Renormalization group early warning method for fatigue disturbance instability of irregular point column-packing system

Abstract

The present application relates to the technical field of mine safety and rock control, and particularly relates to a renormalization group early warning method for fatigue disturbance instability of an irregular point column-filling body system, which comprises the following steps: calculating an initial critical probability, a synergistic reinforcement coefficient and a damage variable of the point column-filling body, calculating a point column-filling body system instability probability according to field monitoring data and the synergistic reinforcement coefficient, and calculating a dynamic critical probability according to the initial critical probability and the damage variable; calculating a comprehensive early warning index according to the point column-filling body system instability probability and the dynamic critical probability, and performing graded early warning according to the comprehensive early warning index. The renormalization group early warning method for fatigue disturbance instability of the irregular point column-filling body system is used to determine a theoretical critical probability for a specific point column system by establishing a multi-scale analysis framework, and to realize a change from post-event alarm to pre-event early warning by integrating synergistic reinforcement effect and fatigue damage dynamic evolution.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of mine safety and strata control, and particularly relates to a renormalization group early warning method for fatigue disturbance instability of an irregular point column-filling body system. BACKGROUND

[0002] In a point column upward horizontal slicing filling mining method, the point column serves as a permanent support structure, and together with the filling body in a mined-out area, forms a complex mechanical system, namely a point column-filling body system. The stability of the system is the lifeline for ensuring mining safety. For a long time, the prediction method for the instability of the system mainly develops along the following three levels, but its limitations are increasingly prominent.

[0003] (1) Engineering analogy method and limit equilibrium method based on experience: the core is to calculate the safety factor of the point column according to the experience formula of the similar mine or the simple static equilibrium condition. This method ignores the interaction between the point column and the filling body, the wrapping reinforcement, the dynamic load disturbance and the spatial correlation effect of the system, and the calculation result cannot reflect the real stress distribution and the potential failure mode, especially cannot be applied to complex geological conditions or new mining design. The critical value adopted by the experience analogy method or the safety factor method lacks a strict physical theoretical basis, and cannot reflect the specificity of a specific point column configuration.

[0004] (2) Numerical simulation method based on continuum mechanics: with the development of computer technology, numerical simulation such as finite element method (FEM) and finite difference method (such as FLAC3D) has become the mainstream. This kind of method can finely analyze the stress-strain field of the point column and the filling body, and carry out parameterized research. However, its effectiveness is seriously dependent on the accuracy of the constitutive model and the reliability of the input parameters. More importantly, the traditional numerical simulation regards the point column as a regular arrangement of isolated units, and cannot accurately describe the complex topological structure and mechanical correlation network of the irregular point column system. It is difficult to efficiently simulate the fatigue damage accumulation process of the system under the cyclic blasting dynamic load formed by a large number of point columns, ignores the strengthening effect of the point column when wrapping the filling body, and even cannot naturally reveal the critical instability state from quantitative change to qualitative change of the system. The calculation amount is large, and it is difficult to realize real-time early warning.

[0005] (3) Early warning method based on field monitoring data: this method relies on a sensor network for real-time monitoring of microseisms, stress, displacement and the like. The current technology mostly stays in the primary stage of setting a threshold value for a single physical quantity for alarm (such as stress over-limit alarm). Its main drawbacks are that the monitoring and mechanism are disconnected, the alarm threshold value is usually determined based on experience or numerical simulation, and lacks a physical basis starting from material damage mechanics; and the data are analyzed in isolation, and multiple source information cannot be fused into a comprehensive index representing the overall health status of the system, resulting in that the early warning belongs to post-event warning, and the early identification of the precursor of instability cannot be realized.

[0006] In summary, the existing prediction methods have a common bottleneck: failing to effectively unify and quantitatively characterize the synergistic reinforcement effect of the filling body, the damage accumulation effect of the fatigue load, and the overall critical instability behavior of the point column group system from the physical nature of the system phase change. Empirical methods and single monitoring methods lack theoretical depth, and numerical simulation methods face the dual challenges of computational efficiency and theoretical framework when solving system critical behavior problems. Therefore, there is an urgent need in the field for a new method that can integrate mechanism and data, balance static strength and dynamic damage, and predict critical instability from a system level. SUMMARY

[0007] The purpose of the present application is to provide a renormalization group early warning method for fatigue disturbance instability of an irregular point column-filling body system, which solves the above technical problems.

[0008] To achieve the above purpose, the present application provides a renormalization group early warning method for fatigue disturbance instability of an irregular point column-filling body system, the specific steps are as follows:

[0009] Step S1: establishing a statistical physics model based on the irregular point column-filling body system, and calculating an initial critical probability according to the statistical physics model;

[0010] Step S2: calculating the synergistic reinforcement coefficient and the damage variable of the point column-filling body;

[0011] Step S3: calculating the instability probability of the point column-filling body system according to the field monitoring data and the synergistic reinforcement coefficient, and calculating the dynamic critical probability according to the initial critical probability and the damage variable;

[0012] Step S4: calculating a comprehensive early warning index according to the instability probability of the point column-filling body system and the dynamic critical probability, and performing graded early warning according to the comprehensive early warning index.

[0013] Preferably, step S1 is specifically as follows:

[0014] Step S11: establishing a statistical physics model based on the irregular point column-filling body system, defining the state of the first point column in the irregular point column-filling body system as a binary variable, and the binary variable is as follows:

[0015] , representing that the state of the first point column at the t time is a stable state; , representing that the state of the first point column at the t time is an unstable state;

[0016]

[0017] ​​​​​​The irregular point-pillar-filling system is divided into multiple cells according to the spatial coordinates of the point-pillars by using the Thiessen polygon diagram division method, and each cell contains a plurality of point-pillars which are set at a distance in space;

[0018] Step S12: for the first cell, the effective state of the cell after the coarse graining is determined according to the state of the point-pillars contained in the cell according to the majority rule ; ;

[0019] Step S13: the renormalization group transformation equation is established as follows:

[0020] ;

[0021] wherein, p is the instability probability of the system after the size transformation, p is the instability probability of the irregular point-pillar-filling system, is the renormalization group transformation function, representing the functional relationship between p and p ; ; ;

[0022] Step S14: the initial critical probability p0 is obtained by solving the fixed point equation as follows:

[0023] ;

[0024] wherein, p0 is the fixed point of the renormalization group transformation.

[0025] Preferably, in step S2, the synergistic reinforcement coefficient and the damage variable are calculated as follows:

[0026] Step S21: sample preparation;

[0027] The sample includes a point-pillar sample, a filling sample and a point-pillar-filling composite sample;

[0028] Step S22: axial cyclic loading fatigue tests are performed on the point-pillar sample and the point-pillar-filling composite sample, the load spectrum applied is determined according to the field blasting vibration data, and the fatigue life of the two samples is obtained;

[0029] Step S23: the synergistic reinforcement coefficient and the damage variable of the point-pillar-filling are calculated,

[0030] The calculation formula of the synergistic reinforcement coefficient is as follows:

[0031] ;

[0032] wherein,​​ and The fatigue life of the point column-filler composite specimen and the point column specimen are respectively.

[0033] According to Miner's linear cumulative damage rule, the damage variable of the point column is defined as follows: , The calculation formula is as follows:

[0034]

[0035] in, To load the number of loop iterations.

[0036] Preferably, the instability probability of the point column-filling system is calculated as follows:

[0037] Step S31a: Construct the point column health state function. as follows:

[0038] ;

[0039] in, For the first The microseismometer within the set range of each point column, from the initial moment to... The cumulative energy of micro-vibrations at any given moment. For the first The rate of change of stress at each point column, For the first The deformation of a point column. , as well as These are the cumulative energy threshold for microseismic events, the rate of change of stress threshold, and the deformation threshold, respectively.

[0040] Step S32a: Determine the state of the point column element;

[0041] when ,but ;when ,but ; To set the state threshold of the point column unit;

[0042] Step S33a: Calculate the instability probability of the point column-filling system;

[0043] statistics The number of points and columns that are constantly in an unstable state Instability probability of point-column-filling system The calculation formula is as follows:

[0044] ;

[0045] in, Total number of point columns.

[0046] Preferably, the process of calculating the dynamic critical probability according to the initial critical probability and the damage variable is as follows:

[0047] Step S31b: Calculate the average fatigue damage degree The calculation formula is as follows:

[0048] ;

[0049] Wherein, is the cumulative blasting frequency;

[0050] Step S32b: Calculate the dynamic critical probability The calculation formula is as follows:

[0051] ;

[0052] Wherein, is the dynamic critical probability at the moment .

[0053] Preferably, in step S4, the comprehensive early warning index The formula is as follows:

[0054] ;

[0055] Wherein, and are weight coefficients, which need to be normalized. When the point column state is the main basis and the point column state trend is the secondary basis, then ; otherwise, ;

[0056] The normalized early warning formula is:

[0057] ;

[0058] Wherein, is the initial critical probability, is the maximum deterioration rate.

[0059] Preferably, the comprehensive early warning index is used for grading early warning, and the specific grading is as follows:

[0060] When , it is a first-level early warning;

[0061] When , it is a second-level early warning;

[0062] When , it is a third-level early warning;

[0063] Wherein, and the first and second early warning thresholds.

[0064] Therefore, the application has the beneficial effects of the above-mentioned renormalization group early warning method for fatigue disturbance instability of an irregular point column-filling body system, establishing a multi-scale analysis framework, calculating a dynamic critical probability according to an initial critical probability and a damage variable, determining a theoretical critical probability for a specific point column system, correcting a point column-filling body system instability probability through a synergistic enhancement coefficient, and fusing a synergistic enhancement effect and fatigue damage dynamic evolution to realize a change from post-event alarm to pre-event early warning.

[0065] The technical solutions of the application are described in further detail below with reference to the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a mining irregular point column setting map for this embodiment;

[0067] Figure 2 is a flowchart of the application;

[0068] Figure 3 is a point column-filling body combined structure sample result graph;

[0069] Figure 4 is a point column-filling body combined structure sample fatigue loading curve;

[0070] Figure 5 is a point column fatigue loading curve;

[0071] Figure 6 is a filling body confining pressure stress in-situ monitoring data curve graph.

[0072] REFERENCE NUMERALS

[0073] 1, sample point column; 2, filling body; 3, actual point column. DETAILED DESCRIPTION

[0074] In the description of the present application, it should be noted that the terms "upper", "lower", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present application is usually placed, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In the description of the present application, it should be noted that, unless otherwise explicitly specified and limited, the terms "arrangement", "installation", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be connected inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0075] The embodiments of the present application will be described in detail below with reference to the drawings.

[0076] The application background of the present embodiment is as follows:

[0077] Mine overview: The point-pillar upward horizontal slicing and filling mining method is used in the Hongniu Copper Mine in Shangri-La City.

[0078] Ore body characteristics: The ore body of the Hongniu Copper Mine is branch-combined, has variable occurrence, and has relatively serious negative variation, so in order to maximize the recovery of high-grade ore, the point pillars are intentionally left in low-grade ore sections, resulting in irregular spatial distribution of the point pillars in the stope.

[0079] Point pillar system: Taking the 3987m section as the research object, there are 9 irregularly distributed actual point pillars 3 in the west three panel stope, the size of the actual point pillar 3 is 4m x 4m, the point pillar spacing is 12m, and the planar coordinate distribution is irregular, as shown in Figure 1 .

[0080] As shown in Figure 2 , the specific steps of the present embodiment are as follows:

[0081] Step S1: Establish a statistical physics model based on the irregular point pillar-filling body system, and calculate the initial critical probability according to the statistical physics model.

[0082] The present embodiment has 9 point pillars, and the entire stope is divided into 9 polygonal regions, each region containing and only containing one point pillar. Adjacent point pillars in space are associated to form a topological structure for renormalization group analysis. Each Voronoi polygon diagram and the point pillar at the center thereof are defined as a basic cell;

[0083] Merge 4 basic cells (determined according to the adjacency relationship of the Voronoi diagram) into one super cell. The effective state of the super cell is determined by the state of the 4 included point columns:

[0084] When 3 or more of the 4 point columns are in the "unstable" state, the super cell is judged to be "unstable".

[0085] According to the rule, the probability of instability of the super cell system at the new scale is related to the instability probability of the original system (non-regular point column-filling body system) as follows:

[0086] ;

[0087] Where, is the probability of instability of all 4 point columns, is the probability of instability of any 3 point columns and stability of 1 point column. .

[0088] Step S2: Calculate the synergistic reinforcement coefficient and damage variable of the point column-filling body.

[0089] Retrieve the ore core from the low-grade ore section on site and process it into standard point column samples.

[0090] Prepare the filling body sample and point column-filling body combined structure sample according to the on-site ratio. The point column-filling body combined structure sample is shown in Figure 3 , and the sample size is a cylinder with a diameter of 50 mm and a height of 100 mm. Among them, the lithology of point column 1 is skarn, and the size of point column 1 is a diameter of 20 mm and a height of 100 mm; the sand ratio of filling body 2 is , and the concentration is , wrapped outside point column 1. The prepared combined structure sample is cured in a curing box for 28 days for mechanical test.

[0091] Perform triaxial axial cyclic loading fatigue test, as shown in Figure 4 and Figure 5 . The confining pressure during triaxial test is determined according to the filling in-situ stress monitoring data, as shown in Figure 6 , and the confining pressure in this embodiment is 120 kPa.

[0092] The average fatigue life of the point column monomer sample measured by the test is: 100000 cycles;

[0093] The average fatigue life of the composite sample measured by the test is: 130000 cycles.

[0094] The synergistic reinforcement coefficient is: 1.3, and the wrapping of the filling body improves the fatigue life of the point column by . ​

[0095] The cumulative number of blasts in the mining area is 40,000, and the damage variable for the point column is... This indicates that the system has been consumed by fatigue load. Lifespan;

[0096] Step S3: Calculate the instability probability of the point column-filling body system based on the field monitoring data and the synergistic enhancement coefficient, and calculate the dynamic critical probability based on the initial critical probability and damage variables.

[0097] Using 150 days of monitoring data, with a threshold value of 1.0 for the point column unit, and employing a modified point column health state function, none of the three point columns exceeded 1.0, indicating they were in a stable state. The protective effect of the filling material prevented three possible false alarms, making the evaluation results more consistent with engineering realities. The probability of instability in the point column-filling material system is 0, and the current system is in an absolutely safe state.

[0098] Due to fatigue damage, the critical threshold for system instability has increased from the theoretical value. Descending to The system's security boundaries are narrowing.

[0099] Step S4: Calculate the comprehensive early warning index based on the instability probability and dynamic critical probability of the point column-filling body system, and conduct graded early warning based on the comprehensive early warning index.

[0100] Current status ;

[0101] Dynamic critical point ;

[0102] Safety margin is The safety margin is very large.

[0103] Assuming the system is stable , and Take values ​​of 0.6 and 0.3 respectively.

[0104] and The values ​​are 0.4 and 0.6. The early warning strategy focuses more on the changing trend of the system state (time has a higher weight). They believe that a daily decrease in the probability of stability exceeding 5% is a very dangerous probability.

[0105] Calculate the spatial term:

[0106] =0.4×0.6+0.6×0=0.24<0.3;

[0107] Early warning conclusion: the system is in the safest three-level early warning (red early warning) state, from the space item (safety distance), the system seems very safe, but the comprehensive early warning model pays more attention to the dynamic stability of the system (β=0.6); and there is no sign of improvement in the current state of the system (change rate is 0), therefore, the highest level of alarm is given. In fact, multiple point pillars in the stope have been broken, although the stope is stable as a whole, but the red early warning will trigger the mine to launch a comprehensive safety check to confirm whether the system is in a stable but fragile balance state. The instability early warning analysis is completely consistent with the scene.

[0108] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: it can still modify or equivalently replace the technical solutions of the present application, and these modifications or equivalent replacements also cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present application.

Claims

1. A renormalization group early warning method of fatigue disturbance instability of an irregular point-pillar infill system, characterized in that, The specific steps are as follows: Step S1: establishing a statistical physics model based on the irregular point column-filling body system, and calculating the initial critical probability according to the statistical physics model; Step S1 is specifically as follows: Step S11: establishing a statistical physics model based on the irregular point column-filling body system, and dividing the irregular point column-filling body system into a plurality of cells according to the spatial coordinates of the point columns, each cell containing a plurality of point columns with a set distance in space; Step S12: for the first cell, according to the state of the point column contained in the cell, the effective state of the cell after the coarse-graining is determined according to the majority rule ;​ Step S13: the renormalization group transformation equation is established as follows: ； wherein, is the system instability probability after the size transformation, is the instability probability of the irregular point-pillar-charge system, is the renormalization group transformation function, representing the functional relationship between and The spatially adjacent point columns are associated to form a topology for renormalization group analysis, each Voronoi polygon map and the point column at its center are defined as a basic cell, 4 adjacent basic cells are combined into a super cell, the effective state of the super cell is determined by the state of the 4 point columns it contains, when 3 or more of the 4 point columns are in an unstable state, the super cell is judged to be unstable, and the probability of instability of the super cell system The relationship between the instability probability of the irregular point column-filler system is as follows: ； wherein, P is the probability that all 4 columns are unstable, P is the probability that any 3 columns are unstable and 1 column is stable. Step S14: solving the fixed point equation to obtain the initial critical probability , is the fixed point of the renormalization group transformation, ; Step S2: calculating the synergistic reinforcement coefficient and damage variable of the point column-filling body; the specific process is as follows: Step S21: sample preparation; Step S22: performing axial cyclic loading fatigue test on the point column sample and the point column-filling body composite sample, the load spectrum being determined according to the field blasting vibration data, and the fatigue life of the two samples being obtained; Step S23: Calculate the synergistic reinforcement coefficient of the point pillar- filling body and the damage variable , Synergistic enhancement factor The calculation formula is as follows: ； wherein, and are the fatigue life of the point-post-filling composite specimen and the point-post specimen, respectively. According to Miner linear cumulative damage rule, the damage variable of the point column is defined as , The calculation formula is as follows: wherein, is the number of loading cycles; Step S3: calculating the instability probability of the point column-filling body system according to the field monitoring data and the synergistic reinforcement coefficient, and calculating the dynamic critical probability according to the initial critical probability and the damage variable; The instability probability of the point column-filling body system is calculated as follows: Step S31a: constructing a point column health state function, the point column health state function is as follows: ； wherein, is the microseismic cumulative energy of the microseismic meter within the range of the nth point column from the initial time to the time t, is the stress rate of change of the nth point column, is the deformation of the nth point column, , and are respectively a microseismic cumulative energy threshold value, a stress rate of change threshold value, and a deformation threshold value.​​​​ Step S32a: determining the state of the point column unit; When , then ; when , then ; is a setpoint column cell state threshold; Step S33a: calculating the instability probability of the point column-filling body system; Statistics the number of point pillars in an unstable state at a time , the probability of instability of the point pillar-charge system The calculation formula is as follows: ； wherein is the total number of point columns; The process of calculating the dynamic critical probability according to the initial critical probability and the damage variable is as follows: Step S31b: Calculate average fatigue damage degree The calculation formula is as follows: ； wherein, is the cumulative number of bursts; Step S32b: Calculate the dynamic critical probability The calculation formula is as follows: ； wherein is a dynamic critical probability at the moment; Step S4: calculating the comprehensive early warning index according to the instability probability of the point column-filling body system and the dynamic critical probability, and performing graded early warning according to the comprehensive early warning index; In step S4, the comprehensive early warning index is calculated The formula is as follows: ； wherein, and are weight coefficients, which need to be normalized, when the point column state is the main basis and the point column state trend is the secondary basis, then ; otherwise, ; The normalized early warning formula is as follows: ； wherein, is the initial critical probability, is the maximum rate of deterioration.

2. The RG warning method for fatigue disturbance instability of a non-regular point-pillar filling system according to claim 1, characterized in that, Step S11 is as follows: A statistical physical model is established based on the irregular point column-filling body system, and the first... The state of each point column is defined as a binary variable, as follows: , the state of the first point column at the first time is a stable state; the state of the first point column at the first time is a stable state;​ or , the state of the first point column at the first moment is unstable state; and , the state of the first point column at the first moment is unstable state; and , the state of the first point column at the first moment is unstable state; and The irregular point column-filling body system is divided into a plurality of cells according to the spatial coordinates of the point columns by adopting the Thiessen polygon diagram division method, and each cell contains a plurality of point columns with a set distance in space.

3. The RG warning method for fatigue disturbance instability of a non-regular point-pillar filling system according to claim 2, characterized in that, In step S2, the sample includes a point column sample, a filling body sample and a point column-filling body composite sample.

4. The RG warning method for fatigue disturbance instability of a non-regular point-pillar filling system according to claim 3, characterized in that, Graded early warning is performed according to the comprehensive early warning index, and the specific grading is as follows: When Level 1 warning; When a secondary warning; When Level III warning; wherein and are a first and second early warning threshold.

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