Rock breaking method capable of realizing self-adaptive regulation and control of electrode form under action of high-voltage electric pulse

Abstract

The invention provides a rock breaking method for self-adaptive regulation and control of an electrode form under the action of high-voltage electric pulses, and relates to the technical field of high-voltage rock breaking. The method comprises the following steps: firstly, acquiring multi-feature parameters, constructing a comprehensive evaluation parameter set and a target working condition reference evaluation parameter set, and calculating a working condition deviation index; and then geometric shape parameters including the curvature radius, the blunt degree, the chamfer size, the asymmetry degree and the surface roughness of the end of the high-voltage electrode are introduced, a linear correlation model of the electrode shape and the rock breaking effect is established, and prediction of the rock breaking effect is achieved. And on the basis of the working condition deviation index and the correlation model output result, form sensitivity and form regulation and control criteria are obtained, an electrode form regulation and control instruction set is generated, machining, polishing or replacement is conducted on the end of the electrode, and quantitative optimization and closed-loop regulation and control of the high-voltage electric pulse rock breaking effect are achieved. The problems that in the high-voltage electric pulse rock breaking process in the prior art, data utilization is dispersed, and intelligent regulation and control of the electrode form cannot be supported are solved.

Description

Technical Field

[0001] This invention relates to the field of high-pressure rock breaking technology, and in particular to a rock breaking method with adaptive control of electrode morphology under high-voltage electrical pulse. Background Technology

[0002] High-voltage electric pulse (HEP) rock breaking technology utilizes short-duration, high-energy electric pulses to create plasma channels within rock masses. The rapid expansion of these channels generates shock waves, causing the rock mass to undergo a fracturing process primarily characterized by tensile failure. With increasing demands for drilling in deep and ultra-deep complex formations, related research has gradually expanded from simply focusing on electrical parameters such as voltage and pulse width to acquiring and analyzing multi-source monitoring data on drilling depth, drilling speed, energy consumption, cuttings size, and borehole diameter changes. This allows for the evaluation and optimization of HEP rock breaking effects in both laboratory and field settings. In existing technologies, researchers have established multiphysics numerical models and several empirical formulas to analyze the relationship between electrical breakdown, channel expansion, shock wave propagation, and rock mass failure. Based on these, discharge parameters and some structural parameters are adjusted to improve energy utilization efficiency and rock breaking efficiency.

[0003] In the continuous development of high-voltage pulsed rock breaking technology, multi-source data acquisition and data-driven analysis methods are receiving increasing attention. In engineering practice, voltage sensors, current sensors, energy testing devices, and drilling status monitoring devices are widely deployed, introducing online acquisition of information such as drilling depth, drilling speed, torque changes, cuttings particle size distribution, and pore size characteristics, providing a rich data foundation for the quantitative evaluation of the rock breaking process. On the other hand, with the improvement of computing power, statistical analysis, regression modeling, and preliminary intelligent algorithms based on historical working condition data are being used to explore the correspondence between electrode morphology, discharge parameters, and rock breaking effect, attempting to improve breaking efficiency and pore size stability through parameter optimization. These trends indicate that high-voltage pulsed rock breaking is shifting from experience-based regulation to data-driven prediction and optimization, and the role of data processing methods in rock breaking control is becoming increasingly prominent.

[0004] However, existing technologies still have significant shortcomings in multi-source data utilization and decision-making logic. On the one hand, the large amount of discharge data, drilling data, cuttings data, and borehole diameter data collected during existing high-voltage pulse rock breaking processes are mostly used for single-index evaluation or empirical adjustments. There is a lack of a systematic method for unified processing and comprehensive evaluation of energy characteristics, drilling characteristics, and fracture morphology characteristics, thus failing to form a comprehensive and quantitative characterization of the rock breaking state. On the other hand, although existing research has revealed the influence of electrode tip geometry on plasma channel morphology and rock breaking effect, a data processing method has not yet been established that uses comprehensive evaluation parameters as the core, automatically generates electrode tip geometry adjustment strategies based on operating condition deviations, and further outputs electrode morphology control commands that can be directly invoked by the execution unit. Existing technologies typically remain at the offline analysis and manual adjustment stage, lacking a closed-loop update and iterative optimization mechanism for continuous rock breaking processes. They cannot gradually correct the model and optimize the control strategy during multiple rounds of discharge and drilling, thereby failing to adjust the electrode tip geometry in real time and adaptively under different formation conditions. Therefore, there is an urgent need for a systematic processing method for multi-source monitoring data of high-voltage electric pulse rock breaking. This method can achieve closed-loop adaptive control of the geometry of the high-voltage electrode end by constructing comprehensive evaluation parameters, establishing a correlation model between electrode morphology and rock breaking effect, evaluating working condition deviations, and generating morphology control criteria and control commands, so as to improve energy utilization efficiency, crushing efficiency and pore size stability. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, the purpose of this invention is to provide a rock-breaking method with adaptive control of electrode morphology under high-voltage electric pulse. This invention solves the problem that in the prior art, multi-source monitoring data in the high-voltage electric pulse rock-breaking process does not form a unified comprehensive evaluation and judgment system, resulting in scattered data utilization and an inability to support intelligent control of electrode morphology.

[0006] To achieve the above objectives, the present invention provides the following solution: A rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse, comprising: The raw data of the rock breaking process is obtained and subjected to time synchronization processing, noise reduction processing and normalization processing to obtain the preprocessed dataset of the rock breaking process. An electrical breakdown channel model was constructed, and the growth behavior of the electrical breakdown plasma channel during the high-voltage electric pulse rock breaking process was simulated based on the electrical breakdown channel model to obtain the characteristic parameter set of the electrical breakdown channel. Energy characteristic parameters, drilling characteristic parameters, and fracture morphology characteristic parameters are extracted from the preprocessed dataset of the rock breaking process. The energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters, and electrical breakdown channel characteristic parameter set are then weighted and combined to obtain a comprehensive evaluation parameter set. The operating condition deviation index is obtained by performing operating condition deviation assessment on the comprehensive evaluation parameter set. Obtain the geometric morphology parameters of the high-voltage electrode end, and construct a correlation model between electrode morphology and rock-breaking effect based on the geometric morphology parameters of the high-voltage electrode end and the comprehensive evaluation parameter set; The operating condition deviation index is input into the correlation model to obtain the morphological control criterion result; Based on the morphology control criteria, an electrode morphology control instruction set is generated to adjust the geometry of the high-voltage electrode tip, and high-voltage electric pulse rock breaking is implemented under the adjusted high-voltage electrode tip geometry.

[0007] The present invention discloses the following technical effects: This invention provides a rock-breaking method with adaptive control of electrode morphology under high-voltage electric pulse. The proposed electrode morphology-rock-breaking effect correlation model organically couples the geometric morphology parameters of the high-voltage electrode tip with the current working condition evaluation parameters and the target working condition benchmark evaluation parameters. This overcomes the problems of electrode structure design mainly relying on empirical trial and error, the inability to quantitatively assess the impact of electrode morphology on rock-breaking effect, and the difficulty in achieving adaptive optimization of electrode morphology for different working conditions. It can directly output the rock-breaking effect prediction and evaluation results under given working conditions and predetermined rock-breaking targets, and achieve a fine characterization of the correlation between electrode morphology and rock-breaking effect. This provides a basis for the quantitative optimization of parameters such as the radius of curvature of the electrode tip, the sharpness of the tip, the chamfer size of the tip, the asymmetry of the tip, and the surface roughness of the tip. It effectively improves the controllability and rock-breaking efficiency of the pulse discharge rock-breaking process, reduces blind experiments and repeated adjustments, and lowers engineering debugging costs. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments 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 flowchart of a rock-breaking method for adaptive control of electrode morphology under high-voltage electric pulse, provided in an embodiment of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0012] like Figure 1 As shown, this invention provides a rock-breaking method with adaptive control of electrode morphology under high-voltage electric pulse, comprising: Step 100: Obtain the raw data of the rock breaking process and perform time synchronization processing, noise reduction processing and normalization processing on the raw data of the rock breaking process to obtain the preprocessed dataset of the rock breaking process; Step 200: Construct an electrical breakdown channel model, and simulate the growth behavior of the electrical breakdown plasma channel during the high-voltage electric pulse rock breaking process based on the electrical breakdown channel model to obtain the electrical breakdown channel characteristic parameter set; Step 300: Extract energy characteristic parameters, drilling characteristic parameters, and fracture morphology characteristic parameters from the rock breaking process preprocessing dataset, and perform a weighted combination of the energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters, and electrical breakdown channel characteristic parameter set to obtain a comprehensive evaluation parameter set; Step 400: Perform operating condition deviation assessment on the comprehensive evaluation parameter set to obtain the operating condition deviation index; Step 500: Obtain the geometric morphology parameters of the high-voltage electrode end, and construct a correlation model between electrode morphology and rock-breaking effect based on the geometric morphology parameters of the high-voltage electrode end and the comprehensive evaluation parameter set; Step 600: Input the operating condition deviation index into the correlation model to obtain the morphological control criterion result; Step 700: Generate an electrode morphology control instruction set based on the morphology control criterion result to adjust the geometry of the high-voltage electrode end, and implement high-voltage electric pulse rock breaking under the adjusted high-voltage electrode end geometry.

[0013] Furthermore, in step 100 of this embodiment, the raw data of the rock breaking process is first obtained. Specifically, during the pulse discharge rock breaking test or on-site construction, the discharge voltage data and discharge current data are continuously recorded using an electrical parameter acquisition device, the single discharge energy data is recorded using a matching energy statistics module, the drilling depth data and drilling speed data are obtained using a drilling displacement and time acquisition device, and the rock cuttings characteristic data and pore size characteristic data are obtained using an image acquisition device and a particle size analysis device. The above-mentioned multi-source raw data are uniformly stored in the order of acquisition time to form a raw dataset of the rock breaking process containing multiple physical quantities.

[0014] In this embodiment, after completing the acquisition of the original data of the rock breaking process, the data from different sampling channels are processed for time synchronization. Specifically, with the discharge trigger signal time as a unified reference, the time axes corresponding to the discharge voltage, discharge current, single discharge energy, drilling depth, drilling speed, cuttings characteristics, and borehole diameter characteristics are aligned. Data from different sampling frequencies are unified to a preset time step through interpolation resampling, and data segments with severe missing parts or incorrect timestamps are deleted. This results in time-series data of the rock breaking process that can be directly compared and correlated under a unified time scale.

[0015] After time synchronization is completed, this embodiment performs denoising and normalization processing on various types of synchronized data. The denoising processing uses frequency domain or time domain filtering to suppress acquisition noise and instantaneous spikes for high-frequency signals such as voltage and current. For signals such as drilling depth, drilling speed, rock cuttings characteristics, and borehole diameter characteristics, smoothing or outlier removal methods are used to reduce occasional interference. The normalization processing linearly scales or standardizes various types of data according to a preset scale to address the differences in the numerical range of different physical quantities, so that various signals have a uniform numerical magnitude and comparability at the same time step. Finally, a preprocessing dataset of the rock breaking process is formed for subsequent feature extraction and model construction.

[0016] Furthermore, in step 200 of this embodiment, a three-dimensional matrix model corresponding to the rock-breaking area is first established in the simulation software. The rock-breaking space is divided into several regular grid cells along three coordinate directions, and each cell represents a discrete spatial cell in the granite medium. By specifying the dielectric constant, conductivity, resistivity, and other electrical parameters of the granite medium for each cell and applying boundary conditions consistent with the actual discharge conditions, a three-dimensional discrete medium model that can reflect the spatial electric field distribution characteristics of the rock-breaking area is obtained. In this embodiment, one or more cells adjacent to the electrode surface in the region near the high-voltage electrode end are set as the starting point of electric tree development. Two working condition-related physical parameters, the growth critical field strength and the threshold breakdown field strength, are introduced into the three-dimensional matrix. Combined with the discharge voltage data and discharge current data extracted from the preprocessing dataset of the rock-breaking process, the electric field strength and potential at the starting point of electric tree development and the surrounding points to be developed are obtained by solving the potential distribution in the discrete medium. This forms initial field distribution data containing the electric field strength and potential at the starting point of electric tree development and the surrounding points to be developed, which is used to characterize the initial discharge breakdown environment.

[0017] In this embodiment, after obtaining the initial field distribution data, all cells within the neighborhood of the starting point of electric tree development and satisfying the requirement that the electric field strength reaches the critical growth field strength are added to the set of points to be developed. Using a pre-constructed electrical breakdown channel model, the growth probability of each point to be developed being selected as the next development point is calculated based on the difference between the local electric field strength and the threshold breakdown field strength at each point to be developed and the electrical parameters of the granite dielectric of the corresponding cell. Thus, the current set of development points for electric trees is determined from the set of points to be developed. In this embodiment, during the growth of electrical trees, a new starting point for electrical tree development is randomly selected from the current set of development points. This new development point is then spatially connected to the starting point of electrical tree development in the previous time step. The developed channels are marked in the three-dimensional matrix, and the spatial coordinates of the new development point are written into the development point recording matrix. Simultaneously, the potential and electric field intensity of the surrounding cells in the next time step are recalculated with the new development point as the center, and the field distribution of each cell in the three-dimensional matrix is ​​updated. This yields the updated development point recording matrix that records the growth path and range of electrical trees, as well as the corresponding temporal field distribution data. This allows for the gradual simulation of the growth behavior of electrically broken plasma channels during high-voltage electric pulse rock breaking.

[0018] In this embodiment, during the simulation of electrical tree growth, growth termination conditions are set based on the development point recording matrix and time-series field distribution data. The determination of whether to terminate electrical tree growth is based on factors such as whether the electrical tree growth path reaches a preset spatial boundary, whether the total number of development steps exceeds a preset upper limit, whether the overall equivalent resistance of the channel drops to near a preset threshold, and whether the electric field strength at the channel end significantly decreases. When any of these conditions is met, this embodiment stops selecting new development points and records the current channel morphology and field distribution as the final growth state of the electrical tree. Based on this final growth state, this embodiment performs statistical analysis on the electrical tree growth path and range recorded in the development point recording matrix, extracting multi-dimensional feature parameters including the total length of the electrical breakdown channel, spatial distribution range, path curvature, number of channel branches, average diameter of the main channel, and changes in equivalent resistance along the channel direction. This forms a set of electrical breakdown channel feature parameters for subsequent electrode morphology optimization and rock-breaking effect evaluation.

[0019] Specifically, in constructing the electrical breakdown channel model during high-voltage electric pulse rock breaking, this embodiment employs a growth probability calculation method based on the combined effect of local electric field strength and equivalent resistivity of the medium to quantitatively describe the channel growth behavior of electrical trees in granite media. Specifically, this embodiment uses several spatial units near the high-voltage electrode as the starting points for electrical tree development, and within each time step, defines the spatial units adjacent to the current electrical tree front as a set of development points. For each spatial unit in the set of development points, this embodiment calculates the growth probability of it being selected as a new development point for electrical trees based on the difference between the local electric field strength at that unit and a pre-set threshold breakdown field strength, and the ratio between the equivalent resistivity of the medium corresponding to that unit and a reference resistivity constant. If the local electric field strength at a certain development point is lower than the threshold breakdown field strength, the growth probability of that point is forcibly set to 0 using a step function to ensure that the electrical trees only continue to grow in areas where the electric field conditions meet the breakdown requirements, making the entire channel growth process conform to the physical laws of high-voltage electric pulses in non-uniform media. This embodiment calculates the growth probability of all points to be developed around the current development point and performs normalization processing so that the sum of the growth probabilities of all points to be developed is 1. Thus, in each time step, one or more points to be developed are randomly selected as new electric tree development points according to the above growth probabilities, thereby simulating the growth process of the discharge breakdown channel in space, which is random and constrained by the local electric field and dielectric properties.

[0020] In this embodiment, the parameters involved in the growth probability calculation are clearly derived and their values ​​are controllable. The local electric field strength is derived from the numerical solution of the potential distribution in the three-dimensional discrete granite medium model. This potential distribution is determined by the discharge voltage and discharge current data in the preprocessing dataset of the rock breaking process, as well as the electrical parameters of the granite medium. The threshold breakdown field strength is an empirical parameter determined by combining the experimental test results of granite materials and publicly available literature data. It is used to characterize the minimum electric field strength required for the granite medium to change from an insulating state to a conductive state. For example, after obtaining the breakdown initiation field strength range by gradually increasing the pulse voltage on a set of typical granite samples, the median value in this range is taken as the threshold breakdown field strength, and it remains unchanged under different working conditions with the same lithology. The equivalent resistivity is used to characterize the overall conductivity of the granite medium in each discrete spatial unit within the rock-breaking area. It can be estimated based on factors such as rock porosity, water content, and fracture development, through rock sample resistivity testing or empirical correlation models. Numerically, it can be set to several representative levels to reflect the spatial non-homogeneity of the medium. The reference resistivity constant is a scaling parameter used to measure the average conductivity level of the granite. It can be selected as the arithmetic mean of the resistivity of all spatial units in the rock-breaking area or the resistivity of a representative rock sample measured under standard conditions. It is used to relatively calibrate the resistivity differences between different spatial units. For example, the reference resistivity can be taken as the median of the measured resistivity distribution under a typical working condition. Through these settings, the growth probability automatically decreases in areas where the equivalent resistivity is higher than the reference resistivity constant, while growth is more easily formed and expanded in low-resistivity channels.

[0021] In this embodiment, the set of potential growth points is a set of spatial cells adjacent to the leading edge of the current electric tree that have the potential to grow. It consists of cells in the three-dimensional discrete matrix that are not yet occupied by channels and have face or edge adjacency with the end cells of the current channel. Specifically, it can be limited to cells whose coordinates differ from the current growth point by no more than one grid step. The step function in this embodiment is a logical judgment function that only outputs 0 or 1. Its input is the difference between the local electric field strength at the potential growth point and the threshold breakdown field strength. When the difference is greater than or equal to 0, the step function outputs 1, corresponding to retaining the growth possibility of the potential growth point; when the difference is less than 0, the step function outputs 0, corresponding to directly setting the growth probability of the point to 0, thereby numerically achieving automatic screening of regions that do not meet the breakdown condition. In this embodiment, the potential field distribution can be established first using a three-dimensional matrix and a preprocessing dataset of the rock breaking process. Then, the local electric field intensity of each point to be developed can be calculated based on the potential gradient at each time step. Subsequently, the growth probability is calculated point by point based on the above parameter relationship, and a random number generator is used to select new development points according to these growth probabilities. The electric field distribution and the set of points to be developed are updated cyclically until the preset growth termination condition is met.

[0022] More specifically, in a set of specific numerical embodiments, this embodiment can take the working condition of drilling a typical granite sample horizontally with a hole diameter of about 50 mm and a discharge gap of about 10 mm as the object: the local electric field intensity is taken from the potential distribution obtained by simulation, for example, at a certain point to be developed near the current electric tree front, its local electric field intensity can be 8.0 × 10 6 The threshold breakdown field strength is derived from the results of pulse breakdown tests on granites of the same lithology. For example, in multiple tests, the breakdown initiation field strength was found to be mainly concentrated in (3.5–4.5) × 10⁻⁶. 6 For the volt-per-meter range, 4.0 × 10⁻⁶ can be selected. 6 The threshold breakdown field strength of the granite medium is defined as volts per meter (V / m). The equivalent medium resistivity is obtained by measuring the volume resistivity of rock samples within the fractured area. For example, in an area with a water content of approximately 1% and moderately developed fractures, the equivalent medium resistivity corresponding to the spatial unit where the point to be developed is located can be taken as 1.0 × 10⁻⁶. 6 The ohm-meter constant is used, while the reference resistivity constant is taken as the spatial average value obtained from the resistivity of a large number of rock samples in the same fractured region, for example, 2.0 × 10⁻⁶. 5 Ohm-meter is used to characterize the average conductivity level of the granite medium; the set of all potential development points around the current development point can be composed of several elements adjacent to the current electric tree front element in a three-dimensional mesh, for example, 20 potential development points enter this set within a certain time step; the input of the step function is the difference between the local electric field strength and the threshold breakdown field strength, for example, the local electric field strength of the aforementioned potential development point is 8.0 × 10⁻⁶. 6 The voltage per meter and the threshold breakdown field strength are 4.0 × 10⁻⁶. 6 If the difference is 4.0 × 10^6 volts per meter, which is greater than 0, the output of the step function is 1, indicating that the point to be developed has the potential to continue growing; if the local electric field strength at a certain point to be developed is only 3.0 × 10^6 volts per meter, then the difference is 4.0 × 10^6 volts per meter, which is greater than 0. At this point, the output of the step function is 1, indicating that the point to be developed has the potential to continue growing; 6 If the difference is volts per meter, then the value is -1.0 × 10⁻⁶. 6 If the value per meter is less than 0, the corresponding step function output is 0. In this embodiment, the growth probability of this point is directly set to 0 and removed from the candidates.

[0023] Specifically, the characteristic parameter set of the electrical breakdown channel includes: Statistical results on the channel length, number of channel branches, and whether the electrical tree comes into contact with the negative electrode.

[0024] Furthermore, in step 300 of this embodiment, an energy characteristic parameter set is first constructed based on the preprocessed dataset of the rock-breaking process. Specifically, using the preprocessed discharge voltage data, discharge current data, and single discharge energy data, the single discharge energy is calculated within each pulse discharge cycle, and the single discharge energy is accumulated over the entire rock-breaking process time to obtain the cumulative discharge energy. Further, combining the rock-breaking volume at the bottom of the hole or the drilling footage, the cumulative discharge energy is divided by the corresponding rock-breaking volume to obtain the rock-breaking energy consumption per unit volume. Simultaneously, using the theoretical or empirical energy consumption of mechanical rock breaking as a reference, the effective proportion of electrical energy input actually used for rock breaking is calculated to obtain the energy utilization rate. In this embodiment, indicators such as single discharge energy, cumulative discharge energy, rock-breaking energy consumption per unit volume, and energy utilization rate are linearly scaled or standardized according to a preset normalization interval, so that their values ​​fall within the same order of magnitude and the influence of dimensions is eliminated, thereby forming an energy characteristic parameter set that can characterize the energy input and utilization characteristics of high-voltage electric pulse rock breaking.

[0025] This embodiment, based on the constructed energy characteristic parameter set, further extracts drilling characteristic parameters and fracture morphology characteristic parameters from the preprocessed rock-breaking dataset. Specifically, based on the preprocessed drilling depth and drilling speed data, this embodiment calculates the drilling footage, average drilling speed, and instantaneous drilling speed fluctuation amplitude under a specified time window or a specified number of discharges. Combined with information such as input energy or axial load, it calculates the mechanical specific energy and rock-breaking efficiency to characterize the energy utilization and rock-breaking output efficiency of the drilling process. The aforementioned drilling footage, drilling speed, mechanical specific energy, rock-breaking efficiency, and drilling stability indicators are also normalized to form a drilling characteristic parameter set. Meanwhile, this embodiment utilizes preprocessed rock cuttings characteristic data and pore size characteristic data to statistically analyze the rock cuttings particle size distribution, obtaining parameters such as the mean particle size, standard deviation of particle size, and proportion of fine-grained components. Based on pore wall images or measurement data, it extracts indicators such as pore wall fragmentation range, crack propagation scale, and fragmentation uniformity. After normalization, a set of fragmentation morphology characteristic parameters is formed to quantitatively describe the differences in spatial fragmentation degree and uniformity of rock breaking results.

[0026] This embodiment, after obtaining the sets of energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters, and electrical breakdown channel characteristic parameters, performs feature alignment and dimensional unification processing on these features. First, based on the physical meaning and dimensions of different features, they are rearranged into multi-dimensional feature vectors with a fixed order. Then, features with inconsistent time scales are aligned to a unified evaluation time or discharge count index through interpolation or time window averaging, constructing a multi-dimensional feature input vector for the corresponding working condition. Based on this, this embodiment pre-determines the weight coefficients of energy features, drilling features, fracture morphology features, and electrical breakdown channel features according to expert experience or data-driven methods. Weighted summation is then performed on the corresponding dimensions of the multi-dimensional feature input vector to obtain a comprehensive evaluation parameter set that comprehensively characterizes the coupling relationship between rock-breaking energy consumption, drilling performance, fracture results, and discharge channel morphology. This provides a unified and quantitative evaluation basis for the subsequent construction and optimization of the electrode morphology-rock-breaking effect correlation model.

[0027] Furthermore, in step 400 of this embodiment, a target working condition benchmark evaluation parameter set is first constructed based on the preset target rock breaking effect requirements and historical data under typical excellent working conditions. Specifically, this embodiment selects several sets of working condition data with excellent rock breaking effect, high drilling efficiency, and reasonable energy utilization obtained under field or experimental conditions. Using the energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters, and electrical breakdown channel characteristic parameters obtained in the aforementioned steps, statistical analysis is performed on each type of characteristic under these excellent working conditions to obtain its mean, standard deviation, and reasonable fluctuation range. All types of characteristics are then uniformly normalized to make each characteristic numerically comparable. Based on this, this embodiment uses the normalized characteristic mean in the excellent working condition sample as the benchmark value of the target working condition, and sets the allowable fluctuation range of each characteristic according to the standard deviation and engineering experience, thereby forming a target working condition benchmark evaluation parameter set used to measure the degree of similarity between the actual working condition and the target working condition.

[0028] This embodiment, after obtaining the comprehensive evaluation parameter set and the target working condition benchmark evaluation parameter set, quantifies the differences between various features in the current working condition and the target working condition item by item, constructing a feature deviation set. Specifically, in this embodiment, on the same feature dimension, the absolute difference of the feature is obtained by subtracting the benchmark value of the corresponding feature in the target working condition benchmark evaluation parameter set from the feature value of the current working condition in the comprehensive evaluation parameter set. The relative deviation of the feature is then calculated by the ratio of the absolute difference to the target benchmark value or to a pre-set allowable fluctuation range, reflecting the degree to which the current working condition deviates from the target working condition. The above calculations are applied to each dimension of the energy feature parameter, drilling feature parameter, fracture morphology feature parameter, and electrical breakdown channel feature parameter, respectively, to obtain a feature deviation set including energy deviation, drilling deviation, fracture morphology deviation, and electrical breakdown channel deviation, providing basic data for subsequent deviation aggregation and working condition evaluation.

[0029] After obtaining the set of characteristic deviations, this embodiment assigns corresponding weights based on the varying degrees of influence of different characteristics on the overall rock-breaking effect, and then performs weighted aggregation on various deviations. Specifically, this embodiment first assigns different weight coefficients to energy utilization-related characteristics, drilling efficiency and stability-related characteristics, fracture morphology-related characteristics, and electrical breakdown channel morphology-related characteristics, based on the design goals and experience of the rock-breaking task. Then, it performs a weighted average of the multi-dimensional characteristic deviations within the same category to obtain the category aggregation results of energy deviation, drilling deviation, fracture morphology deviation, and electrical breakdown channel deviation. Subsequently, it performs a second weighted summation of the above-mentioned category deviations according to their importance to obtain the condition deviation aggregation result reflecting the overall deviation of the current working condition from the target working condition. Based on this, this embodiment maps the condition deviation aggregation result into quantitative or graded condition deviation characterization quantities according to a preset grading threshold. For example, it classifies the deviation results into acceptable, moderate, and severe levels, and outputs corresponding condition deviation indices to guide subsequent adjustment and optimization of electrode structure or discharge parameters.

[0030] Furthermore, in step 500 of this embodiment, the geometric morphology parameters of the high-voltage electrode tip are first obtained to establish a quantitative correlation between electrode morphology and rock-breaking effect. Specifically, for the high-voltage electrode participating in high-voltage pulse discharge rock breaking, this embodiment extracts geometric morphology parameters such as radius of curvature, tip sharpness, tip chamfer size, tip asymmetry, and tip surface roughness of the electrode tip through design drawing data, 3D modeling data, or actual measurement data. Among them, the radius of curvature is used to characterize the curvature of the electrode tip surface; the tip sharpness is used to quantitatively represent the degree of transition from a sharp to a blunt shape; the tip chamfer size is used to describe the width and height of the edge transition region of the tip; the tip asymmetry is used to characterize the degree of eccentricity or shape difference of the electrode tip shape relative to the electrode axis in different directions; and the tip surface roughness is used to reflect the level of micro-undulations of the tip surface. The above geometric morphology parameters can be recorded as a geometric feature vector of the electrode tip in a unified numerical form for joint modeling with the comprehensive evaluation parameter set.

[0031] In this embodiment, after obtaining the geometric morphology parameters of the high-voltage electrode tip, the geometric feature vector is paired and aligned with the comprehensive evaluation parameter set obtained in the preceding steps to form multiple sets of "electrode tip morphology - rock breaking effect" sample data. Specifically, this embodiment conducts a series of high-voltage electric pulse rock breaking tests or numerical simulations under the same or comparable working conditions for different electrode tip structure schemes. Each set of test or simulation working conditions corresponds to a specific set of electrode tip geometric morphology parameters and a comprehensive evaluation parameter set obtained by weighting energy characteristics, drilling characteristics, fracture morphology characteristics, and electrical breakdown channel characteristics. This embodiment uses the electrode tip geometric morphology parameters as input features and one or more indicators used in the comprehensive evaluation parameter set to measure rock breaking energy consumption, drilling performance, fracture results, and discharge channel behavior as output responses. By organizing a large number of sample working conditions, a training sample library covering typical structural forms and typical working condition ranges is constructed, providing sufficient data support for the subsequent establishment of correlation models.

[0032] This embodiment constructs a correlation model between electrode morphology and rock-breaking effect based on the aforementioned sample library. Specifically, it can employ multiple regression models, weighted linear models, or data-driven nonlinear models to achieve the ability to predict the comprehensive rock-breaking effect from the geometric morphology parameters of the electrode tip. In one optional implementation, this embodiment selects the radius of curvature, tip sharpness, tip chamfer size, tip asymmetry, and tip surface roughness as input variables. Parameter calibration is used to determine the influence coefficients and sensitivities of each geometric parameter on different indicators in the comprehensive evaluation parameter set, thereby forming an explicitly analytically expressible correlation. In another optional approach, this embodiment can utilize existing training samples to train a nonlinear electrode morphology-rock-breaking effect mapping model using machine learning algorithms. This model is used to capture the comprehensive influence of complex geometric changes on electric field distribution, electrical breakdown channel morphology, and the final rock-breaking effect. Through the aforementioned correlation model, this embodiment can predict the corresponding comprehensive rock-breaking evaluation results given a set of high-voltage electrode tip geometric morphology parameters. Conversely, it can also back-calculate or optimize the geometric morphology parameters of the electrode tip given a target rock-breaking effect requirement, providing quantitative guidance for high-voltage electrode structure design and rock-breaking process optimization.

[0033] Specifically, in this embodiment, when constructing the correlation model between electrode morphology and rock breaking effect, the predicted evaluation result of rock breaking effect output by the model is represented as a multi-dimensional evaluation vector, which is used to simultaneously characterize the comprehensive effect of multiple aspects such as rock breaking energy consumption level, drilling performance, fracture morphology quality, and electrical breakdown channel behavior. This multi-dimensional evaluation vector is obtained by linear combination of three types of input information, namely, the geometric morphology parameter vector of the high-voltage electrode end, the current working condition evaluation parameter vector extracted from the comprehensive evaluation parameter set, and the target working condition benchmark evaluation parameter vector corresponding to the target rock breaking effect. Specifically, in this embodiment, the curvature radius, tip bluntness, chamfer size, asymmetry, and surface roughness of the high-voltage electrode tip geometry parameters are arranged in a fixed order to form a high-voltage electrode tip geometry parameter vector. Normalized indices used to characterize energy utilization, drilling performance, fracture morphology, and electrical breakdown channel features are organized into a current working condition evaluation parameter vector. The target working condition benchmark evaluation parameter set obtained based on historical excellent working condition statistics is organized into a target working condition benchmark evaluation parameter vector. Based on this, this embodiment configures corresponding coefficient matrices for the high-voltage electrode tip geometry parameter vector, the current working condition evaluation parameter vector, and the target working condition benchmark evaluation parameter vector. Through the multiplication and summation of matrices and vectors, a rock-breaking effect prediction evaluation vector is obtained, enabling quantitative prediction of the comprehensive rock-breaking effect under given electrode tip morphology and working conditions.

[0034] In the aforementioned correlation model, the rock-breaking effect prediction evaluation vector is the output of the model. Its dimension is determined by the number of evaluation indicators selected in this embodiment. For example, it may include energy consumption indicators for evaluating the energy consumption per unit volume of rock breaking, speed and stability indicators for evaluating drilling efficiency and drilling stability, fracture morphology indicators for evaluating the distribution of rock cuttings and the range of borehole wall fracture, and channel characteristic indicators for characterizing the length, branching degree, and directionality of the electrical breakdown channel. In implementation, these normalized evaluation indicators can be combined into a multi-dimensional vector in a fixed order. The high-voltage electrode end geometric morphology parameter vector is one of the inputs. Its components are geometric morphology parameters such as radius of curvature, end bluntness, end chamfer size, end asymmetry, and end surface roughness obtained through design or measurement. These parameters can be assigned actual values ​​according to the specific electrode structure. For example, the radius of curvature can be taken as a measured or designed value in the range of a few millimeters to tens of millimeters, the end bluntness can be represented by a dimensionless shape coefficient, the end chamfer size can be represented by the length value of the chamfer height or width, the end asymmetry can be represented by the eccentricity distance or the percentage of shape difference, and the end surface roughness can be represented by the profile arithmetic mean deviation value. The current working condition evaluation parameter vector is derived from the aforementioned comprehensive evaluation parameter set. The specific values ​​are obtained by weighting and organizing the energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters, and electrical breakdown channel characteristic parameters of the current rock breaking test or field working condition according to preset rules. Each component is a dimensionless value after normalization, and its magnitude reflects the strength of the working condition in a certain evaluation dimension. The target working condition benchmark evaluation parameter vector is obtained by statistical averaging and normalization of one or more sets of data identified as excellent working conditions. Each component represents the target or expected level under the same evaluation dimension and is used to provide a benchmark for the model.

[0035] In this embodiment, the coefficient matrices used to map the geometric morphology parameter vector of the high-voltage electrode tip to the rock-breaking effect prediction and evaluation vector, the coefficient matrices used to map the current working condition evaluation parameter vector to the rock-breaking effect prediction and evaluation vector, and the coefficient matrices used to map the target working condition benchmark evaluation parameter vector to the rock-breaking effect prediction and evaluation vector are all obtained through parameter calibration of a large number of sample data. Specifically, in this embodiment, multiple sets of samples are collected under different electrode tip shapes and different discharge and drilling conditions. Each set of samples includes a set of geometric morphology parameter vectors, a set of current working condition evaluation parameter vectors, a set of target working condition benchmark evaluation parameter vectors, and the corresponding actual rock-breaking effect evaluation results. These samples are input into a preset linear model structure, and the values ​​of each element in the above three coefficient matrices are obtained by parameter estimation methods such as least squares fitting or regularized fitting. In practical applications, for example, the energy utilization-related output components can be set to have high positive or negative weights for the radius of curvature and the bluntness of the end, and moderate weights for the surface roughness of the end. This way, when the radius of curvature decreases and the end becomes sharper, the predicted electric field concentration increases, and the corresponding energy utilization indicators are improved in the model output. Similarly, the drilling stability-related output components can give a large weight to the end asymmetry, so that when the end shape is eccentric, the predicted drilling stability indicators are significantly reduced in the model output.

[0036] More specifically, this embodiment can set the following values ​​for a certain high-voltage electric pulse rock breaking test condition: the rock breaking effect prediction evaluation vector can include three normalized evaluation indicators: energy utilization, drilling efficiency, and breaking uniformity, for example, 0.80, 0.75, and 0.70 respectively; the high-voltage electrode end geometric morphology parameter vector consists of the radius of curvature, end bluntness, end chamfer size, end asymmetry, and end surface roughness, for example, the radius of curvature is 5.0 mm, and the end bluntness is represented by a dimensionless shape coefficient. The end chamfer size is 1.0 mm, the end asymmetry is expressed as eccentricity as 0.10, and the end surface roughness is 3.2 micrometers. The current working condition evaluation parameter vector is derived from the comprehensive evaluation parameter set. After normalizing the indicators such as unit volume rock breaking energy consumption, drilling footage and drilling stability, rock cuttings particle size uniformity, and electrical breakdown channel extension, they can be taken as 0.65, 0.70, 0.60, and 0.55, respectively. The target working condition benchmark evaluation parameter vector is derived from the statistical results of historical excellent working conditions, corresponding to the same The target values ​​for the evaluation dimensions can be set to 0.90, 0.85, 0.80, and 0.75 respectively, serving as benchmarks. The coefficient matrix for mapping the geometric parameter vectors of the high-voltage electrode ends to the rock-breaking effect prediction evaluation vector can be, for example, a 3x5 real number matrix, where each element is obtained from sample fitting. For instance, the first row might contain 0.20, 0.15, 0.10, -0.05, and -0.08, used to characterize the linear influence of each geometric parameter on the energy utilization index. The current operating condition evaluation parameter vector can be mapped... The coefficient matrix of the rock-breaking effect prediction evaluation vector can be set as a 3-row, 4-column real number matrix. For example, the second row is 0.10, 0.25, 0.20, and 0.15, which is used to reflect the contribution of each working condition evaluation dimension to the drilling efficiency prediction value. The coefficient matrix of the target working condition benchmark evaluation parameter vector to the rock-breaking effect prediction evaluation vector can be set as a 3-row, 4-column real number matrix. For example, the third row is 0.05, 0.10, 0.15, and 0.20, which is used to introduce the guiding effect of the target working condition into the model output.

[0037] Furthermore, in step 600 of this embodiment, the working condition deviation index and the target working condition information are processed in a unified manner to construct an input quantity for morphology control. Specifically, based on the working condition deviation index obtained in the aforementioned steps, this embodiment normalizes quantities such as energy deviation, drilling deviation, fracture morphology deviation, and electrical breakdown channel deviation, making different deviation quantities comparable within the same numerical range, while retaining the directional information of the deviation to distinguish whether the deviation from the target working condition is too large or too small. On this basis, this embodiment combines the normalized working condition deviation index with the target working condition benchmark evaluation parameter set corresponding to the target rock breaking effect in a preset order, using the target working condition benchmark evaluation parameter set as the "desired state" quantity and the working condition deviation index as the "current deviation degree" quantity, splicing them together to form a morphology control evaluation input vector corresponding to the current working condition, providing a unified input for subsequent analysis of the impact of electrode end morphology adjustment on rock breaking effect through an association model.

[0038] This embodiment, after obtaining the morphology control evaluation input vector, utilizes the aforementioned constructed electrode morphology and rock-breaking effect correlation model to predict the changing trend of the comprehensive evaluation parameter set caused by changes in the geometric morphology parameters of different high-pressure electrode ends, thus obtaining morphology sensitivity prediction results. Specifically, this embodiment, while keeping the current working condition evaluation parameters and the target working condition benchmark evaluation parameters unchanged, applies small positive or negative perturbations to parameters such as radius of curvature, end bluntness, end chamfer size, end asymmetry, and end surface roughness around the value points of the existing high-pressure electrode end geometric morphology parameters. The perturbed geometric morphology parameters and the morphology control evaluation input vector are input into the correlation model together, and the difference and changing trend of the output rock-breaking effect prediction evaluation vector before and after the perturbation are calculated. By comparing the increase or decrease of the prediction evaluation results under different perturbation directions and amplitudes, this embodiment can obtain the sensitivity and influence direction of each high-pressure electrode end geometric morphology parameter to each index in the comprehensive evaluation parameter set, forming morphology sensitivity prediction results to indicate which geometric parameter adjustments can effectively improve the rock-breaking effect and the degree of improvement under the current working condition deviation background.

[0039] After obtaining the morphology sensitivity prediction results, this embodiment introduces a preset optimization objective function and constraints to comprehensively analyze and determine the adjustment direction and range of the geometric morphology parameters at the ends of each high-voltage electrode, ultimately forming a morphology control criterion result that can be directly used for structural adjustment design. Specifically, this embodiment sets optimization objective functions according to different application scenarios, such as reducing unit volume rock-breaking energy consumption, improving drilling efficiency, improving crushing uniformity, and improving electrical breakdown channel stability as the main objectives. Simultaneously, engineering constraints are set, including the curvature radius variation range, end chamfer size processing limits, end asymmetry process tolerance, and achievable end surface roughness levels. Based on this, this embodiment combines the morphology sensitivity prediction results to determine whether positive or negative adjustments to each geometric morphology parameter are conducive to achieving the optimization objectives, and provides recommended adjustment ranges under the premise of meeting the constraints, such as suggesting appropriately reducing the curvature radius, slightly reducing end asymmetry, or optimizing the end surface roughness level. Based on the above analysis, this embodiment summarizes the complex model prediction results into a set of clear morphological control criteria, which guides engineers to adjust the geometric morphological parameters of the high-voltage electrode end in a targeted manner during the actual electrode design and modification process, thereby achieving quantitative optimization control of the rock breaking effect.

[0040] Furthermore, in step 700 of this embodiment, based on the aforementioned morphology control criteria results, the preferred adjustment direction and adjustment range of each geometric morphology parameter at the high-voltage electrode end are analyzed and discretized to form a set of target adjustment amounts that can be directly used for processing or replacement. Specifically, for parameters such as radius of curvature, end sharpness, end chamfer size, end asymmetry, and end surface roughness, this embodiment reads the adjustment direction ("increase" or "decrease") and the continuous numerical range of the "preferred adjustment interval" given in the morphology control criteria results. Combining the minimum resolution of the processing equipment, the achievable step size, and the process safety margin, the continuous adjustment interval is divided into several discrete candidate values. For example, the suggested interval for reducing the radius of curvature from 5mm to 3mm is subdivided into discrete target values ​​such as 4.5mm, 4mm, 3.5mm, and 3mm. The suggested interval for optimizing the end surface roughness from 3.2μm to 1.6μm is subdivided into discrete target levels such as 2.4μm and 1.6μm. Through the above discretization, this embodiment obtains a set of target adjustment amounts containing specific target adjustment amounts for each geometric parameter and their selectable gears, providing clear parameter input for subsequent generation of processing and replacement instructions.

[0041] In this embodiment, after obtaining the set of target adjustment values, the target values ​​of the geometric morphology parameters of each high-voltage electrode end and the corresponding machining accuracy requirements are converted into instructions that can be directly executed by the machining equipment or maintenance personnel, thus forming an electrode morphology control instruction set.

[0042] Specifically, this embodiment first assigns a reasonable machining tolerance range to each target parameter value based on the electrode material, structural form, and existing processing capabilities. For example, the machining allowable deviation is set to ±0.1mm for the target value of a radius of curvature of 3mm, ±0.05mm for the target value of an end chamfer size of 0.8mm, and the upper limit requirement of 1.6μm for the end surface roughness is set to no higher than 2.0μm. Subsequently, based on the above target values ​​and tolerance ranges, combined with the electrode three-dimensional structural model and fixture arrangement, this embodiment automatically generates parameter adjustment instructions and machining path instructions adapted to the CNC machine tool, including the feed path, feed speed, and depth of cut of the end face forming tool, as well as parameters such as the grinding wheel grit size, polishing pressure, and polishing time of the polishing process. When the processing capability cannot be achieved or the cost is too high, this embodiment can also provide a replacement electrode scheme in the instruction set, such as directly selecting a prefabricated standardized end geometry electrode assembly and marking its model and assembly requirements.

[0043] This embodiment guides the actual high-voltage electrode end processing and replacement operations after obtaining the electrode morphology control instruction set, and implements the high-voltage electric pulse rock breaking process under the adjusted structural conditions to achieve closed-loop verification and optimization.

[0044] Specifically, this embodiment first performs mechanical cutting, chamfering, and fine polishing on the end of the existing high-voltage electrode on a CNC lathe, CNC grinding machine, or other precision machining equipment, according to the electrode morphology control instruction set, so that the radius of curvature, chamfer size, end asymmetry, and surface roughness of the electrode end meet the range specified in the target adjustment set. For cases where the instruction set provides an electrode replacement scheme, this embodiment completes the disassembly of the old electrode and the installation and positioning of the new electrode according to the instructions, ensuring that the concentricity of the electrode axis and the installation preload meet the working condition requirements. After machining or replacement, this embodiment performs necessary dimensional and surface quality inspections on the adjusted high-voltage electrode end geometry. After confirming that it meets the requirements of the morphology control instruction set, a high-voltage electric pulse rock breaking test or on-site construction is re-implemented under the same discharge voltage, discharge frequency, drilling load, and medium conditions as the previously evaluated working conditions. Data such as discharge, electrical breakdown channels, drilling and fracture morphology are collected according to the aforementioned method, and a new comprehensive evaluation parameter set and working condition deviation index are calculated. By comparing the rock-breaking effects before and after the adjustment, this embodiment can verify the effectiveness of the electrode morphology control instruction set, and, when necessary, feed the newly acquired data back to the associated model and morphology control process to further iteratively optimize the geometric morphology design of the electrode end.

[0045] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0046] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse, characterized in that, include: The raw data of the rock breaking process is obtained and subjected to time synchronization processing, noise reduction processing and normalization processing to obtain the preprocessed dataset of the rock breaking process. An electrical breakdown channel model was constructed, and the growth behavior of the electrical breakdown plasma channel during the high-voltage electric pulse rock breaking process was simulated based on the electrical breakdown channel model to obtain the characteristic parameter set of the electrical breakdown channel. Energy characteristic parameters, drilling characteristic parameters, and fracture morphology characteristic parameters are extracted from the preprocessed dataset of the rock breaking process. The energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters, and electrical breakdown channel characteristic parameter set are then weighted and combined to obtain a comprehensive evaluation parameter set. The operating condition deviation index is obtained by performing operating condition deviation assessment on the comprehensive evaluation parameter set. Obtain the geometric morphology parameters of the high-voltage electrode end, and construct a correlation model between electrode morphology and rock-breaking effect based on the geometric morphology parameters of the high-voltage electrode end and the comprehensive evaluation parameter set; The operating condition deviation index is input into the correlation model to obtain the morphological control criterion result; Based on the morphology control criteria, an electrode morphology control instruction set is generated to adjust the geometry of the high-voltage electrode tip, and high-voltage electric pulse rock breaking is implemented under the adjusted high-voltage electrode tip geometry.

2. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The raw data of the rock-breaking process includes: Discharge voltage data, discharge current data, single discharge energy data, drilling depth data, drilling speed data, cuttings characteristic data, and borehole diameter characteristic data.

3. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The process involves constructing an electrical breakdown channel model and simulating the growth behavior of the electrical breakdown plasma channel during high-voltage electric pulse rock breaking, thereby obtaining a set of characteristic parameters for the electrical breakdown channel, including: A three-dimensional matrix corresponding to the rock-breaking area was created using simulation software; The discrete spatial units in the granite medium are represented by the cells in the three-dimensional matrix, and the growth critical field strength, threshold breakdown field strength and electrical parameters of the granite medium are introduced to construct an electrical breakdown channel model. In the three-dimensional matrix corresponding to the electrical breakdown channel model, the cell closest to the high-voltage electrode is set as the starting point of electrical tree development. Based on the growth critical field strength and the threshold breakdown field strength, combined with the discharge voltage and discharge current in the rock breaking process preprocessing dataset, the electric field strength and potential of the starting point of electrical tree development and the surrounding points to be developed are calculated, and the initial field distribution data containing the electric field strength and potential of the starting point of electrical tree development and the surrounding points to be developed are obtained. Based on the initial field distribution data, when the starting point of the electric tree development meets the condition that the electric field strength is greater than the threshold breakdown field strength, all possible development points around the starting point of the electric tree development are added to the set of points to be developed, and the development probability of each point to be developed in the set of points to be developed is calculated to obtain the current set of development points of the electric tree. Based on the current set of development points of the electric tree, a development point is randomly selected from the current developed points as a new development starting point of the electric tree. The new development point is connected to the current development starting point, and the coordinates of the new development point are stored in the development point recording matrix. At the same time, the electric field strength and electric potential at each cell in the three-dimensional matrix are updated to the values ​​at the next time step, thus obtaining the updated development point recording matrix that records the growth path and growth range of the electric tree and the corresponding time-series field distribution data. Based on the updated development point record matrix and the temporal field distribution data, during the electric tree growth process, it is determined whether to terminate the electric tree growth according to the preset growth termination condition, so as to obtain the final growth state of the electric tree. Based on the final growth state of the electrical tree, the growth paths and ranges of the electrical trees recorded in the development point recording matrix are statistically analyzed to obtain the electrical breakdown channel characteristic parameter set. The expression for the electrical breakdown channel model is: ； in, As a point of development The growth probability of being selected as a new development point for electric tree branches; As a point of development The local electric field intensity at that location; The threshold breakdown field strength of the granite medium; As a point of development The equivalent dielectric resistivity of the space cell in question; This is a reference resistivity constant used to characterize the average conductivity level of granite media; The set of all undeveloped points surrounding the current development point; For step function, when hour ,when hour .

4. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The characteristic parameter set of the electrical breakdown channel includes: Statistical results on the channel length, number of channel branches, and whether the electrical tree comes into contact with the negative electrode.

5. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The process involves extracting energy characteristic parameters, drilling characteristic parameters, and fracture morphology characteristic parameters from the preprocessed dataset of the rock breaking process, and then weighting and combining these parameters with the electrical breakdown channel characteristic parameters to obtain a comprehensive evaluation parameter set, including: Based on the discharge voltage data, discharge current data and single discharge energy data in the preprocessing dataset of the rock breaking process, the single discharge energy, cumulative discharge energy, rock breaking energy consumption per unit volume and energy utilization rate are calculated and normalized to obtain energy characteristic parameters. Based on the drilling depth and drilling speed data in the preprocessing dataset of the rock breaking process, the drilling footage, drilling speed, mechanical specific energy, rock breaking efficiency, and drilling stability are calculated and normalized to obtain drilling characteristic parameters. Based on the rock cutting feature data and pore size feature data in the preprocessing dataset of the rock breaking process, the rock cutting particle size distribution, pore wall fragmentation range, crack propagation scale and fragmentation uniformity are extracted and normalized to obtain a set of fragmentation morphology feature parameters. The energy characteristic parameters, drilling characteristic parameters, and fracture morphology characteristic parameters are aligned with the electrical breakdown channel characteristic parameter set and their dimensions are unified to obtain a multi-dimensional feature input vector. Based on the multidimensional feature input vector, the energy feature parameters, drilling feature parameters, fracture morphology feature parameters, and electrical breakdown channel feature parameters are weighted and summed according to the preset feature weights to obtain a comprehensive evaluation parameter set.

6. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The process of evaluating the operating condition deviation of the comprehensive evaluation parameter set to obtain the operating condition deviation index includes: Based on the preset target rock breaking effect requirements and historical data under typical excellent working conditions, statistical analysis and normalization were performed on energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters and electrical breakdown channel characteristic parameters to obtain the target working condition benchmark evaluation parameter set. Based on the comprehensive evaluation parameter set and the target working condition benchmark evaluation parameter set, the differences and relative deviations of energy characteristic parameters, drilling characteristic parameters, fracture morphology characteristic parameters and electrical breakdown channel characteristic parameters between the current working condition and the target working condition are calculated item by item to obtain the characteristic deviation set. Based on the set of characteristic deviations, according to the weights set for the degree of influence of different features on rock breaking effect, the energy deviation, drilling deviation, fracture morphology deviation and electrical breakdown channel deviation were weighted and summed to obtain the aggregated result of working condition deviations. Based on the aggregated results of the operating condition deviations, the aggregated results of the operating condition deviations are mapped into quantitative or hierarchical operating condition deviation characteristics according to a preset grading threshold, thereby obtaining the operating condition deviation index.

7. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The geometric parameters of the high-voltage electrode tip include: Radius of curvature, degree of end bluntness, end chamfer size, end asymmetry, and end surface roughness.

8. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The expression for the association model is: ； in, This is the rock-breaking effect prediction and evaluation vector output by the correlation model between electrode morphology and rock-breaking effect; This is a vector of geometric parameters at the end of the high-voltage electrode. This is the current working condition evaluation parameter vector obtained from the comprehensive evaluation parameter set; M is the target working condition benchmark evaluation parameter vector corresponding to the target rock breaking effect; N is the coefficient matrix that maps the geometric morphology parameters of the high-voltage electrode end to the rock breaking effect prediction evaluation vector; K is the coefficient matrix that maps the current working condition evaluation parameters to the rock breaking effect prediction evaluation vector.

9. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The operating condition deviation index is input into the correlation model to obtain the morphological control criterion result, including: Based on the working condition deviation index and the target working condition benchmark evaluation parameter set corresponding to the target rock breaking effect, the working condition deviation index is normalized and combined with the target working condition benchmark evaluation parameter set to obtain the morphological control evaluation input vector. Based on the correlation model between the electrode morphology and rock breaking effect, the morphology control evaluation input vector is input into the correlation model to obtain the prediction results of the influence of changes in geometric morphology parameters of different high-pressure electrode ends on the comprehensive evaluation parameter set, and thus obtain the morphology sensitivity prediction results. Based on the morphological sensitivity prediction results, the adjustment direction and adjustment range of the geometric morphological parameters of each high-voltage electrode end are analyzed and determined according to the preset optimization objective function and constraints, so as to obtain the morphological control criterion results.

10. The rock-breaking method with adaptive electrode morphology control under high-voltage electric pulse action according to claim 1, characterized in that, The step of generating an electrode morphology control instruction set based on the morphology control criterion result to adjust the geometry of the high-voltage electrode tip, and implementing high-voltage electric pulse rock breaking under the adjusted high-voltage electrode tip geometry, includes: Based on the morphological control criterion results, the preferred adjustment direction and adjustment range of the geometric morphological parameters at the end of the high-voltage electrode are analyzed and discretized to obtain a set of target adjustment amounts. Based on the target adjustment set, the target values ​​of the geometric parameters of each high-voltage electrode end and the processing accuracy requirements are converted into parameter adjustment instructions, processing path instructions or electrode replacement schemes that can be executed by the processing equipment, thus obtaining the electrode shape control instruction set. Based on the electrode shape control instruction set, the high voltage electrode end is machined, polished and replaced to obtain the adjusted geometry of the high voltage electrode end. Based on the adjusted geometry of the high-voltage electrode tip, a high-voltage electric pulse rock-breaking process is implemented under the same discharge conditions.

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