and current density when the buffer layer sample ablates are recorded to obtain the electric field strength threshold and current density threshold corresponding to the current ablation fault of the buffer layer.

[0057] Here, during the experiment, the critical electric field strength and critical current density when the buffer layer experienced an ablation failure were measured; the critical electric field strength and critical current density were determined as the electric field strength threshold and current density threshold, respectively. An ablation failure was defined as the occurrence of an ablation failure in the buffer layer; specifically, the electric field strength threshold for the buffer layer in a dry state was 3.0 × 10⁻⁶. 6 V / m, current density threshold is 1.02×10 2 A / m 2 The electric field strength threshold of the buffer layer under damp conditions is 3.0 × 10⁻⁶. 6 V / m, current density threshold is 56.1 A / m 2 .

[0058] In some possible implementations, the electric field governing equations in the electrothermal coupling model include: the current conservation equation. and current density ;in, A current source per unit volume j The imaginary unit, To inject current density externally, The electric field vector, For electrical conductivity, Angular frequency, It is the electric displacement vector. For the Nabra operator; the thermal field governing equation in the electrothermal coupling model is: ;in, It is a heat flux vector. As a volumetric heat source, As a boundary heat source, It is the heat source generated by the Joule heating effect.

[0059] In some embodiments, to facilitate subsequent determination of a reasonable range of values ​​for the structural characteristics of the buffer layer when it does not ablate, it is necessary to analyze the structural characteristics of the buffer layer under different operating conditions. First, cross-sectional images of the buffer layer under dry and damp conditions are acquired using an X-ray micro-CT scanning system. The cross-sectional image under dry conditions is shown below. Figure 4 As shown, the cross-sectional image of the buffer layer in a damp state is as follows: Figure 5 As shown in the figure. Based on this, image processing algorithms are used to segment and identify the water-blocking powder and fiber regions, and combined with statistical analysis methods, structural features such as porosity, thickness, and fractal dimension are extracted.

[0060] Table 1 shows that the porosity of the water-blocking powder was 0.43-0.70 under dry conditions, decreasing to 0.38-0.65 after moisture absorption; the thickness of the water-blocking powder increased from 0.33-0.46 mm to 1.20-1.35 mm, indicating that the water-blocking powder underwent significant expansion and its structure became denser after moisture absorption. In contrast, the changes in fiber porosity and thickness were smaller, with fiber porosities of 0.66-0.77 and 0.60-0.78, respectively, and thicknesses of 0.20-1.00 mm for both. Furthermore, the fractal dimension ranges of the buffer layer were quite similar under the two conditions, 1.74-1.83 and 1.70-1.84, respectively, indicating that moisture absorption had a relatively limited overall impact on the structural complexity of the buffer layer.

[0061] Table 1. Structural characteristics of the buffer layer under different conditions

[0062] In the subsequent electrothermal coupling model, the magnitude of the voltage applied to the buffer layer directly affects the insulation field strength. This parameter is calculated using the voltage divider formula (3): (3); in, It is the voltage divider of the buffer layer, V; It is the voltage of a conductor relative to ground, in volts (V). It is the equivalent capacitance of the buffer layer, F; It is the equivalent capacitance of the insulation layer, F. According to this formula, the potential in the dry state is 78V (i.e., the voltage division in the dry state). Because the relative permittivity and volume resistivity of the buffer layer in the damp state increase significantly due to the precipitation of water-blocking powder, resulting in an increase in the voltage division, the potential in the damp state is taken as 150V (i.e., the voltage division in the damp state).

[0063] To facilitate the subsequent determination of a reasonable range of structural characteristics for which the buffer layer does not ablate, it is also necessary to clarify the electric field strength threshold and current density threshold for ablation. Therefore, a buffer layer ablation test platform was constructed, consisting of an AC power supply, sample clamps, an electrode system, voltage and current measuring devices, and an ablation state monitoring device. During the experiment, the buffer layer sample was placed between the upper and lower electrodes, and the applied current was gradually increased. The electrical parameters of the sample during ablation were observed and recorded.

[0064] Experimental results show that when the local current density reaches 1.02 × 10⁻⁶, 2 A / m 2 When dry, the buffer layer exhibited significant current ablation failure. However, when damp, the ablation current density decreased to 56.1 A / m. 2 Because the fibers and air pores within the buffer layer are intertwined, the electric field strength exhibits significant non-uniformity, with electric field concentration easily occurring at localized air pores. Considering that the breakdown field strength of air is lower than that of the solid material in the buffer layer, the breakdown field strength of air at room temperature and pressure can be estimated as 3.0 × 10⁻⁶. 6 V / m is used as the electric field strength threshold for partial discharge ablation of the buffer layer. Therefore, the above-mentioned electric field strength threshold and current density threshold are used as the judgment thresholds in subsequent analyses.

[0065] To reduce the time and computational resources required for constructing the initial porous buffer layer model and performing finite element analysis, while ensuring good coverage of the parameter space, this embodiment of the invention employs the Latin hypercube sampling method. Using the porosity of the water-blocking powder, the porosity of the fiber, and the thickness as initial design variables, 40 representative parameter combinations are selected to generate the initial porous buffer layer model. Compared to a fully combined experiment, Latin hypercube sampling can stratify the value ranges of each parameter and distribute the samples more evenly across the multidimensional parameter space. This allows for reflecting the influence of different structural features (i.e., different target constraint parameters) combinations on the electric field characteristics of the buffer layer with fewer computational samples. Initial porous buffer layer models are established based on the sampling parameters, and finite element analysis is performed to obtain the maximum electric field intensity of each initial porous buffer layer model. E max and maximum current density J max It should be noted that porosity and thickness mainly reflect the macroscopic composition and dimensional characteristics of the structure, and are insufficient to fully describe the spatial distribution and topological morphology of the pores. Due to the influence of the random growth process, even with similar initial porosity and thickness, the degree of pore connectivity, the number of pore components, and the pore size distribution may still differ among different initial porous structure models of buffer layers. Therefore, after the initial porous structure model of the buffer layer is generated, structural features such as pore volume fraction, pore connectivity, interconnectivity, number of pore components, maximum pore size, and average pore size are further extracted. This facilitates the use of initial design parameters and derived structural features as input feature variables for classification regression tree (CART) classification, in order to identify key structural factors affecting the electrical performance of the buffer layer and the range of values ​​for feasible samples.

[0066] Based on the obtained structural features, Spearman rank correlation coefficient was used to analyze the correlation between each structural feature and the maximum electric field strength and maximum current density. Spearman rank correlation coefficient is not directly based on the original data size, but rather on the rank of each structural feature after sorting, thus reflecting the monotonic correlation between structural features.

[0067] Table 2 shows the correlation coefficients between structural features and maximum electric field strength and maximum current density under dry conditions, and Table 3 shows the correlation coefficients between structural features and maximum electric field strength and maximum current density under damp conditions; therefore, it can be seen that each structural feature correlates with the maximum electric field strength. E maxThe overall correlation between the parameters was weak. The correlation coefficient for pore volume fraction was the largest, but only -0.36, indicating that macroscopic structural features could not adequately describe the local electric field concentration phenomenon. In contrast, the maximum current density was more sensitive to pore structure: the porosity, pore volume fraction, and pore connectivity of the water-blocking powder showed a strong negative correlation with the maximum current density, with correlation coefficients of -0.83, -0.72, and -0.74, respectively; the number of pore components showed a strong positive correlation with the maximum current density, with a correlation coefficient of 0.75; and the connectivity showed a moderate negative correlation with the maximum current density, with a correlation coefficient of -0.58. These results suggest that increasing porosity and pore connectivity, and reducing isolated or dispersed pore components, is beneficial for reducing the current density concentration in the buffer layer. Since this analysis was based on 40 samples, the obtained correlation coefficients are mainly used to characterize the statistical association between structural features and do not directly represent causal relationships.

[0068] Table 2 Structural features and E max and J max Correlation coefficient (dry state)

[0069] Table 3 Structural features and E max and J max Correlation coefficient (dampness condition)

[0070] In some embodiments, the feasible domain analysis process for the porous structure model of the buffer layer in the dry state is as follows: For the samples in the dry state, a total of 40 samples were collected, of which 18 met the electrical performance constraints, with an overall feasibility rate of 45.0%. CART analysis results showed that pore connectivity was the primary factor affecting structural feasibility, with its importance significantly higher than other structural features, followed by volume-weighted average equivalent pore size and maximum equivalent pore size.

[0071] The initial split of the classification regression tree is determined by the porosity. When the porosity is below 0.9825, all samples are classified as infeasible. When the porosity is not below 0.9825, the volume-weighted average equivalent pore size further influences the classification results. When the volume-weighted average equivalent pore size is greater than 0.6715 mm and the maximum equivalent pore size is less than 0.7482 mm, the proportion of feasible samples in the corresponding leaf nodes reaches 91.7%, indicating that the region has high structural feasibility.

[0072] The results indicate that, in the dry state, a distinct continuous conductive medium has not yet formed inside the buffer layer, and current transmission is primarily controlled by the overall pore network structure. Higher pore connectivity is beneficial for homogenizing the electric field intensity within the buffer layer, reducing local current concentration; conversely, excessively large local pore sizes can easily create regions of electric field distortion, leading to increased current density. Therefore, structural optimization of the buffer layer in the dry state should focus on ensuring high pore connectivity while limiting the formation of large local pore defects.

[0073] In some embodiments, the feasible region analysis process for the porous structure model of the buffer layer under damp conditions is as follows: For the damp samples, a total of 40 structural samples were collected, of which 16 met the electrical performance constraints, resulting in an overall feasibility rate of 40.0%. CART analysis results showed that the maximum equivalent pore size was the most important factor affecting structural feasibility, followed by fiber porosity and the number of pore connectivity domains, while the importance of pore connectivity decreased significantly.

[0074] The initial split of the classification regression tree is determined by fiber porosity. When the fiber porosity is below 0.6855, the maximum equivalent pore size further determines the feasibility of the structure; when the fiber porosity is not lower than 0.6855, the number of pore connected domains becomes the main discriminant factor. Specifically, when the fiber porosity is high and the number of pore connected domains is less than 1950, the probability of the sample entering the feasible region increases significantly; for samples with low fiber porosity, good structural feasibility is only observed when the maximum equivalent pore size is in the range of 0.7219–0.8531 mm.

[0075] This invention provides a system for evaluating the structural features of a cable buffer layer, such as... Figure 6 As shown, the system 500 includes: Module 501 is used to sample multiple sets of design parameter combinations by using the Latin hypercube sampling method with the water-blocking powder porosity, fiber porosity, water-blocking powder thickness, and fiber thickness of the buffer layer as initial design variables; each set of design parameter combinations is used as target constraint parameters, and the QSGS algorithm is used to construct multiple initial porous structure models of the buffer layer that satisfy the target constraint parameters and are used to characterize the random porosity characteristics of the buffer layer. Optimization module 502 is used to perform secondary optimization on multiple sets of initial buffer layer porous structure models to obtain multiple sets of target buffer layer porous structure models. The calculation module 503 is used to apply the partial pressure under operating conditions to multiple sets of target buffer layer porous structure models using the finite element method, and calculate the electric field strength and current density inside the buffer layer based on the electric field control equation and thermal field control equation in the electrothermal coupling model. The acquisition module 504 is used to acquire the maximum electric field intensity and the maximum current density among the electric field intensity and current density corresponding to multiple sets of target buffer layer porous structure models. Analysis module 505 is used to construct a classification regression tree and extract feasible domain discrimination rules for low ablation risk of buffer layers, taking the structural characteristics of each sample as input and the feasibility category as output. Based on the feasible domain discrimination rules, it performs feasibility analysis on multiple groups of target buffer layer porous structure models and obtains the corresponding ablation risk assessment results. Each sample includes feasible and infeasible samples. Target buffer layer porous structure models with a maximum electric field strength less than the electric field strength threshold and a maximum current density less than the current density threshold are feasible samples, while the remaining target buffer layer porous structure models are infeasible samples. The electric field strength threshold and current density threshold are the critical electric field strength and critical current density corresponding to the ablation failure of the buffer layer.

[0076] In some possible implementations, the optimization module 502 is also used to remove isolated fragments with a voxel count less than a preset threshold from each group of initial buffer layer porous structure models, smooth the structural boundaries, perform a pore filling operation on the smoothed porous structure model, and use an expansion operation to thicken the connecting neck to obtain the corresponding target buffer layer porous structure model.

[0077] In some possible implementations, the analysis module 505 is also used to build a buffer layer ablation test platform for testing the electric field strength and current density of the buffer layer ablation; based on the buffer layer ablation test platform, during the test, the buffer layer sample is placed between the upper and lower electrodes, the applied current is gradually increased, and the electric field strength and current density when the buffer layer sample ablation occurs are recorded to obtain the electric field strength threshold and current density threshold corresponding to the current ablation fault of the buffer layer.

[0078] In some possible implementations, the analysis module 505 is further used to determine, during the experiment, the critical electric field strength and critical current density when the buffer layer experiences an ablation failure as the electric field strength threshold and current density threshold, respectively; wherein, the electric field strength threshold of the buffer layer in the dry state is 3.0 × 10⁻⁶. 6 V / m, current density threshold is 1.02×10 2 A / m 2 The electric field strength threshold of the buffer layer under damp conditions is 3.0 × 10⁻⁶. 6 V / m, current density threshold is 56.1 A / m 2 .

[0079] In some possible implementations, the electric field governing equations in the electrothermal coupling model include: the current conservation equation. and current density ;in, The electric field vector, For electrical conductivity, Angular frequency, It is the electric displacement vector. For the Nabra operator; the thermal field governing equation in the electrothermal coupling model is: ;in, It is a heat flux vector. As a volumetric heat source, As a boundary heat source, It is the heat source generated by the Joule heating effect.

[0080] In some possible implementations, the construction module 501 is also used to acquire cross-sectional images of the buffer layer in dry and damp states using an X-ray micro-CT scanning system; segment and identify the water-blocking powder and fiber regions in the cross-sectional images; extract the water-blocking powder porosity, fiber porosity, water-blocking powder thickness, fiber thickness, and fractal dimension from the identified water-blocking powder and fiber regions; and use the water-blocking powder porosity, fiber porosity, water-blocking powder thickness, fiber thickness, fractal dimension, and the set growth kernel distribution probability and growth probability as target constraint parameters for QSGS algorithm modeling.

[0081] In some possible implementations, the construction module 501 is also used to determine the computational domain at preset grid points; randomly distribute fiber growth nuclei within the computational domain according to the growth nuclei distribution probability, and the growth nuclei distribution probability is less than or equal to the difference between 1 and the fiber porosity; randomly expand towards the neighborhood of the selected fiber growth nuclei according to the set directional growth probability to form a fiber skeleton structure; in the fiber skeleton structure, a water-blocking powder functional layer is introduced into the local area of ​​the buffer layer, and the local fiber porosity is reassigned in the local area to generate a fiber water-blocking powder hybrid structure to simulate the filling characteristics of water-blocking powder particles; repeat the above fiber skeleton structure formation process and the process of simulating the filling characteristics of water-blocking powder particles, and calculate the current fiber porosity and fractal dimension in real time; stop the iteration after the current fiber porosity and fractal dimension both reach the set target, and obtain the initial buffer layer porous structure model that meets the statistical characteristics requirements.

[0082] In some possible implementations, the construction module 501 is also used to calculate the fractal dimension of the current model in real time during the construction of the initial porous structure model of the buffer layer; wherein, the fractal dimension of the current model... , For box counting, the box dimensions are... The number of non-empty boxes required to cover the structure; compare the fractal dimension of the current model with the target fractal dimension. If the fractal dimension of the current model is less than the target fractal dimension, increase the growth probability to enhance connectivity; if the fractal dimension of the current model is greater than the target fractal dimension, decrease the growth probability to suppress overgrowth.

[0083] In some possible implementations, the analysis module 505 is also used to obtain the pore volume fraction, pore connectivity, interconnectivity, number of pore components, maximum pore size, and average pore size of multiple sets of samples, and use them as input feature variables for constructing a classification regression tree, with sample feasibility and infeasibility as output categories; wherein, the multiple sets of samples include: feasible samples and infeasible samples; each input feature variable and its candidate split point are traversed sequentially, and the feature variable that can reduce the Gini impurity of the node to the greatest extent and its corresponding value range are selected for sample division, and this process is repeated until the set maximum number of splits or the minimum number of samples in the leaf node is reached; classification paths are extracted from the leaf nodes where the proportion of feasible samples is higher than a preset proportion threshold, and the feature segmentation conditions on the extracted classification paths are combined into feasible region discrimination rules to construct a classification regression tree.

[0084] Optionally, the transmission medium can be a wired link (e.g., but not limited to, coaxial cable, optical fiber, and Digital Subscriber Line (DSL)) or a wireless link (e.g., but not limited to, Wireless Fidelity (WIFI), Bluetooth, and mobile device networks). It should be noted that the control device provided in the above embodiments is only an example illustrating the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the method embodiments provided in the above embodiments belong to the same concept, and their specific implementation processes are detailed in the method embodiments, and will not be repeated here.

[0085] Figure 7 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. For example, as shown... Figure 7 As shown, the computer device 600 includes: a memory 601, a processor 602, and a computer program 603 stored in the memory 601 and running on the processor 602, wherein when the processor 602 executes the computer program 603, the computer device can execute any of the cable buffer layer structure feature evaluation methods described above.

[0086] Furthermore, embodiments of the present invention also protect a control device, which may include a memory and a processor. The memory stores executable program code, and the processor is used to call and execute the executable program code to perform a cable buffer layer structure feature evaluation method provided by the embodiments of the present invention. Embodiments of the present invention can divide the control device into functional modules according to the above method examples. For example, each module may correspond to a specific function, or two or more functions may be integrated into a processing module. The integrated module can be implemented in hardware. It should be noted that the module division in the embodiments of the present invention is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here. It should be understood that the control device provided by the embodiments of the present invention is used to execute the above-mentioned cable buffer layer structure feature evaluation method, and therefore can achieve the same effect as the above-mentioned implementation method. When using integrated units, the control device may include a processing module and a storage module. When the control device is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device in executing mutual program code, etc. The processing module may be a processor or a controller, which can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor may also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0087] Furthermore, the control device provided in the embodiments of the present invention may specifically be a chip, component, or module. The chip may include a connected processor and a memory. The memory stores instructions, and when the processor calls and executes the instructions, the chip can execute the cable buffer layer structure feature evaluation method provided in the above embodiments. The embodiments of the present invention also provide a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the cable buffer layer structure feature evaluation method provided in the above embodiments.

[0088] This invention also provides a computer program product. When the computer program product is run on a computer, it causes the computer to perform the aforementioned related steps to implement the cable buffer layer structure feature evaluation method provided in the above embodiments. The control device, computer-readable storage medium, computer program product, or chip provided in this invention are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here. Through the description of the above embodiments, those skilled in the art can understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the control device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed control device and method can be implemented in other ways. For example, the control device embodiments described above are merely illustrative. For example, the division of modules or units is merely a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another control device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, control device or unit, and can be electrical, mechanical or other forms.

[0089] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for evaluating the structural characteristics of a cable buffer layer, characterized in that, The method includes: The Latin hypercube sampling method was used to sample multiple sets of design parameter combinations by taking the water-blocking powder porosity, fiber porosity, water-blocking powder thickness and fiber thickness of the buffer layer as initial design variables. Each set of design parameters is used as the target constraint parameters. The QSGS algorithm is used to construct multiple initial porous structure models of the buffer layer that satisfy the target constraint parameters and are used to characterize the random porosity of the buffer layer. Multiple initial porous buffer layer models were optimized in a secondary manner to obtain multiple target porous buffer layer models. Using the finite element method, partial pressure under operating conditions is applied to multiple target buffer layer porous structure models, and the electric field intensity and current density inside the buffer layer are calculated based on the electric field control equation and thermal field control equation in the electrothermal coupling model. Among the electric field strength and current density corresponding to multiple sets of target buffer layer porous structure models, the maximum electric field strength and maximum current density are obtained. Using the structural features of each sample as input and the feasibility category as output, a classification regression tree is constructed and the feasible region discrimination rules for low ablation risk of the buffer layer are extracted. Feasibility analysis was conducted on multiple sets of target buffer layer porous structure models based on the feasible region discrimination rule, and the corresponding ablation risk assessment results were obtained. Each sample includes feasible samples and infeasible samples. Among them, the target buffer layer porous structure models with a maximum electric field strength less than the electric field strength threshold and a maximum current density less than the current density threshold are feasible samples, and the remaining target buffer layer porous structure models are infeasible samples. The electric field strength threshold and the current density threshold are the critical electric field strength and critical current density corresponding to the ablation failure of the buffer layer.

2. The method according to claim 1, characterized in that, Multiple initial porous buffer layer models were optimized to obtain multiple target porous buffer layer models, including: For each group of initial buffer layer porous structure models, isolated fragments with a voxel count less than a preset threshold are removed, and the structural boundaries are smoothed. The smoothed porous structure model is filled with pores, and the connecting neck is thickened by an expansion operation to obtain the corresponding target buffer layer porous structure model.

3. The method according to claim 1, characterized in that, The method further includes: An experimental platform for testing the electric field strength and current density of the buffer layer during ablation was constructed. Based on the buffer layer ablation test platform, during the test, the buffer layer sample is placed between the upper and lower electrodes, the applied current is gradually increased, and the electric field strength and current density when the buffer layer sample is ablated are recorded, so as to obtain the electric field strength threshold and current density threshold corresponding to the current ablation fault of the buffer layer.

4. The method according to claim 3, characterized in that, The method further includes: During the experiment, the critical electric field strength and critical current density at which the buffer layer ablation failed were determined as the electric field strength threshold and current density threshold, respectively; among them, the electric field strength threshold of the buffer layer in the dry state was 3.0 × 10⁻⁶. 6 V / m, current density threshold is 1.02×10 2 A / m 2 The electric field strength threshold of the buffer layer under damp conditions is 3.0 × 10⁻⁶. 6 V / m, current density threshold is 56.1 A / m 2 .

5. The method according to claim 1, characterized in that, The electric field governing equations in the electrothermal coupling model include: the current conservation equation. and current density ;in, A current source per unit volume j The imaginary unit, To inject current density externally, The electric field vector, For electrical conductivity, Angular frequency, It is the electric displacement vector. For the Nabra operator; The thermal field control equation in the electrothermal coupling model is: ;in, It is a heat flux vector. As a volumetric heat source, As a boundary heat source, It is the heat source generated by the Joule heating effect.

6. The method according to claim 1, characterized in that, The method further includes: Cross-sectional images of the buffer layer under dry and damp conditions were obtained using an X-ray micro-CT scanning system. Segmentation and identification of water-blocking powder and fiber regions in cross-sectional images; In the identified water-blocking powder and fiber regions, the porosity of the water-blocking powder, the porosity of the fiber, the thickness of the water-blocking powder, the thickness of the fiber, and the fractal dimension are extracted. The porosity of the water-blocking powder, the porosity of the fiber, the thickness of the water-blocking powder, the thickness of the fiber, the fractal dimension, and the set growth kernel distribution probability and growth probability are used as the target constraint parameters for modeling the QSGS algorithm.

7. The method according to claim 6, characterized in that, Multiple initial porous structure models of the buffer layer, satisfying the target constraint parameters, are constructed using the QSGS algorithm to characterize the random porosity features of the buffer layer, including: Determine the computational domain at preset grid points; The fiber growth nuclei are randomly distributed within the computational domain based on the growth nuclei distribution probability, and the growth nuclei distribution probability is less than or equal to the difference between 1 and the fiber porosity. The fiber grows randomly in the neighborhood of the selected fiber growth nucleus according to a set growth probability, forming a fiber skeleton structure. In the fiber structure skeleton, a water-blocking powder functional layer is introduced into the local area of ​​the buffer layer, and the local fiber porosity is reassigned in the local area to generate a fiber water-blocking powder hybrid structure to simulate the filling characteristics of water-blocking powder particles. Repeat the above process of fiber skeleton structure formation and simulation of water-blocking powder particle filling characteristics, and calculate the current fiber porosity and fractal dimension in real time; stop the iteration after the current fiber porosity and fractal dimension both reach the set target, and obtain the initial buffer layer porous structure model that meets the statistical characteristics requirements.

8. The method according to claim 7, characterized in that, The method further includes: During the construction of the initial porous structure model of the buffer layer, the fractal dimension of the current model is calculated in real time; where, the fractal dimension of the current model... , For box counting, the box dimensions are... The number of non-empty boxes required to cover the structure; The fractal dimension of the current model is compared with the target fractal dimension. If the fractal dimension of the current model is less than the target fractal dimension, the growth probability is increased to enhance connectivity. If the fractal dimension of the current model is greater than the target fractal dimension, the growth probability is reduced to suppress overgrowth.

9. The method according to claim 1, characterized in that, Using the structural features of each sample as input and the feasibility category as output, a classification regression tree is constructed, and the feasibility domain discrimination rules for low ablation risk of the buffer layer are extracted, including: The pore volume fraction, pore connectivity, interconnectivity, number of pore components, maximum pore size, and average pore size of multiple sets of samples are obtained and used as input feature variables for constructing a classification regression tree. The feasibility and infeasibility of the samples are used as the output categories. The multiple sets of samples include feasible samples and infeasible samples. Iterate through each input feature variable and its candidate split points in turn, select the feature variable that can reduce the Gini impurity of the node to the greatest extent and the corresponding value range for sample division, and repeat the process until the set maximum number of splits or the minimum number of leaf nodes is reached. Classification paths are extracted from leaf nodes where the proportion of feasible samples is higher than a preset threshold. The feature segmentation conditions on the extracted classification paths are combined into feasible region discrimination rules to construct a classification regression tree.

10. A system for evaluating the structural characteristics of a cable buffer layer, characterized in that, The system includes: The module is used to sample multiple sets of design parameter combinations by using the Latin hypercube sampling method with the water-blocking powder porosity, fiber porosity, water-blocking powder thickness, and fiber thickness of the buffer layer as initial design variables; and each set of design parameter combinations is used as target constraint parameters. The QSGS algorithm is used to construct multiple initial porous structure models of the buffer layer that meet the target constraint parameters and are used to characterize the random porosity characteristics of the buffer layer. The optimization module is used to perform secondary optimization on multiple sets of initial buffer layer porous structure models to obtain multiple sets of target buffer layer porous structure models. The calculation module is used to apply the partial pressure under operating conditions to multiple sets of target buffer layer porous structure models using the finite element method, and calculate the electric field strength and current density inside the buffer layer based on the electric field control equation and thermal field control equation in the electrothermal coupling model. The acquisition module is used to obtain the maximum electric field intensity and the maximum current density among the electric field intensity and current density corresponding to multiple sets of target buffer layer porous structure models. The analysis module is used to construct a classification regression tree and extract feasible domain discrimination rules for low ablation risk of buffer layers, taking the structural characteristics of each sample as input and the feasibility category as output. Based on the feasible domain discrimination rules, it performs feasibility analysis on multiple groups of target buffer layer porous structure models and obtains the corresponding ablation risk assessment results. Each sample includes feasible and infeasible samples. Target buffer layer porous structure models with a maximum electric field strength less than the electric field strength threshold and a maximum current density less than the current density threshold are considered feasible samples, while the remaining target buffer layer porous structure models are considered infeasible samples. The electric field strength threshold and current density threshold are the critical electric field strength and critical current density corresponding to the ablation failure of the buffer layer.

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