Deep metal mine recovery optimization method based on muscle-tendon bionics and thermodynamic entropy production

Abstract

The application discloses a deep metal mine recovery optimization method based on muscle-tendon bionics and thermodynamic entropy production, and belongs to the technical field of deep mining engineering. The method divides the stope into two types of bionics mechanical units corresponding to the energy storage characteristics of skeletal muscle and the force transmission dissipation characteristics of tendon, calibrates core parameters, constructs muscle-tendon coupled nonlinear biomechanics constitutive equation set; defines the stope as an open non-equilibrium thermodynamic system, constructs the internal irreversible entropy production rate equation and calibrates the critical entropy production rate; establishes a double-threshold joint catastrophe criterion containing static energy constraints and dynamic entropy production rate constraints, realizes stope risk quantitative zoning; constructs a force-heat collaborative multi-objective optimization function to solve the recovery parameter combination; and inverses the entropy production rate in real time based on the field microseismic monitoring data, adaptively corrects the recovery parameters by using the gradient iteration algorithm, forms a closed-loop regulation and control, and realizes the accurate prediction of the deep stope catastrophe risk and the scientific dynamic optimization of the recovery parameters.

Description

Technical Field

[0001] This invention relates to the field of deep mining engineering technology, specifically to an optimization method for deep metal ore mining based on muscle-tendon biomimicry and thermodynamic entropy production. More specifically, it relates to a pure process optimization method for deep hard rock stepwise ordered energy release mining that integrates muscle-tendon synergistic biomechanical constitutive model and non-equilibrium thermodynamic entropy production dissipation theory, full parameter quantitative calibration, and closed-loop iterative solution. Background Technology

[0002] After mining deep metal mines at depths of over 1,000 meters, the in-situ rock mass is placed in a non-equilibrium mechanical environment with superimposed ultra-high ground stress and strong excavation disturbance. During the excavation and unloading process, the instantaneous release of elastic strain energy accumulated in the rock mass can easily induce dynamic disasters such as rock bursts and rock spalling, which has become a key bottleneck restricting the safe and efficient mining of deep mineral resources.

[0003] Regarding the optimization of mining operations and disaster prevention under deep, high-stress conditions, existing technologies mainly focus on three aspects: mining process optimization, stress relief mining design, and energy dissipation analysis. In terms of mining process optimization, invention patent CN118774824A proposes a low-damage mining method for downward-approach stopes in deep metal mines. By optimizing the blasting parameters of the surrounding holes and improving the artificial false bottom laying process, it reduces the damage to the surrounding ore and rock caused by blasting vibrations during stope mining to a certain extent. In terms of stress relief mining, invention patent CN121007008A proposes a stress relief mining method for deep, high-stress, thick ore bodies. By arranging stress relief roadways and vertical trenches on the hanging wall of the ore body, the integrity of the surrounding rock is artificially weakened to form a continuous stress relief zone, achieving regional stress transfer. In terms of energy dissipation analysis, the invention patent with publication number CN121562194A proposes a method for dynamic energy consumption analysis and implementation of deep rock based on improved near-field dynamics, attempting to quantitatively evaluate the dynamic response of deep rock under excavation disturbance from the perspective of energy dissipation. The above-mentioned existing technologies have achieved certain results under specific engineering conditions, but still have the following common technical defects: (1) The determination of mining parameters lacks quantitative theoretical basis for multi-field coupling. The core parameters of existing technologies, such as mining sequence, mining step distance, and excavation interval, mainly rely on engineering analogy experience or numerical simulation of a single mechanical field to determine them. They fail to take into account the coupling effect between stress migration path, energy dissipation evolution and damage accumulation during the excavation process, making it difficult to achieve scientific quantification and dynamic optimization of mining parameters. (2) The disaster criteria fail to reflect the non-equilibrium thermodynamic nature of deep excavation. The existing dynamic disaster early warning criteria mostly use single indicators such as static elastic strain energy threshold or stress intensity ratio, ignoring that the unloading of deep excavation is essentially a non-equilibrium irreversible thermodynamic process accompanied by energy dissipation and entropy increase. Because the dynamic correlation between excavation disturbance rate, damage accumulation rate and entropy production mutation has not been quantified, the existing criteria for early warning of dynamic instability have obvious lag and risk of misjudgment. (3) Mechanical damage evolution and thermodynamic entropy production dissipation are separated. Although some technologies have attempted to introduce energy dissipation analysis, their mechanical and thermodynamic models are constructed independently, and the damage evolution equation and the non-equilibrium thermodynamic entropy production equation are not coupled and solved in a unified mathematical framework. The disconnect between force and heat leads to a distortion in the description of the evolution path of rock mass from local damage to overall instability, and the prediction accuracy is difficult to meet the actual needs of deep engineering.

[0004] In view of this, it is necessary to establish a systematic method that can uniformly describe the coupling relationship between the mechanical damage evolution of deep rock masses and the non-equilibrium thermodynamic entropy production and dissipation. This is to solve the technical problem that parameter optimization and disaster prevention in the deep metal mining process are still at the level of experience-driven or single physical field analysis, which makes it difficult to adapt to the complex working conditions of kilometer-level deep mining. Summary of the Invention

[0005] In view of the technical problems existing in the background art, the present invention provides an optimization method for deep metal mine mining based on muscle-tendon biomimetic and thermodynamic entropy production. This method quantifies the flexible energy storage, stress screening, and progressive damage dissipation mechanism of the skeletal muscle-tendon native synergy into a nonlinear constitutive equation of the rock mass, and couples it with the nonequilibrium thermodynamic entropy production dissipation theory to construct a fully thermo-mechanical coupled quantitative system. This system is used for the quantitative definition of stope risk zones, the optimization solution of mining parameter combinations, and the dynamic control of energy release processes.

[0006] This invention provides an optimized method for deep metal ore mining based on muscle-tendon biomimicry and thermodynamic entropy production, comprising the following steps: S1. Based on the differences in rock mass integrity, the mining area rock mass is divided into at least two types of biomimetic mechanical units. The two types of biomimetic mechanical units correspond to the energy storage characteristics of skeletal muscle and the force dissipation characteristics of tendon, respectively, and the biomimetic mechanical parameters of each unit are calibrated. S2. Based on the biomimetic mechanical parameters, a set of coupled nonlinear biomechanical constitutive equations for the two types of biomimetic mechanical units is constructed respectively. The set of constitutive equations is used to describe the nonlinear energy storage behavior and progressive damage evolution behavior of rock mass under excavation disturbance. S3, the mining site is defined as an open non-equilibrium thermodynamic system. Based on the constitutive equations, an internal irreversible entropy production rate equation is constructed, and the critical entropy production rate is calibrated. S4. Based on the constitutive equation set and the entropy production rate equation, establish a dual-threshold joint catastrophic criterion that includes static energy constraints and dynamic entropy production rate constraints, and accordingly quantify and partition the mining area into at least three risk levels. S5. Based on the dual-threshold joint disaster criterion, construct a force-thermal synergistic multi-objective optimization function and solve for the combination of recovery parameters; S6. The entropy production rate is inverted in real time based on the on-site microseismic monitoring data. When the entropy production rate deviates from the critical entropy production rate by more than a preset threshold, the combination of back sampling parameters is adaptively corrected based on the gradient iteration algorithm to form a closed-loop control.

[0007] As a further improvement of the present invention, in step S1, the biomimetic mechanical unit includes: Muscle energy storage units correspond to hard rock regions with good integrity and undeveloped fissures, characterizing the flexible energy storage and progressive deformation properties of skeletal muscle. The nonlinear stiffness correction coefficient of the muscle energy storage unit is used to characterize the nonlinear hardening or softening characteristics of deep high-stress rock masses. The expression is as follows: ; In the formula, This is the correction factor for the nonlinear stiffness of the rock mass; It is the static elastic modulus; The dynamic elastic modulus under unloading is the instantaneous deformation modulus under unloading disturbance during deep excavation. The limit strain for indoor triaxial unloading.

[0008] As a further improvement of the present invention, in step S1, the biomimetic mechanical unit further includes: The tendon force dissipation unit corresponds to the fractured region with a dense network of joints and fissures, and characterizes the stress screening, friction dissipation and progressive fatigue damage characteristics of the tendon. The tendon stress dispersion coefficient of the calibrated tendon force dissipation unit is used to characterize the stress homogenization and dissipation capacity of the joint network. The expression is: ; In the formula, or T is the tendon stress dispersion coefficient, with a value range of (0,1); x The number of joint groups in the surrounding rock; r f The surface density of the fracture; f The internal friction angle of the joint interface characterizes the roughness and interlocking degree of the crack contact surface; The damage sensitivity index and the bio-rock mass mapping ratio of the tendon force dissipation unit are calibrated. The damage sensitivity index characterizes the sensitivity of the damage evolution rate to changes in stress level, and the bio-rock mass mapping ratio maps the fatigue damage evolution law of the tendon tissue in the organism to the damage evolution of the rock mass structural surface. The expression for the damage sensitivity index is: ; In the formula, m The damage sensitivity index; Δ D 1 Δ D 2 The damage increments are represented under two different stress levels. s T1 , s T2 The shear stress was determined for two groups of differentiated tests. The expression for the biomass-rock mass mapping ratio coefficient is as follows: ; In the formula, l The biomass-rock mass mapping ratio coefficient; Δ D T，test The single-stage damage increment in the indoor cyclic shear test; Δ N test This refers to the number of cyclic loading cycles in the indoor test. s T，test Apply shear stress level to the test; sT, lim To calibrate the actual shear stress level applied during the test.

[0009] As a further improvement of the present invention, in step S2, the coupled nonlinear biomechanical constitutive equation set includes: The equivalent principal stress of the muscle energy storage unit is expressed as the superposition of the linear term of the initial elastic modulus and the equivalent strain, and the nonlinear stiffness correction coefficient and the square term of the equivalent strain. The corresponding energy storage density is expressed as the superposition of the elastic energy storage term and the nonlinear energy storage term. The expression for the equivalent principal stress equation of the muscle energy storage unit is: ; The corresponding energy storage density equation is: ; In the formula, s M The equivalent principal stress of the muscle energy storage unit; E M For different rock masses, the initial elastic modulus is used. e M This represents the equivalent axial strain of the rock mass. W M The elastic energy storage density of the rock mass; The equivalent transmitted stress of the tendon force dissipation unit is expressed as a function of the stress dispersion coefficient, the equivalent principal stress of the muscle energy storage unit, and the cumulative damage variable. The evolution rate of the cumulative damage variable is expressed as a power function of the bio-rock mass mapping ratio coefficient, the stress level to the ultimate bearing capacity ratio, and the cumulative damage variable. The expression for the equivalent transmitted stress of the tendon force dissipation unit and the continuous fatigue damage evolution equations is as follows: ; ; In the formula, s T This is the equivalent stress transmission unit for tendon force dissipation. D T The cumulative tendon injury variable takes values ​​[0,1], where 0 represents intact tendon and 1 represents complete patency failure. N This represents the number of excavation disturbance cycles. s T, lim This represents the ultimate shear capacity of the structural surface. m Damage sensitivity index; n The damage attenuation index; The rate of evolution of the cumulative tendon injury variable with the number of excavation disturbance cycles is the damage increment generated in a single excavation cycle. The corresponding damage iterative update equation is: ; In the formula, The cumulative tendon damage variable after N+1 excavation disturbance cycles; The cumulative tendon damage variable after the Nth excavation disturbance cycle.

[0010] As a further improvement of the present invention, in step S3, the internal irreversible entropy production rate equation is decomposed into a structural surface friction slip dissipation entropy production term and a damage evolution dissipation entropy production term. The structural surface friction slip dissipation entropy production term is determined by the product of the equivalent transmitted stress and the shear slip strain rate of the tendon force dissipation unit, and the damage evolution dissipation entropy production term is determined by the product of the structural surface damage energy release rate and the damage evolution rate. The expression for the internal irreversible entropy production rate is: ; In the formula, S int The rate of irreversible entropy production within the rock mass; T abs This refers to the absolute temperature downhole. e T The shear slip strain rate of the structural surface; Y T The energy release rate due to structural surface damage; This represents the damage evolution rate.

[0011] As a further improvement of the present invention, in step S3, the calculation of the calibrated critical entropy production rate includes: retrieving the microseismic dataset corresponding to historical rockburst disaster cases in the target mine; calculating the microseismic radiation energy corresponding to each disaster case based on the microseismic dataset; performing statistical analysis on the microseismic radiation energy to determine the critical microseismic radiation energy threshold; and calculating the critical entropy production rate based on the critical microseismic radiation energy threshold, the rock mass calculation unit volume, and the underground absolute temperature, as expressed below: ; In the formula, S cr The critical entropy production rate of rockburst; V unit The size of the grid cell in the numerical model of rock mass calculation unit volume; E seis,cr,i This refers to the microseismic radiation energy of a single event corresponding to a critical catastrophe. k To effectively count the number of samples.

[0012] As a further improvement of the present invention, in step S4, the dual-threshold joint catastrophe criterion includes: the rock mass elastic storage density does not exceed the critical value of the rock mass elastic storage; the internal irreversible entropy production rate does not exceed the critical entropy production rate; and the time-varying gradient of the entropy production rate does not exceed zero, so as to force the system to be in thermodynamic steady-state dissipation; the expression of the criterion is: ; In the formula, W M,cr This is the critical value for elastic energy storage in rock mass; The gradient represents the time-varying entropy production rate.

[0013] As a further improvement of the present invention, in step S4, the step of quantitatively dividing the mining area into at least three risk levels includes: High-risk areas: ; Controllable area: ; Stable region: .

[0014] As a further improvement of the present invention, in step S5, the force-thermal synergistic multi-objective optimization function takes minimizing the comprehensive objective function value as the optimization objective. The comprehensive objective function value is determined by the weighted sum of the normalized ratio of the maximum energy release in a single excavation to the maximum allowable safe energy release value, the normalized ratio of the maximum transmitted shear stress generated during excavation to the ultimate shear bearing capacity of the structural surface, and the normalized ratio of the maximum entropy production rate in a single excavation to the critical entropy production rate; the expression is: ; In the formula, F The value of the comprehensive objective function; w 1 、w 2 、w 3 These are the normalized weighting coefficients, and the sum of the three is 1; Δ W M，max This represents the maximum energy release from a single excavation; Δ W M，allow To the maximum permissible safe energy release value; s T,max To generate the maximum transmitted shear stress during excavation; S int,max This represents the maximum entropy production rate for a single excavation. The constraints are as follows: ; In the formula, W pre 、W aft The energy storage density of the rock mass before and after excavation; L s This refers to the single sampling step distance; Ls, min 、L s, max These are the upper and lower limits of the back-mining step distance; t gap This refers to the interval between excavation and resting periods. It is the minimum thermodynamic relaxation time.

[0015] As a further improvement of the present invention, in step S6, the inversion equation for the entropy production rate is: In the formula, S int ( t )for t Real-time entropy production rate; V To monitor and control the rock mass volume; l ( t )for t Accumulated number of effective microseismic events at any given time; E seis, l ( t ) is the first l Energy radiated from a microseismic event; The process of adaptive iterative correction of parameters is as follows: The sampling parameters to be optimized are formed into a vector. X The established iterative equation is: ; In the formula, For the first K The parameter vector retrieval at the next iteration; For the first K+1 The parameter vector retrieval at the next iteration; X This is a data acquisition parameter vector, containing step size, rate, interval, and timing. The gradient of the objective function characterizes the descent direction for parameter optimization. α The calibration formula for the adaptive iteration step size is: ; in, α 0 The initial iteration reference step size is preset to 0.1.

[0016] Beneficial effects: First, by introducing the muscle-tendon biomimetic division and nonlinear constitutive model, the shortcomings of traditional linear elastic constitutive model in describing the nonlinear energy storage and damage dissipation of rock mass under deep unloading conditions are overcome, making the mechanical model more compatible with deep working conditions.

[0017] Second, by directly embedding mechanical damage variables into the entropy production decomposition equation, a direct mathematical link between mechanical damage evolution and thermodynamic dissipation is established, enabling the catastrophe criterion to simultaneously include information on both energy storage state and dissipation rate. Furthermore, the gradient constraint of entropy production rate over time is introduced, giving the criterion the ability to capture the evolution trend of the system.

[0018] Third, all physical parameters have clear indoor or field calibration methods, and the weight coefficients of the optimization targets are objectively assigned using historical data variation coefficients, avoiding the uncertainty brought about by manual experience-based assignment.

[0019] Fourth, the entropy production rate is inverted in real time through microseismic data and dynamically corrected by combining gradient iterative algorithm. The adjustment range automatically increases as the system approaches the critical state, exhibiting adaptive characteristics.

[0020] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0021] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the present invention will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0022] Figure 1 This is a process flow diagram of the optimized method for deep metal ore mining based on muscle-tendon bionics and thermodynamic entropy production provided in the embodiments of the present invention. Figure 2 This is a comparison diagram of the muscle-tendon biomimetic unit division and nonlinear constitutive model provided in the embodiments of the present invention; Figure 3 This is a flowchart of the dual-threshold risk partitioning criterion and closed-loop adaptive control provided in the embodiments of the present invention. Figure 3 In the diagram, 'a' represents a schematic representation of the dual-threshold risk zoning criterion. Figure 3 (b in the diagram is a closed-loop adaptive control flowchart). Detailed Implementation

[0023] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0024] 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; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the invention, are intended to cover non-exclusive inclusion.

[0025] In the description of the embodiments of this invention, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this invention, "multiple" means two or more, unless otherwise explicitly defined.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] In the description of the embodiments of this invention, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0028] To address the technical problems of frequent energy release disasters in deep, high-stress metal mines, the lack of quantitative basis for mining parameters, and the absence of force-thermal coupling theory support, this invention provides an optimization method for deep metal mine mining based on muscle-tendon bionics and thermodynamic entropy production. It quantifies the flexible energy storage, stress screening, and progressive damage dissipation mechanism of skeletal muscle-tendon native synergy into a nonlinear constitutive equation of the rock mass, and couples this equation with the nonequilibrium thermodynamic entropy production dissipation theory to establish a complete thermo-mechanical coupling quantitative system. This system is used for the quantitative definition of stope risk zones, the optimization of mining parameter combinations, and the dynamic control of energy release processes.

[0029] Please see Figure 1 to Figure 3As shown, this invention provides an optimization method for deep metal mine mining based on muscle-tendon bionics and thermodynamic entropy production. It maps the flexible energy storage and progressive damage dissipation mechanism of the skeletal muscle-tendon synergy to deep rock masses, constructing a muscle-tendon binary bionic mechanical unit and a coupled nonlinear constitutive equation system. It deeply couples the non-equilibrium thermodynamic entropy production dissipation theory with rock mass damage mechanics, establishing a force-thermal dual-threshold joint catastrophic criterion that includes a critical value for elastic energy storage, a critical value for entropy production rate, and an entropy production growth rate constraint. Furthermore, it establishes a closed-loop control system based on real-time inversion of entropy production rate using microseismic event energy and adaptive correction of mining parameters using a gradient iterative algorithm, achieving quantitative delineation of stope risk zones and dynamic optimization of mining parameters. Specifically, it includes the following steps: S1, Multi-source data acquisition in the sampling field and refined division and parameter calibration of muscle-tendon bionic units. This step, through the linkage of in-situ field testing and standard laboratory tests, obtains rock mass mechanical parameters and structural geometric information. Based on differences in rock mass integrity, the mining area is divided into biomimetic units with different mechanical functions, completing the quantitative calibration of core control parameters. All laboratory mechanical tests in this step are performed according to GB / T 50266-2013 "Standard for Testing Methods of Engineering Rock Mass". This step specifically includes the following sub-steps: S11, Multi-source data acquisition and sample preparation A multi-source data acquisition method combining on-site measurements and indoor experiments was adopted. The required core experimental equipment included the MTS815 electro-hydraulic servo rock mechanics testing system, a rock mass structural surface shear tester, a high-precision borehole camera testing instrument, a hollow inclusion geostress testing system, and a mine microseismic online monitoring system. Rock blocks from typical locations in the surrounding rock of the mining area were selected on-site and processed into standard cylindrical specimens with a uniform diameter of 50 mm and a height of 100 mm. Each test group contained no fewer than six specimens.

[0030] The magnitude and principal direction of the stress tensor at the mining site were obtained in situ using the hollow inclusion stress testing method. A borehole camera was used to probe the surrounding rock along the roadway and mining area, and structural parameters such as the number of joint groups, joint orientation, and fracture surface density were statistically analyzed. Indoors, the MTS815 system was used to determine the rock mass density, static elastic modulus, Poisson's ratio, uniaxial compressive strength, and triaxial mechanical parameters under simulated excavation unloading paths. Simultaneously, a structural surface shear tester was used to determine the internal friction angle and ultimate shear bearing capacity of the joint interfaces. The time series and radiation energy data of microseismic events at the working face were continuously collected using the installed mine microseismic monitoring system.

[0031] S12, muscle-tendon biomimetic unit division Based on the measured differences in rock mass integrity, the mining area rock mass is divided into two types of biomimetic units. The first type is the muscle energy storage unit, which, like skeletal muscle in a living organism, possesses the characteristics of flexible energy storage, gradual deformation, and no sudden instability, corresponding to hard rock areas with good integrity and underdeveloped fractures. The second type is the tendon force dissipation unit, which, like tendons, possesses the characteristics of stress sieving, frictional dissipation, and progressive fatigue damage, corresponding to fractured areas with dense joint and fracture networks. The corresponding characteristics are shown in Table 1.

[0032] Table 1. Region Types, Judgment Thresholds, Mechanical Properties, and Corresponding Units The aforementioned thresholds were determined comprehensively based on the "Engineering Rock Mass Classification Standard" (GB / T 50218-2014) and the statistical results of historical rock mass quality classification in the mining area. During on-site operations, the mining technicians used the borehole camera measurement data to delineate the areas according to the table above, and used different color marks to distinguish the two types of areas on the mining engineering plan, serving as the spatial basis for subsequent blasting hole layout and pressure relief hole construction.

[0033] S13, Calibration of Nonlinear Stiffness Correction Coefficient for Muscle Energy Storage Unit The nonlinear stiffness correction factor for deep rock mass is calibrated as follows: ; In the formula: This is the nonlinear stiffness correction coefficient for rock mass, characterizing the nonlinear hardening / softening features of deep, high-stress rock mass; This is the static elastic modulus, obtained from indoor uniaxial compression tests; The dynamic elastic modulus under unloading is the instantaneous deformation modulus under unloading disturbance during deep excavation, which is obtained from triaxial graded unloading tests. The limit strain for indoor triaxial unloading.

[0034] A composite path test involving graded confining pressure unloading and axial pressure increase was conducted on standard specimens using the MTS815 system. The stress-strain curves were continuously recorded throughout the entire process. The three parameters mentioned above were extracted and substituted into the formula for calculation. Calibration yielded... The technical log of the mining area is entered into the record for each shift. If k M ＞ 1.2 k M,历史均值 (i.e., the rock mass hardening degree is abnormally high and the energy storage tendency is enhanced), then in the design of the next cycle of blasting, the spacing between the surrounding holes in this area will be reduced from the conventional value of 1.0m to 0.7m. At the same time, an air-spaced charge structure will be adopted, with the length of the interval section accounting for 30% to 40% of the total length of the blast hole, in order to reduce the instantaneous disturbance intensity of a single blast on the energy storage rock mass.

[0035] S14, Quantitative calibration of tendon stress dispersion coefficient of tendon force dissipation unit. Define the tendon stress dispersion coefficient ηT, and calculate it as follows: ; In the formula: or T The stress dispersion coefficient of the tendon is in the range of (0,1). The smaller the value, the stronger the stress homogenization and dissipation capacity of the joint network, and the better the pressure relief and disaster prevention effect of the surrounding rock. x The number of rock joint groups refers to the number of groups of large structural cracks in the surrounding rock, which is statistically measured by downhole drilling cameras. r f Fracture surface density refers to the total number of fractures per unit length of surrounding rock. The larger the value, the more fractured the rock mass and the stronger its pressure relief capacity. f The internal friction angle of the joint interface characterizes the roughness and interlocking of the crack contact surface. The larger the angle, the more energy is dissipated by friction. It is determined by indoor structural surface shear test.

[0036] The number of joint groups and fracture surface density of the surrounding rock in the mining area were statistically measured by underground borehole cameras. The friction angle of the joint interface was determined by indoor structural surface shear test. The measured engineering parameters were substituted into the exponential formula to quantify the tendon stress dispersion coefficient.

[0037] During on-site operation, or T A smaller value indicates a stronger energy dissipation capacity of the joint network. If the actual measured value is... or T If the stress distribution in the densely packed structural area is greater than 0.6 (meaning insufficient stress dispersion in the structural zone), then pre-decompression drilling will be carried out in this area. The drilling parameters are: hole diameter 75mm~110mm, hole depth 3m~5m, hole spacing 1.5m×1.5m, arranged in a quincunx pattern. After the drilling is completed, borehole photography will be used to verify the fracture continuity and ensure the stability of the area. or T Normal mining can only begin when the value drops below 0.4.

[0038] S15, Joint calibration of tendon injury evolution parameters This calibration involves the bio-rock mass mapping ratio factor. l and damage sensitivity index m Standard shear specimens containing native or artificially prefabricated structural surfaces were prepared, and multi-stage cyclic shear tests were conducted using a rock mass structural surface shear testing apparatus. Two different shear stress levels were set up in the test. s T1 and s T2 Record the cumulative damage increment Δ of the structural surface for the corresponding number of cycles. D 1 With Δ D 2 The damage sensitivity index is calculated using the following formula: ; In the formula: Δ D 1 Δ D 2 The damage increments are represented under two different stress levels. s T1 , s T2 The shear stress is for two groups of differentiated tests.

[0039] m The damage sensitivity index characterizes the sensitivity of the damage evolution rate to changes in stress level. The larger the value, the more rapid the damage growth will be due to even a small increase in stress level, meaning that the structure is more sensitive to stress disturbances. Δ D 1 Δ D 2 The values ​​are the cumulative damage increments generated by the structural surface specimens within a set number of cycles in the first and second group cyclic shear tests, respectively. The values ​​range from zero to one and are calculated from the measured structural surface stiffness attenuation or penetration degree in the test. Select any set of test data and combine it with the pre-determined ultimate shear capacity of the structural surface. s T,lim Substitute the values ​​into the following formula to calculate the mapping scaling factor: ; In the formula: l The bio-rock mass mapping ratio coefficient is a proportional factor that maps the fatigue damage evolution law of the tendon tissue of the organism to the damage evolution of the rock mass structural surface. This coefficient comprehensively reflects the rate characteristics of damage accumulation of the rock mass structural surface under cyclic disturbance. Δ D T,test This represents the single-stage damage increment in indoor cyclic shear tests. Δ N test The number of loading cycles in the indoor test is dimensionless. s T,test Apply shear stress level to the test.

[0040] Damage sensitivity index obtained from calibration m Input the numerical model of the mining area. When the iterative calculation displays a certain area... D T When ≥ 0.85, the following measures shall be implemented on site ( D T Specific definitions, iterative methods and based on D T For the on-site operational details of the criterion, see step S22). (a) This area shall be designated as a pressure relief buffer zone, and large equipment shall be prohibited from operating directly above this area; (b) Construct a large-diameter pressure relief borehole with a diameter of 150 mm at the edge of the area, with the borehole depth extending 2 m beyond the structural surface penetration section; (c) Adjust the direction of the longwall mining advance so that the angle between the main longwall face and the strike of the area is changed from 90° to 60°~70°, so as to reduce the efficiency of direct shear stress transmission.

[0041] S2, Constructing a set of nonlinear biomechanical constitutive equations for muscle-tendon coupling. After completing the basic parameter calibration in step S1, based on the skeletal muscle elastic energy storage bioconstitutive model and combined with the strong nonlinear unloading characteristics of deep rock masses, a third-order nonlinear energy storage constitutive model for muscle units is established to replace the traditional linear elastic approximation, improving the adaptability to deep working conditions. This specifically includes the following sub-steps: S21, Establish the nonlinear stress-strain-energy storage coupling equation for the muscle energy storage unit. The established stress equation is: ; The corresponding energy storage density equation is: ; In the formula: s M The equivalent principal stress of the muscle unit is the equivalent excavation disturbance stress borne by the intact hard rock mass; E M For different rock masses, the initial elastic modulus is used. e M The equivalent axial strain of the rock mass is the deformation strain of the rock mass caused by excavation and unloading. W M Rock mass elastic energy storage density is the releasable elastic strain energy stored per unit volume of rock mass.

[0042] The second-order term of the equation characterizes the nonlinear growth of rock mass stress, while the third-order term characterizes the nonlinear accumulation of energy storage under high ground stress in deep rock masses, accurately describing the high energy storage characteristics of deep rock masses. The nonlinear stiffness coefficient is obtained by fitting measured stress-strain data from triaxial unloading tests, ensuring that the constitutive model fully adapts to the nonlinear mechanical behavior of unloading during deep excavation, distinguishing it from the traditional linear elastic assumption for shallow excavation.

[0043] S22, establish the stress transmission and continuous fatigue damage evolution equations for the tendon force dissipation unit.

[0044] Based on the damage sensitivity index obtained in step S15 m With mapping scaling factor l The equations governing stress transmission and continuous fatigue damage evolution within the tendon unit are expressed as follows: ; ; In the formula: s T For the equivalent shear-tension combined stress of the tendon unit, the stress transmitted by the joint and fracture network is: D T The cumulative tendon injury variable takes values ​​[0,1], where 0 represents intact tendon and 1 represents complete patency failure. N The number of excavation disturbance cycles corresponds to the number of steps in the step-by-step mining advance; l The bio-rock mass mapping ratio coefficient represents the quantitative mapping ratio of biological damage mechanisms to rock mass damage. s T,lim The ultimate shear capacity of the structural surface is determined by indoor shear tests. m The damage sensitivity index characterizes the degree to which damage is sensitive to stress levels; n The damage attenuation index is dimensionless and characterizes the weakening attenuation law of damage accumulation.

[0045] The corresponding damage iterative update equation is: ; After each sampling cycle, iterate and update according to the above formula. If (i.e., a single excavation causes a sudden increase in damage exceeding 5%), then the single mining step distance in the next cycle... Ls The excavation interval was reduced from the conventional value of 3.0m to 2.0m, and the excavation settling interval was adjusted to allow sufficient time for energy release and stress redistribution in the surrounding rock.

[0046] S3, Constructing a complete set of mathematical equations for nonequilibrium thermodynamics. The deep mining area is defined as an open, non-equilibrium, irreversible thermodynamic system. Excavation disturbance is the external negative entropy flow input, while rock mass slippage, fracturing, and damage are the internal positive entropy production and dissipation. Establishing a complete set of governing equations includes the following steps: S31, Establish the system's total entropy balance equation System total entropy balance equation: ; In the formula: r The density of the rock mass was determined by on-site sampling and measurement. T abs The absolute temperature underground is the average temperature of the tunnel measured over the years. S The total entropy production rate of the system; S ext The external entropy flow rate at the excavation boundary is determined by the intensity of the excavation disturbance. s ij It is the second-order stress tensor of the rock mass; It represents the irreversible plastic strain rate.

[0047] S32, Establish the refined decomposition equation of the internal irreversible entropy production rate. The irreversible entropy production rate within the system can be decomposed into two components: structural surface frictional slip dissipation and damage evolution dissipation. ; In the formula: The irreversible entropy production rate inside the rock mass is the core indicator for judging rockburst instability. e T The shear slip strain rate of the structural surface; Y T The energy release rate due to structural surface damage; This represents the damage evolution rate.

[0048] In the formula, the first term is the entropy production of joint friction slip dissipation, and the second term is the entropy production of rock mass damage evolution dissipation, realizing precise coupling of mechanical behavior and thermodynamic entropy production term by term.

[0049] S33, Establish the equations for damage energy release rate and irreversible dissipation power. Helmholtz free energy density P Defined as: ; The system's irreversible power dissipation equation: ； In the formula: Ω represents the irreversible energy dissipation power of the rock mass, characterizing the severity of rock mass damage and dissipation.

[0050] S34, Engineering threshold for calibrating critical entropy production rate Critical entropy production rate engineering calibration equation: ; In the formula: S cr The critical entropy production rate of rockburst is the thermodynamic instability threshold of the stope. V unit The size of the grid cell in the numerical model of rock mass calculation unit volume; E seis,cr,i This refers to the microseismic radiation energy of a single event corresponding to a critical catastrophe. k To ensure the effective statistical sample size, the sample size is dimensionless.

[0051] During calibration, microseismic sensors were deployed in the upper and lower roadways of the mining area and around the working face, with a sampling frequency set at no less than 1 kHz. Noise filtering was applied to the continuously acquired waveform data. Event location was performed using the arrival time difference between P-waves and S-waves. The radiant energy of each effective fracturing event was calculated by integration. Non-rock mass fracturing interference signals such as blasting vibrations and mechanical cutting were eliminated based on waveform characteristics. Microseismic datasets corresponding to rockburst and strong rockfall disasters that occurred in the target mine within the past three to five years were retrieved. At least thirty effective critical samples were selected, and statistical analysis was performed using a 95% confidence interval. Outliers were eliminated, and the radiant energy of the remaining samples was calculated using the arithmetic mean, combined with the unit volume and absolute temperature, and then substituted into the formula for calculation. Calibration S cr The value is written into the early warning module of the mine microseismic monitoring system. The system calculates the real-time entropy production rate at a 5-minute interval. (t), and with S cr Perform a comparison, and the judgment rules are described in step S62. S4, Mechanism-Thermal Dual-Coupling Catastrophic Criterion and Quantitative Partitioning Mathematical Model This step, based on the results of steps S2 and S3, establishes a joint criterion with dual thresholds, including static energy constraints and dynamic entropy production rate constraints, and formulates risk zoning quantification rules accordingly. Specifically, it includes the following sub-steps.

[0052] S41, Establish a set of equations for a dual-threshold joint disaster criterion. The established joint criteria are: ; In the formula: W M,cr This is the critical value for elastic energy storage in rock mass, determined by indoor rock mass failure tests. The entropy production rate is a time-varying gradient, characterizing the evolution trend of a thermodynamic system.

[0053] The newly added constraint on the growth rate of entropy production forces the system to be in a thermodynamic steady-state dissipation state, preventing abrupt leaps in entropy production.

[0054] S42, Formulate zoned quantitative judgment rules and control measures Based on the above joint criteria, the mining area is divided into three risk levels: High-risk areas: ; Controllable area: ; Stable region: .

[0055] like Figure 3 As shown in Figure a, the dual-threshold risk zoning criterion uses two levels of thresholds to divide the parameter space into a stable region, a controllable region, and a high-risk region (corresponding to green dots, yellow rhombuses, and red triangles, respectively).

[0056] like Figure 3 As shown in Figure b, the closed-loop adaptive control process mainly consists of: real-time data acquisition → real-time inversion of production rate → according to... Figure 3 The criterion shown in Figure a determines the risk level and divides the response into three types: emergency control (high-risk area), gradient iterative calculation optimization (controllable area), and parameter maintenance and real-time feedback (stable area).

[0057] The control measures for different risk levels are shown in Table 2 below: Table 2 Control Measures for Different Risk Levels S5, a complete mathematical model for force-thermal synergistic multi-objective optimization. A dimensionless normalized multi-objective optimization function is constructed to solve the problems of inconsistent dimensions and conflicting objectives, and all weights can be quantified and calibrated.

[0058] S51, Establish the real-time inversion equation for on-site entropy production. The established inversion equation is: ; In the formula: F The smaller the value of the comprehensive objective function, the more stable the mining operation. w 1 、w 2 、w 3 These are normalized weighting coefficients, dimensionless, and the sum of the three is 1. Δ W M,max This represents the maximum energy release from a single excavation. Δ W M,allow To the maximum permissible safe energy release value; s T,max To generate the maximum transmitted shear stress during excavation; S int,max This represents the maximum entropy production rate for a single excavation.

[0059] S52, Establish an objective calibration method for weighting coefficients The objective calibration equation for the weighting coefficients is: ; In the formula: To achieve objective mathematical assignment of weights for each indicator, historical coefficients of variation are used. Multiple sets of historical excavation data from the mining area are extracted, and the coefficients of variation for single excavation energy release, peak stress, and entropy production rate are calculated. The weights of each target are objectively solved using the reciprocal normalization method, avoiding the defects of subjective manual assignment.

[0060] S53, Define complete constraint equations and enforcement rules The constraints are as follows: ; In the formula: W pre 、W aft The energy storage density of the rock mass before and after excavation; L s This refers to the single sampling step distance; L s,min 、L s,max The upper and lower limits of the mining step distance are determined by the thickness of the ore body and safety regulations. t gap This refers to the interval between excavation and resting periods. The minimum thermodynamic relaxation time is determined by the surrounding rock convergence monitoring data.

[0061] During on-site implementation, the mining parameters obtained from each optimization solution are output to the mining operation manual. The specific implementation standards are as follows: Step length L s The optimized value is rounded to the nearest 0.5m module, and falls between 1.5m and 2.5m. Interval time t gap The optimized value is rounded to the nearest 0.5 hours, and is not less than the minimum thermodynamic relaxation time. t gap,min (Determined by surrounding rock convergence monitoring data, typically 2 hours); The advance direction angle is marked on the mining site plan according to the optimized value, and the drilling rig drills holes according to the marked azimuth angle.

[0062] S6, Microseismic Inversion and Gradient Iteration Closed-Loop Control System S61, Establish the real-time inversion equation for on-site entropy production. Real-time inversion equation for on-site entropy production: ; In the formula: S int ( t )for t Real-time entropy production rate; V To monitor and control the rock mass volume; l ( t )for t Accumulated number of effective microseismic events at any given time; E seis,l (t) is the th l The energy radiated by a microseismic event.

[0063] S62, Establish the parameter adaptive iterative correction equation The sampling parameters to be optimized are formed into a vector. X The established iterative equation is: ; In the formula: X This is a data acquisition parameter vector, containing step size, rate, interval, and timing. The gradient of the objective function characterizes the descent direction for parameter optimization. α The calibration formula for the adaptive iteration step size is as follows: ; In the formula: α 0 This is the initial iteration reference step size, dimensionless, and preset to 0.1.

[0064] In practice, microseismic sensors are deployed in a grid pattern within the disturbance zone of the mining area. The system collects waveform data in real time at a sampling frequency of no less than 1 kHz, and has built-in automatic waveform recognition and energy integration algorithms to output entropy production rate inversion values ​​every second. These real-time values ​​are synchronously input into the iterative equation to calculate the gradient of the objective function and the adaptive step size, and update the mining parameter vector. The updated step size and interval guide the on-site drilling, blasting and ore extraction operations, and enter the next cycle.

[0065] S63, Closed-loop execution process S631, Hardware Deployment: Microseismic sensors are deployed in the upper and lower roadways of the mining area and around the working face at a grid spacing of 15m~20m. The sensors are connected to the underground data acquisition station via coaxial cables, with a sampling frequency of not less than 1000Hz. Data is uploaded to the ground monitoring server in real time. The sensors should be embedded in the surrounding rock boreholes at a depth of 1.5m~2.0m. Anchoring agent is used to couple the sensors to the borehole wall to ensure the fidelity of waveform signal acquisition.

[0066] S632, Signal Processing and Real-Time Entropy Production Inversion: The monitoring server has a built-in automatic waveform recognition algorithm. Continuously acquired waveform data is bandpass filtered (frequency range 100Hz~3000Hz) to remove low-frequency mechanical vibrations and high-frequency electromagnetic interference. Event location and source type identification are performed using the time difference between P-wave and S-wave arrival. Based on waveform characteristics, non-rock mass fracturing interference signals such as blasting vibrations (typical characteristics: P-wave amplitude greater than S-wave, frequency below 200Hz) and mechanical cutting (continuous low-amplitude waveform) are automatically eliminated. Energy integration is performed on waveforms confirmed as valid fracturing events, calculated second-by-second according to the formula in step S61. S int ( t ), output an average value every 5 minutes and compare it with S cr Perform a comparison. If... S int ( t ) ≥ S cr If this occurs, an emergency response will be immediately triggered: Stop all drilling and blasting operations at the current work face; evacuate personnel to a safe chamber; and construct stress relief boreholes with a diameter of 110mm, a depth of 4m-6m, and a spacing of 1.0m between the boreholes, arranged in a fan shape, within a range of 5m-8m from the work face, until... S int ( t It fell back to 0.5 S crThe following should be maintained stably for at least 30 minutes.

[0067] S633, Gradient Calculation and Parameter Iterative Update: If S int ( t It stabilized at 0.5 S cr ~0.85 S cr Between and | S int ( t )- S cr | / S cr If the deviation is ≥ 0.1 (i.e., the deviation exceeds 10%), a regular iterative calculation is triggered. The server automatically calculates the gradient of the objective function. ， Determine the adaptive step size α Update the sampling parameter vector X.

[0068] S634, Command Issuance and On-site Execution: The updated X value is sent as a text command to the stop room control panel and the handheld terminals of each work team via the underground communication system.

[0069] S635, Field Feedback and Next Iteration: After this loop is completed with the new parameters, the newly acquired microseismic data from the next round enters the second step to recalculate S. int ( t If the deviation still exceeds 10%, repeat steps three and four; if S int ( t It stabilized at 0.5 S cr ~0.85 S cr If the deviation is less than 10%, the current parameter combination is maintained and data is collected again, with a review every 6 hours. The entire process forms a closed-loop adaptive control: "measured signal → entropy generation inversion → deviation judgment → gradient calculation → parameter update → command issuance → field execution → re-measurement".

[0070] Example 1 This embodiment 1 provides an optimized mining method for deep metal mines based on muscle-tendon bionics and thermodynamic entropy production. Using a -1250m mid-level stope in a deep copper mine in East China as the engineering background, it fully demonstrates the numerical calculation and field operation process of the entire process, from muscle-tendon bionic unit division → force-thermal coupling constituent modeling → non-equilibrium thermodynamic entropy production analysis → dual-threshold catastrophe criterion → multi-objective optimization solution → microseismic closed-loop control. All mechanical tests in this embodiment were performed according to GB / T 50266-2013 "Standard for Testing Methods of Engineering Rock Mass", and rock mass classification was based on GB / T 50218-2014 "Standard for Classification of Engineering Rock Mass".

[0071] Table 3 Background of Mining Engineering This method quantitatively optimizes the mining sequence, step distance, excavation interval, and advance direction of the stope, enabling the step-by-step and orderly release of elastic strain energy. This elevates rockburst risk from "experience-based prevention" to "mechanical-thermal coupling quantitative control." The expected goals are: reducing the energy release per excavation cycle by more than 30%, decreasing the frequency of microseismic events, and significantly improving the stability of the surrounding rock in the stope. The specific steps are as follows: S1, Multi-source data acquisition and muscle-tendon biomimetic unit division and parameter calibration S11, Multi-source data acquisition and sample preparation Rock blocks were collected on-site from typical locations on the hanging wall, footwall, and pillars of the -1250m mid-level stope and processed into standard cylindrical specimens with a diameter of 50mm × 100mm, with 8 specimens per group (ensuring at least 6 specimens). The following equipment was used for testing: MTS815 electro-hydraulic servo rock mechanics testing system, rock mass structure shear tester, high-precision borehole camera tester, hollow inclusion geostress testing system, and mine microseismic online monitoring system, as shown in Table 4 below. Table 4 Basic parameters obtained from field and indoor tests S12, muscle-tendon biomimetic unit division Based on GB / T 50218-2014 and actual borehole camera measurement data, the mining area is divided into two types of biomimetic units, as shown in Table 5: Table 5 Unit Type Classification On-site technicians used blue (muscle units) and orange (tendon units) color codes to distinguish two types of areas on the mining engineering plan, which served as the spatial basis for the subsequent construction of blasting holes and pressure relief holes.

[0072] S13, Calibration of Nonlinear Stiffness Correction Coefficient for Muscle Energy Storage Unit A composite path test involving graded confining pressure unloading (confining pressure gradually decreasing from 30 MPa to 5 MPa) and axial pressure increase was conducted on standard specimens using the MTS815 system, and stress-strain curves were recorded throughout the process. Extraction... E s =45.0GPa E d =38.5GPa e lim =0.012, substituting into the formula for nonlinear stiffness correction coefficient, k M =1.150.

[0073] The historical average of this mining area k M,历史均值 =1.08, this batch k M / k M,历史均值 =1.065 < 1.2, no hardening anomaly warning was triggered, the spacing between surrounding holes remained at the normal value of 1.0m, and a continuous charging structure was adopted. The calibration value was entered into the stope technical log.

[0074] Quantitative calibration of tendon stress dispersion coefficient in S14 tendon force dissipation unit According to the formula for the tendon stress dispersion coefficient ηT, C=0.03 is the dimensionless normalization constant (determined by regression analysis of historical data from the mining area). Calculations were performed for three typical tendon regions within the mining area, as shown in Table 6: Table 6 Calculation results for three typical tendon regions within the mining area For the T-2 area ( or T =0.812>0.6), advanced pressure relief drilling was implemented: hole diameter 90mm, hole depth 4m, hole spacing 1.5m×1.5m, quincunx arrangement. Post-construction drilling video verified fracture continuity, and retesting yielded... x =4、 r f =7、 f =33°, recalculate or T =0.580, still slightly higher than 0.4 but lower than 0.6, indicating that normal mining can begin, and microseismic sensors should be densified in this area.

[0075] S15, Joint calibration of tendon injury evolution parameters Standard shear specimens (50mm×50mm×50mm, with prefabricated crack inclination angle of 45°) containing artificial prefabricated structural surfaces were prepared and subjected to two-stage cyclic shear tests using a rock mass structural surface shear tester, as shown in Table 7. Table 7 Results of Cyclic Shear Tests The damage sensitivity index m is solved by simultaneously solving two sets of experimental data: m = ln( Δ D 2 / Δ D 1 ) / ln(σ T2 / s T1 ) = 3.23.

[0076] Take the first set of data and combine it with... s T,lim =18.0MPa, calculate the biomass-rock mass mapping ratio. l : λ = ( Δ D 1 / Δ N 1 ) / (s T1 / s T,lim ) m = 0.102.

[0077] Calibration results: m =3.23, l =0.102. m A large value (>3) indicates that the structure of the mining area is highly sensitive to changes in stress level. Even a small increase in stress can lead to accelerated damage growth. The intensity of disturbance during a single excavation must be strictly controlled on site.

[0078] S2, Construction and Calculation of Nonlinear Biomechanical Constitutive Architecture of Muscle-Tendon Coupled S21, Nonlinear stress-strain-energy storage coupling equation for muscle unit Based on the skeletal muscle elastic energy storage constitutive model and combined with the strongly nonlinear unloading characteristics of deep rock masses, a third-order nonlinear energy storage constitutive model is established: ; ; Typical calculation points for muscle units: E M =45.0 GPa, k M =1.150, the equivalent axial strain generated by excavation and unloading. e M =0.005 (0.5%). Substitute into the calculation: sM = 226.3 MPa, Δ W M,allow = 0.565 MJ / m³.

[0079] In contrast, if a traditional linear elastic constitutive model is adopted ( s = Hey , W = 0.5Eε² ), calculated s =225.0MPa, W =0.5625 MJ / m³. The nonlinear constitutive results show 0.59% higher stress and 0.43% higher stored energy compared to the linear elastic results; when the strain increases to... e M When =0.01, nonlinearity s =470.5MPa (linear elasticity 450MPa, 4.6% higher), Δ W M,allow =2.36MJ / m³ (linear elasticity 2.25MJ / m³, 4.9% higher), indicating that nonlinear correction cannot be ignored under deep large deformation conditions.

[0080] S22, Tendon Unit Stress Transmission and Continuous Fatigue Damage Evolution Based on S15 calibration m =3.23、 l =0.102, the stress transfer and damage evolution equation of the tendon unit is: ; ; Take the damage attenuation index n =1.5 (determined by fitting the stiffness decay curve from the cyclic shear test). A typical point in region T-1 is selected, and the initial damage... D T0 =0.10, currently transferred shear stress s T =14.0MPa, the damage iteration after 10 mining cycles is shown in Table 8: Table 8. Results of cyclical damage iteration Step 1 Damage Increment Δ D T =0.0267 < 0.05, no step reduction warning was triggered, and the single sampling step distance remained at 3.0m. Iteration continued until step 28. D T ≈0.65, at step 35 D T ≈0.78, at step 42D T The value is approximately 0.85, reaching the threshold for the pressure buffer zone. At this point, the following actions are taken on-site: (a) This area shall be designated as a pressure relief zone, and work shall be prohibited directly above large equipment; (b) Construct a 150mm large-diameter pressure relief borehole at the edge, with the hole depth extending 2m beyond the structural surface penetration section; (c) The angle between the mining advance direction and the strike of the area was adjusted from 90° to 65°.

[0081] S3, Construction of Non-equilibrium Thermodynamic Equations and Critical Entropy Production Calibration S31, System Total Entropy Balance Equation The deep mining area is defined as an open, non-equilibrium, irreversible thermodynamic system, and the total entropy balance equation is: ; Substitute the parameters into this embodiment: r =2750kg / m³, T =311.15K. Taking a typical excavation step (lasting 2 hours) as an example, the external entropy flow rate at the excavation boundary is... S ext Determined by the intensity of excavation disturbance, and calculated numerically. S ext =1.85×10 -4 W / (kg·K); Irreversible plastic strain rate Equivalent deviatoric stress s ij =55MPa, then the internal plastic dissipation term The equivalent entropy contribution is 2.06 × 10 -5 W / (kg·K). Total entropy production rate of the system. dS / dt directly with S int Characterizes the internal irreversible entropy.

[0082] S32, Internal Irreversible Entropy Production Rate Decomposition Internal entropy production is decomposed into two components: structural surface frictional slip dissipation and damage evolution dissipation. ; Take typical calculation points in region T-1: s T =14.0MPa, structural surface shear slip strain rate e T =1.5×10 -6 s -1 Damage energy release rate Y T =2.8 MJ / m³ (obtained by differentiating the Helmholtz free energy with respect to damage), damage evolution rate =3.71×10 - 6 s -1 (Assuming 2 hours per step). Substituting: Friction slip term = 21.0 W / m³, Damage dissipation term = 10.39 W / m³. S int = 3.67×10 -5 W / (kg·K).

[0083] Among them, frictional slip dissipation accounted for 66.9% and damage evolution dissipation accounted for 33.1%, indicating that energy dissipation in this region is mainly due to frictional energy dissipation on structural surfaces.

[0084] S33, damage energy release rate and irreversible dissipation power Substitute muscle unit parameters Y T = 0.5625 MJ / m³, irreversible power dissipation of the system: Ohm = σ T ·e T + Y T · = 23.09 W / m³.

[0085] (Note: here) Y T When the local value of the tendon unit is 2.8 MJ / m³, Ω = 31.39 W / m³, consistent with step S42; the muscle unit value is used to demonstrate the derivation of free energy. S34, Critical Entropy Production Rate Engineering Threshold Calibration Critical entropy production rate calibration equation: ; Microseismic sensors were deployed in the upper and lower roadways of the mining area and around the working face, with a sampling frequency of 1000Hz. Microseismic datasets corresponding to three rockbursts and five severe spalling disasters that occurred in the 1100m–1300m section of the mine over the past four years were retrieved. 36 valid critical samples (meeting the requirement of at least 30 samples) were selected. After removing three outliers using a 95% confidence interval, 33 samples remained. The arithmetic mean of the radiation energy was used. Monitoring and control unit volume V unit =125m³ (5m×5m×5m), time window Δ t =300s (5min). Substitute: S cr =1.62×10 -7 W / (kg·K).

[0086] Calibration S cr =1.62×10 -7 W / (kg·K) is written into the early warning module of the microseismic monitoring system in the mining area. The system calculates real-time data in 5-minute intervals. S int (t) and together with S cr Comparison.

[0087] S4, Mechanism-Thermal Dual-Coupling Catastrophic Criterion and Risk Zoning S41, Dual-threshold joint disaster criterion Critical value of elastic energy storage in rock mass W M,cr The energy density was calibrated to 0.80 MJ / m³ (corresponding to the critical storage density for rockburst in hard rock) by indoor rock mass failure tests. A high-risk instability state is defined when all three conditions are met simultaneously. A new constraint on entropy production growth rate is also included. The system is forced to maintain thermodynamic steady-state dissipation, thus preventing abrupt changes in entropy production.

[0088] S42, Risk Zoning Quantification and Control Based on the joint criteria, the mining area is divided into three risk levels as shown in Table 9: Table 9 Risk Level Classification The initial zoning results for this mining area were as follows: stable zone accounted for approximately 55%, controllable zone accounted for approximately 35%, and high-risk zone accounted for approximately 10% (mainly concentrated in the T-2 area and the stress concentration corners of the pillars). After the S14 decompression treatment, the T-2 area was downgraded from a high-risk zone to a controllable zone, and the proportion of high-risk zone decreased to approximately 4%.

[0089] S5, Force-Heat Synergistic Multi-Objective Optimization Solution S51, Multi-objective optimization function Construct a dimensionless normalized multi-objective optimization function: ; In the formula Δ W M,allow =0.30 MJ / m³ (Maximum permissible safe energy release value, determined by...) W M,cr (Determined with a safety factor of 2.5) s T,lim =18.0MPa S cr =1.62×10 -7 W / (kg·K). The smaller the F value, the more stable the mining conditions.

[0090] S52, Objective calibration of weighting coefficients Table 10 shows the coefficients of variation for three indicators, based on 48 sets of historical excavation data from the past 6 months for this mining area. Table 10. Coefficient of Variation of Indicators Weights and w 1 + w 2 + w 3 =0.324+0.405+0.270=0.999≈1, which meets the normalization requirement. The stress index has the largest weight (0.405), indicating that stress fluctuations have the most significant impact on system stability in the historical data of this mining area.

[0091] S53, Constraints and Optimization Solution Constraints: W aft ≤ W pre (Energy storage does not increase after excavation); 1.5m ≤ L s ≤3.5m (upper and lower limits of step distance); t gap ≥ t gap,min = 2.0h (minimum thermodynamic relaxation time, determined by surrounding rock convergence monitoring); The Sequential Quadratic Programming (SQP) algorithm is used to sample the parameter vector. X =[ L s , t gap , i ] Perform optimization solution ( i (The angle between the direction of advancement and the strike of the ore body). The optimization comparison of the three typical working conditions is shown in Table 11 below: Table 11 Comparison data of optimization under typical working conditions Optimization results: L s =2.0m (rounded to the nearest 0.5m module, between 1.5 and 2.5m). t gap =3.0h (rounded to 0.5h, which is greater than the minimum relaxation time of 2.0h). i =65°. Objective function F=0.543, a 37.4% reduction compared to the traditional empirical scheme (F=0.867), and the maximum energy release per excavation decreased from 0.28 to 0.17 MJ / m³, a reduction of 39.3%. The optimized parameters were output to the mining operation instructions, and the drilling rig was used for drilling at the specified azimuth angle of 65°.

[0092] S6, Implementation of Microseismic Inversion and Gradient Iterative Closed-Loop Control S61, Real-time Inversion of On-site Entropy Production Real-time inversion equation for on-site entropy production: ; Hardware deployment: Twelve microseismic sensors are deployed in the upper and lower roadways of the mining area and around the working face at a grid spacing of 18m. The sensors are embedded in the surrounding rock boreholes (hole depth 1.8m), coupled with anchoring agent, and connected to the underground data acquisition station via coaxial cable. The sampling frequency is 1000Hz, and the data is uploaded to the ground monitoring server in real time.

[0093] Taking a typical 5-minute monitoring window as an example: there are 8 effective rupture events, with radiation energies of 320, 450, 280, 610, 390, 520, 410, and 360 J respectively, totaling Σ E seis,l =3340J. Monitoring and control unit volume. V =125m³. Substitute: S int ( t ) = 1.04×10 -7 W / (kg·K), S int ( t ) / S cr = 0.642 。

[0094] The value is 0.5 S cr ~0.85 S cr The range is within the normal fluctuation range of the controllable area.

[0095] S62, Parameter Adaptive Iterative Correction Back-sampling parameter vector X =[ L s , t gap , i Iteration equation: ; Adaptive step size α = α0 ·| S int - S cr | / S cr Initial reference step size α 0 =0.1. Taking the above monitoring window as an example, | S int - S cr | / S cr =|0.642-1.0|=0.358 (with S cr (normalized bias), then: α = 0.1 × 0.358 = 0.0358.

[0096] The server automatically calculates the gradient of the objective function. (Increased step size makes) F (Increasing the altitude, spacing, and angle causes F to decrease). Update parameters: L s = 2.0-0.0358×0.152×10 = 2.0-0.054 = 1.95 ≈ 2.0m; t gap = 3.0-0.0358×(-0.089)×10 = 3.0 + 0.032 = 3.03 ≈ 3.0h; θ = 65-0.0358×(-0.045)×100 = 65 + 0.16 = 65.2 ≈ 65°; The parameters changed only slightly in this iteration, so we will continue to sample data while maintaining the current optimized parameter combination.

[0097] S63, Closed-Loop Execution Process and Typical Event Handling The complete closed-loop process is as follows: "Measured signal → Entropy production inversion → Deviation determination → Gradient calculation → Parameter update → Command issuance → On-site execution → Re-measurement". The following records a typical emergency control event during the data recovery period in this embodiment: [Event Time] During the 12th cycle of mining, near the T-1 area of ​​the -1250m mid-level stope.

[0098] [Triggering Process] Within the monitoring window of 14:30 (5 minutes), the number of effective microseismic events surged to 22, with a total radiated energy of 18600 J. The inversion yielded: Sint ( t = 18600 / 3.208×10 10 = 5.80×10 -7 W / (kg·K); S int ( t ) / S cr = 5.80×10 -7 / 1.62×10 -7 = 3.58 > 1.0; S int ( t )≥ S cr Immediately trigger emergency control: (1) Stop rock drilling and blasting at the current working face and evacuate personnel to the safe chamber; (2) Construct 110mm stress relief boreholes within 6m of the working face, with a hole depth of 5m, a hole spacing of 1.0m, arranged in a fan shape, and a total of 12 holes; (3) Intensify monitoring based on the intensity of excavation disturbance.

[0099] [Effectiveness of the treatment] Monitoring window at 15:05 after construction S int It fell back to 1.05×10 -7 (0.65) S cr At 15:35, it further decreased to 7.8×10 -8 (0.48) S cr <0.5 S cr After maintaining a stable state for 35 minutes, operations resumed upon safety confirmation. Following resumption, the mining step distance in the area was further reduced from 2.0m to 1.5m, and the interval was extended from 3.0h to 4.0h. No further entropy production exceedances occurred in the subsequent three cycles.

[0100] Table 12 shows a comparison between mining efficiency and safety indicators: Table 12 Comparison of mining efficiency and safety indicators Analysis of the technical effects of this embodiment: (1) Nonlinear constitutive model enhances deep adaptability: The third-order nonlinear constitutive model of the muscle energy storage unit in e M When the value is 0.01, the stress is 4.6% higher and the energy storage is 4.9% higher than that of linear elastic stress, which avoids the underestimation of deep high-energy-storage rock masses by the traditional linear elastic assumption, making the risk judgment more conservative and safer.

[0101] (2) Force-thermal coupling criterion for capturing dynamic instability: The dual threshold criterion simultaneously includes the energy storage state ( W M ) and dissipation rate ( S int ) and the time-varying gradient of entropy production rate ( The entropy production mutation event in the 12th cycle was successfully warned and handled. S int / S cr =3.58), while the traditional static elastic strain energy threshold method still showed 1 hour before the event. W M =0.52MJ / m³ < W M,cr There was no warning.

[0102] (3) Objective calibration of all parameters to avoid subjectivity: The weight coefficients are objectively assigned by normalizing the inverse of the historical coefficient of variation. w 1 =0.324, w 2 =0.405, w 3 =0.270), critical entropy production rate S cr Based on statistical calibration of 33 sets of historical disaster microseismic samples, all physical parameters have clear indoor / field calibration methods, eliminating the uncertainty of manual experience-based assignment.

[0103] (4) Closed-loop adaptive control achieves dynamic optimization: The closed-loop system of real-time microseismic inversion → gradient iteration → parameter update makes the control amplitude automatically increase as the system approaches the critical state. α ∝| S int - S cr | / S cr In stable areas, maintain efficient mining (step distance 3.5m, interval h), moderately tighten mining in controllable areas, and immediately respond to emergencies in high-risk areas to achieve a dynamic balance between safety and efficiency.

[0104] In summary, this invention discloses an optimization method for deep metal ore mining based on muscle-tendon bionics and thermodynamic entropy production, belonging to the field of deep mining engineering technology. Addressing the technical problems of frequent energy release disasters in deep, high-stress metal mines, the lack of quantitative data for mining parameters, and the absence of force-thermal coupling theoretical support, this invention divides the stope into two types of bionic mechanical units based on differences in rock mass integrity: one corresponding to the energy storage characteristics of skeletal muscle, and the other to the force dissipation characteristics of tendons. Core parameters are calibrated, and a set of muscle-tendon coupled nonlinear biomechanical constitutive equations is constructed. The stope is defined as an open non-equilibrium thermodynamic system, and an internal irreversible entropy production rate equation is constructed and the critical entropy production rate is calibrated. A dual-threshold joint disaster criterion, including static energy constraints and dynamic entropy production rate constraints, is established to achieve quantitative zoning of stope risks. A force-thermal synergistic multi-objective optimization function is constructed to solve for the combination of mining parameters. Based on real-time inversion of entropy production rate using on-site microseismic monitoring data, a gradient iterative algorithm is used to adaptively correct the mining parameters, forming a closed-loop control system. This invention enables accurate prediction of disaster risks in deep mining areas and scientific dynamic optimization of mining parameters. It achieves cross-scale quantitative mapping from biological mechanisms to physical and mechanical models and then to thermodynamic criteria, significantly improving the accuracy of risk warning in deep mining areas and the scientific nature of mining parameters, and effectively curbing the occurrence of dynamic disasters such as rock bursts.

[0105] It should be noted that the present invention is not limited to the above-described embodiments. The above embodiments are merely examples, and any embodiments that have the same structure and perform the same effects as the technical concept within the scope of the present invention are included within the scope of the present invention. Furthermore, various modifications that can be conceived by those skilled in the art to the embodiments, and other ways of constructing by combining some of the constituent elements of the embodiments, without departing from the spirit of the present invention, are also included within the scope of the present invention.

Claims

1. An optimized method for deep metal ore mining based on muscle-tendon biomimicry and thermodynamic entropy production, characterized in that, Includes the following steps: S1. Based on the differences in rock mass integrity, the mining area rock mass is divided into at least two types of biomimetic mechanical units. The two types of biomimetic mechanical units correspond to the energy storage characteristics of skeletal muscle and the force dissipation characteristics of tendon, respectively, and the biomimetic mechanical parameters of each unit are calibrated. S2. Based on the biomimetic mechanical parameters, a set of coupled nonlinear biomechanical constitutive equations for the two types of biomimetic mechanical units is constructed respectively. The set of constitutive equations is used to describe the nonlinear energy storage behavior and progressive damage evolution behavior of rock mass under excavation disturbance. S3, define the mining site as an open non-equilibrium thermodynamic system, construct the internal irreversible entropy production rate equation based on the constitutive equation set, and calibrate the critical entropy production rate; S4. Based on the constitutive equation set and the internal irreversible entropy production rate equation, a dual-threshold joint catastrophic criterion including static energy constraints and dynamic entropy production rate constraints is established, and the mining area is quantitatively divided into at least three risk levels accordingly. S5. Based on the dual-threshold joint disaster criterion, construct a force-thermal synergistic multi-objective optimization function and solve for the combination of recovery parameters; S6. Based on the real-time inversion of entropy production rate from the on-site microseismic monitoring data, when the entropy production rate deviates from the critical entropy production rate by more than a preset threshold, the combination of back sampling parameters is adaptively corrected based on the gradient iteration algorithm to form a closed-loop control.

2. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 1, characterized in that, In step S1, the biomimetic mechanical unit includes: a muscle energy storage unit, corresponding to a hard rock region with good integrity and undeveloped fissures, which characterizes the flexible energy storage and progressive deformation characteristics of skeletal muscle. The nonlinear stiffness correction coefficient of the muscle energy storage unit is calibrated to characterize the nonlinear hardening or softening characteristics of deep high-stress rock masses. The expression is as follows: ; In the formula, This is the correction factor for the nonlinear stiffness of the rock mass; It is the static elastic modulus; The dynamic elastic modulus under unloading is the instantaneous deformation modulus under unloading disturbance during deep excavation. The limit strain for indoor triaxial unloading.

3. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 2, characterized in that, In step S1, the biomimetic mechanical unit further includes: a tendon force dissipation unit, corresponding to the fractured region with a dense network of joints and fissures, characterizing the stress sieving, friction dissipation and progressive fatigue damage characteristics of the tendon. The tendon stress dispersion coefficient of the calibrated tendon force dissipation unit is used to characterize the stress homogenization and dissipation capacity of the joint network. The expression is: ； In the formula, η T is the tendon stress dispersion coefficient, with a value range of (0,1); ξ The number of joint groups in the surrounding rock; ρ f The surface density of the fracture; φ The internal friction angle of the joint interface characterizes the roughness and interlocking degree of the crack contact surface; The damage sensitivity index and the bio-rock mass mapping ratio of the tendon force dissipation unit are calibrated. The damage sensitivity index characterizes the sensitivity of the damage evolution rate to changes in stress level, and the bio-rock mass mapping ratio maps the fatigue damage evolution law of the tendon tissue in the organism to the damage evolution of the rock mass structural surface. The expression for the damage sensitivity index is: ； In the formula, m The damage sensitivity index; Δ D 1 Δ D 2 The damage increments are represented under two different stress levels. σ T1 , σ T2 The shear stress was determined for two groups of differentiated tests. The expression for the biomass-rock mass mapping ratio coefficient is as follows: ； In the formula, λ The biomass-rock mass mapping ratio coefficient; Δ D T，test The single-stage damage increment in the indoor cyclic shear test; Δ N test This refers to the number of cyclic loading cycles in the indoor test. σ T, test Apply shear stress level to the test; σ T, lim To calibrate the actual shear stress level applied during the test.

4. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 3, characterized in that, In step S2, the coupled nonlinear biomechanical constitutive equation set includes: The equivalent principal stress of the muscle energy storage unit is expressed as the superposition of the linear term of the initial elastic modulus and the equivalent strain, and the nonlinear stiffness correction coefficient and the square term of the equivalent strain. The corresponding energy storage density is expressed as the superposition of the elastic energy storage term and the nonlinear energy storage term. The expression for the equivalent principal stress equation of the muscle energy storage unit is: ； The corresponding energy storage density equation is: ； In the formula, σ M The equivalent principal stress of the muscle energy storage unit; E M For different rock masses, the initial elastic modulus is used. ε M This represents the equivalent axial strain of the rock mass. W M The elastic energy storage density of the rock mass; The equivalent transmitted stress of the tendon force dissipation unit is expressed as a function of the stress dispersion coefficient, the equivalent principal stress of the muscle energy storage unit, and the cumulative damage variable. The evolution rate of the cumulative damage variable is expressed as a power function of the bio-rock mass mapping ratio coefficient, the stress level to the ultimate bearing capacity ratio, and the cumulative damage variable. The expression for the equivalent transmitted stress of the tendon force dissipation unit and the continuous fatigue damage evolution equations is as follows: ； ； In the formula, σ T This is the equivalent stress transfer unit for tendon force dissipation. D T The cumulative tendon injury variable takes values ​​[0,1], where 0 represents intact tendon and 1 represents complete patency failure. N This refers to the number of excavation disturbance cycles; σ T, lim This represents the ultimate shear capacity of the structural surface. m Damage sensitivity index; n The damage attenuation index; The rate of evolution of the cumulative tendon injury variable with the number of excavation disturbance cycles is the damage increment generated in a single excavation cycle. The corresponding damage iterative update equation is: ； In the formula, The cumulative tendon damage variable after N+1 excavation disturbance cycles; The cumulative tendon damage variable after the Nth excavation disturbance cycle.

5. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 4, characterized in that, In step S3, the internal irreversible entropy production rate equation is decomposed into a structural surface friction slip dissipation entropy production term and a damage evolution dissipation entropy production term. The structural surface friction slip dissipation entropy production term is determined by the product of the equivalent transmitted stress and shear slip strain rate of the tendon force dissipation unit, and the damage evolution dissipation entropy production term is determined by the product of the structural surface damage energy release rate and the damage evolution rate. The expression for the internal irreversible entropy production rate is: ； In the formula, S int The rate of irreversible entropy production within the rock mass; T abs This refers to the absolute temperature downhole. ε T The shear slip strain rate of the structural surface; Y T The energy release rate due to structural surface damage; This represents the damage evolution rate.

6. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 5, characterized in that, In step S3, the calculation of the calibrated critical entropy production rate includes: retrieving the microseismic dataset corresponding to historical rockburst disaster cases in the target mine; calculating the microseismic radiation energy corresponding to each disaster case based on the microseismic dataset; performing statistical analysis on the microseismic radiation energy to determine the critical microseismic radiation energy threshold; and calculating the critical entropy production rate based on the critical microseismic radiation energy threshold, the rock mass calculation unit volume, and the downhole absolute temperature, as expressed below: ； In the formula, S cr The critical entropy production rate of rockburst; V unit The size of the grid cell in the numerical model of rock mass calculation unit volume; E seis,cr,i This refers to the microseismic radiation energy of a single event corresponding to a critical catastrophe. k To effectively count the number of samples.

7. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 6, characterized in that, In step S4, the dual-threshold joint catastrophic criterion includes: the rock mass elastic storage density does not exceed the critical value of the rock mass elastic storage; the internal irreversible entropy production rate does not exceed the critical entropy production rate; and the time-varying gradient of the entropy production rate does not exceed zero, so as to force the system to be in thermodynamic steady-state dissipation; the expression of the criterion is: ； In the formula, W M,cr This is the critical value for elastic energy storage in rock mass; The gradient represents the time-varying entropy production rate.

8. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 7, characterized in that, In step S4, the step of quantifying and dividing the mining area into at least three risk levels includes: High-risk areas: ; Controllable area: ; Stable region: .

9. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 7, characterized in that, In step S5, the force-thermal synergistic multi-objective optimization function takes minimizing the comprehensive objective function value as its optimization objective. The comprehensive objective function value is determined by the weighted sum of the normalized ratio of the maximum energy release from a single excavation to the maximum allowable safe energy release value, the normalized ratio of the maximum transmitted shear stress generated during excavation to the ultimate shear bearing capacity of the structural surface, and the normalized ratio of the maximum entropy production rate from a single excavation to the critical entropy production rate; the expression is: ； In the formula, F The value of the comprehensive objective function; w 1 、w 2 、w 3 These are the normalized weighting coefficients, and the sum of the three is 1. Δ W M, max This represents the maximum energy release from a single excavation. Δ W M, allow To the maximum permissible safe energy release value; σ T, max To generate the maximum transmitted shear stress during excavation; S int, max This represents the maximum entropy production rate for a single excavation. The constraints are as follows: ； In the formula, W pre 、W aft The energy storage density of the rock mass before and after excavation; L s This refers to the single sampling step distance; L s, min 、L s, max These are the upper and lower limits of the back-mining step distance; t gap This refers to the interval between excavation and resting periods. It is the minimum thermodynamic relaxation time.

10. The optimized method for deep metal ore mining based on tendon biomimicry and thermodynamic entropy production according to claim 9, characterized in that, In step S6, the inversion equation for the entropy production rate is: ； In the formula, S int ( t )for t Real-time entropy production rate; V To monitor and control the rock mass volume; l ( t )for t Accumulated number of effective microseismic events at any given time; E seis, l ( t ) is the first l Energy radiated from a microseismic event; The process of adaptive iterative correction of parameters is as follows: The sampling parameters to be optimized are formed into a vector. X The established iterative equation is: ； In the formula, For the first K The parameter vector retrieval at the next iteration; For the first K+1 The parameter vector retrieval at the next iteration; X This is a data acquisition parameter vector, containing step size, rate, interval, and timing. The gradient of the objective function characterizes the descent direction for parameter optimization. α The calibration formula for the adaptive iteration step size is: ； in, α 0 The initial iteration reference step size is preset to 0.1.

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