Deep hole pre-splitting blasting effect quantitative evaluation and dynamic regulation system and method

Abstract

The application discloses a deep hole pre-splitting blasting effect quantitative evaluation and dynamic regulation system, comprising a data acquisition and input module, an efficiency index calculation engine, an intelligent evaluation and grading module, a parameter response relationship analyzer, a dynamic optimization recommendation engine and a trend monitoring and early warning platform; the data acquisition and input module acquires microseismic energy, theoretical blasting energy, explosive quantity and an adjustment coefficient; the efficiency index calculation engine calculates a blasting efficiency index; the intelligent evaluation and grading module evaluates the blasting efficiency index; the parameter response relationship analyzer establishes a response model; the dynamic optimization recommendation engine generates a blasting parameter optimization scheme; and the trend monitoring and early warning platform tracks a sequence change of the blasting efficiency index and triggers an early warning. The application further discloses a deep hole pre-splitting blasting effect quantitative evaluation and dynamic regulation method. The application solves the defects of the prior blasting effect evaluation method, such as subjectivity, one-sidedness, weak physical correlation, low sensitivity and poor horizontal comparability.

Description

Technical Field

[0001] This invention belongs to the field of mining engineering and rock and soil blasting technology, specifically involving a quantitative evaluation and dynamic control system for deep hole pre-splitting blasting effects, and also involving a method for quantitative evaluation and dynamic control of deep hole pre-splitting blasting effects. Background Technology

[0002] In geotechnical engineering such as mining and tunneling, pre-splitting blasting is a key technology for controlling surrounding rock and weakening hard roofs. Existing methods for evaluating blasting effectiveness suffer from three core flaws: First, evaluations often rely on empirical qualitative judgments or monitoring of single vibration parameters, lacking quantitative indicators directly related to the core objectives of blasting (energy utilization efficiency, rock mass energy induced release). Second, traditional linear energy ratio methods (such as the ratio of monitored energy to explosive energy) cannot accurately reflect the physical nature of the nonlinear propagation of blasting energy in the rock mass and are sensitive to absolute energy values, making fair comparisons between blasts of different scales difficult. Finally, existing methods lack sensitivity, failing to identify subtle improvements in blasting parameter optimization and thus unable to provide a basis for refined control. Therefore, a new quantitative evaluation method based on solid physical principles, with high sensitivity and comparability, is urgently needed. Summary of the Invention

[0003] The purpose of this invention is to provide a quantitative evaluation and dynamic control system for deep hole pre-splitting blasting effects, which solves the problems of existing blasting effect evaluation methods, such as subjective bias, weak physical correlation, low sensitivity, and poor horizontal comparability.

[0004] Another objective of this invention is to provide a method for quantitative evaluation and dynamic control of the effect of deep hole pre-fracture blasting.

[0005] The technical solution adopted in this invention is a quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effects, comprising a data acquisition and input module, an effectiveness index calculation engine, an intelligent evaluation and grading module, a parameter response relationship analyzer, a dynamic optimization recommendation engine, and a trend monitoring and early warning platform. The data acquisition and input module acquires microseismic energy, theoretical blasting energy, explosive quantity, and adjustment coefficients. The effectiveness index calculation engine calculates the blasting effectiveness index. The intelligent evaluation and grading module automatically evaluates the blasting effectiveness index. The parameter response relationship analyzer establishes a response model between the blasting effectiveness index and blasting parameters. The dynamic optimization recommendation engine generates blasting parameter optimization schemes based on the evaluation results and response model. The trend monitoring and early warning platform tracks changes in the blasting effectiveness index sequence and triggers early warnings.

[0006] Another technical solution adopted in this invention is a method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect, comprising the following steps:

[0007] Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area.

[0008] Another feature of the technical solution adopted in this invention is that: Step 1 specifically involves: constructing a response model based on the nonlinear propagation law of blasting energy using a parameter response relationship analyzer, and defining the blasting effectiveness index as S. e .

[0009] Step 2 specifically involves: establishing a quantitative grading standard for blasting effectiveness index through the intelligent evaluation and grading module; The grading standard is: S e When S < 0, the corresponding effect is poor; 0 ≤ S e When S < 0.3, the corresponding effect is moderate; when S ≤ 0.3 e When S < 0.7, the corresponding effect is good; e When the value is ≥ 0.7, the corresponding effect is excellent.

[0010] Step 3 specifically involves: using the data acquisition and input module to perform blasting in the target area and collect data; and using the effectiveness index calculation engine to calculate the blasting effectiveness index S for each blast. e .

[0011] The collected data includes measured microseismic energy, theoretical blasting energy, explosive charge, and adjustment coefficients.

[0012] The blasting effectiveness index is calculated according to formulas (1) and (2); S e =[K r lg(E s )-lg(E p )] / lg(P e (1); E p = E Pi ×P e ×K s (2); Among them, S e E represents the blasting effectiveness index for each blast. s E represents the measured microseismic energy. pP is the theoretical blast energy. e K represents the amount of explosives. r The adjustment factor ranges from 1.0 to 1.3; E Pi K represents the theoretical energy of a unit of explosive. s The energy conversion coefficient has a value ranging from 0.1 to 0.3.

[0013] Step 4 specifically involves: evaluating the blasting effectiveness index of the blasting based on the grading standards through the intelligent evaluation and grading module, and obtaining the evaluation results.

[0014] Step 5 specifically involves: Step 5.1: By dynamically optimizing the recommendation engine, based on the evaluation results, establish the blasting effectiveness index S as shown in equation (3). e A model of the logarithmic response relationship between the value and the blasting parameters; S e = f(lg(L), lg(P) e ), C, F ) (3; Where L is the hole spacing, C is the charge structure score, and F is the free surface condition score; Step 5.2: Based on the logarithmic response relationship model, obtain the partial derivative analysis results of the logarithmic response relationship model. S e / lg(L); Step 5.3: Analyze the results based on partial derivatives. S e / lg(L) generates parameter optimization suggestions; Specifically: when the partial derivative analysis results S e / When lg(L) < 0, the generated optimization suggestions include reducing the hole spacing L; when the partial derivative analysis results S e / lg(P e When )>0 and the marginal effect is significant, the generated optimization suggestions include appropriately increasing the amount of drug per orifice P. e .

[0015] Step 6 specifically involves: calculating the moving average and standard deviation of the blasting effectiveness index for consecutive blasting events in the same area, establishing a trend early warning mechanism and triggering an early warning through the trend monitoring and early warning platform; The trend early warning mechanism is established and triggered as follows: a yellow warning is triggered when the moving average decreases by more than 2 standard deviations; a yellow warning is triggered when the explosive efficiency index S appears. e A red alert is triggered when the value is less than 0.

[0016] Step 3 also includes calculating the relative efficiency index S' according to equation (4). e This is used for longitudinal trend comparison of blasting effects within the area; S' e = S e / S e0 (4); Among them, S' e S is the relative efficiency index. e0 S is the baseline performance index determined in the target area. e This refers to the blasting effectiveness index.

[0017] The beneficial effects of this invention are: This invention provides a quantitative evaluation and dynamic control system and method for deep-hole pre-splitting blasting effects, constructing a precise evaluation and control system for deep-hole pre-splitting blasting effects based on nonlinear logarithmic ratios. This system, through an original quantitative evaluation model, effectively overcomes the shortcomings of traditional methods, such as strong subjectivity, weak physical correlation, and incomparability across different processes, achieving for the first time a fundamental shift from empirical qualitative to scientific quantitative assessment of blasting effects. Its established hierarchical evaluation criteria can clearly and reasonably distinguish the differences in blasting effectiveness under different geological conditions and technological levels. By establishing a parameter response relationship model, it accurately diagnoses the root causes of varying effectiveness, intelligently generates optimization suggestions, and forms a complete intelligent closed loop of "monitoring-evaluation-diagnosis-optimization." Ultimately, this method provides key technical support for risk identification, process optimization, refined parameter control, and standardized effect management in blasting engineering, significantly improving engineering safety and economy. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to the present invention. Figure 2 This is a diagram showing the distribution of Se values ​​on the working surface under different conditions in Embodiment 8 of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0020] Example 1 The deep-hole pre-splitting blasting effect quantitative evaluation and dynamic control system proposed in this embodiment includes a data acquisition and input module, an effectiveness index calculation engine, an intelligent evaluation and grading module, a parameter response relationship analyzer, a dynamic optimization recommendation engine, and a trend monitoring and early warning platform. The data acquisition and input module acquires microseismic energy, theoretical blasting energy, explosive quantity, and adjustment coefficients. The effectiveness index calculation engine calculates the blasting effectiveness index. The intelligent evaluation and grading module automatically evaluates the blasting effectiveness index. The parameter response relationship analyzer establishes a response model between the blasting effectiveness index and blasting parameters. The dynamic optimization recommendation engine generates blasting parameter optimization schemes based on the evaluation results and response model. The trend monitoring and early warning platform tracks changes in the blasting effectiveness index sequence and triggers early warnings.

[0021] Example 2 The method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect proposed in this embodiment is based on the aforementioned quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effect, such as... Figure 1 As shown, it includes the following steps: Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area.

[0022] Example 3 The method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect proposed in this embodiment is based on the aforementioned quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effect, such as... Figure 1 As shown, it includes the following steps: Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Specifically, a response model based on the nonlinear propagation law of blasting energy is constructed using a parameter response relationship analyzer, and the blasting effectiveness index is defined as S. e ; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area.

[0023] Example 4 The method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect proposed in this embodiment is based on the aforementioned quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effect, such as... Figure 1 As shown, it includes the following steps: Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Specifically, a response model based on the nonlinear propagation law of blasting energy is constructed using a parameter response relationship analyzer, and the blasting effectiveness index is defined as S. e ; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 2 specifically involves: establishing a quantitative grading standard for blasting effectiveness index through the intelligent evaluation and grading module; The grading standard is: S e When S < 0, the corresponding effect is poor; 0 ≤ S e When S < 0.3, the corresponding effect is moderate; when S ≤ 0.3 e When S < 0.7, the corresponding effect is good; e When the value is ≥ 0.7, the corresponding effect is excellent; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area.

[0024] Example 5 The method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect proposed in this embodiment is based on the aforementioned quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effect, such as... Figure 1 As shown, it includes the following steps: Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Specifically, a response model based on the nonlinear propagation law of blasting energy is constructed using a parameter response relationship analyzer, and the blasting effectiveness index is defined as S. e ; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 2 specifically involves: establishing a quantitative grading standard for blasting effectiveness index through the intelligent evaluation and grading module; The grading standard is: S e When S < 0, the corresponding effect is poor; 0 ≤ S e When S < 0.3, the corresponding effect is moderate; when S ≤ 0.3 e When S < 0.7, the corresponding effect is good; e When the value is ≥ 0.7, the corresponding effect is excellent; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 3 specifically involves: using the data acquisition and input module to perform blasting in the target area and collect data; and using the effectiveness index calculation engine to calculate the blasting effectiveness index S for each blast. e ; The collected data includes measured microseismic energy, theoretical blasting energy, explosive charge, and adjustment coefficients; The blasting effectiveness index is calculated according to formulas (1) and (2); S e =[K r lg(E s )-lg(E p )] / lg(P e (1); E p = E Pi ×P e ×K s (2); Among them, S e E represents the blasting effectiveness index for each blast. s E represents the measured microseismic energy. p P is the theoretical blast energy. e K represents the amount of explosives. r The adjustment factor ranges from 1.0 to 1.3; E Pi K represents the theoretical energy of a unit of explosive. s This is the energy conversion coefficient, with a value ranging from 0.1 to 0.3. Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area.

[0025] Example 6 The method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect proposed in this embodiment is based on the aforementioned quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effect, such as...Figure 1 As shown, it includes the following steps: Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Specifically, a response model based on the nonlinear propagation law of blasting energy is constructed using a parameter response relationship analyzer, and the blasting effectiveness index is defined as S. e ; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 2 specifically involves: establishing a quantitative grading standard for blasting effectiveness index through the intelligent evaluation and grading module; The grading standard is: S e When S < 0, the corresponding effect is poor; 0 ≤ S e When S < 0.3, the corresponding effect is moderate; when S ≤ 0.3 e When S < 0.7, the corresponding effect is good; e When the value is ≥ 0.7, the corresponding effect is excellent; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 3 specifically involves: using the data acquisition and input module to perform blasting in the target area and collect data; and using the effectiveness index calculation engine to calculate the blasting effectiveness index S for each blast. e ; The collected data includes measured microseismic energy, theoretical blasting energy, explosive charge, and adjustment coefficients; The blasting effectiveness index is calculated according to formulas (1) and (2); S e =[K r lg(E s )-lg(E p )] / lg(P e (1); E p = E Pi ×P e ×K s (2); Among them, S e E represents the blasting effectiveness index for each blast. s E represents the measured microseismic energy. p P is the theoretical blast energy. e K represents the amount of explosives. r The adjustment factor ranges from 1.0 to 1.3; E Pi K represents the theoretical energy of a unit of explosive. s This is the energy conversion coefficient, with a value ranging from 0.1 to 0.3. Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 4 specifically involves: evaluating the blasting effectiveness index of the blasting based on the grading standards through the intelligent evaluation and grading module, and obtaining the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area.

[0026] Example 7 The method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect proposed in this embodiment is based on the aforementioned quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effect, such as... Figure 1 As shown, it includes the following steps: Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Specifically, a response model based on the nonlinear propagation law of blasting energy is constructed using a parameter response relationship analyzer, and the blasting effectiveness index is defined as S. e ; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 2 specifically involves: establishing a quantitative grading standard for blasting effectiveness index through the intelligent evaluation and grading module; The grading standard is: S e When S < 0, the corresponding effect is poor; 0 ≤ S e When S < 0.3, the corresponding effect is moderate; when S ≤ 0.3 e When S < 0.7, the corresponding effect is good; e When the value is ≥ 0.7, the corresponding effect is excellent; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 3 specifically involves: using the data acquisition and input module to perform blasting in the target area and collect data; and using the effectiveness index calculation engine to calculate the blasting effectiveness index S for each blast. e ; The collected data includes measured microseismic energy, theoretical blasting energy, explosive charge, and adjustment coefficients; The blasting effectiveness index is calculated according to formulas (1) and (2); S e =[K r lg(E s )-lg(E p )] / lg(P e (1); E p = E Pi ×P e ×K s (2); Among them, S e E represents the blasting effectiveness index for each blast. s E represents the measured microseismic energy. p P is the theoretical blast energy. e K represents the amount of explosives. r The adjustment factor ranges from 1.0 to 1.3; E Pi K represents the theoretical energy of a unit of explosive. s This is the energy conversion coefficient, with a value ranging from 0.1 to 0.3. Step 3 also includes calculating the relative efficiency index S' according to equation (4). e This is used for longitudinal trend comparison of blasting effects within the area; S' e = S e / S e0 (4); Among them, S' e S is the relative efficiency index. e0 S is the baseline performance index determined in the target area. e The blasting effectiveness index; Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 4 specifically involves: evaluating the blasting effectiveness index of the blasting based on the grading standards through the intelligent evaluation and grading module, and obtaining the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 5 specifically involves: Step 5.1: By dynamically optimizing the recommendation engine, based on the evaluation results, establish the blasting effectiveness index S as shown in equation (3). e A model of the logarithmic response relationship between the value and the blasting parameters; S e = f(lg(L), lg(P) e ), C, F ) (3; Where L is the hole spacing, C is the charge structure score, and F is the free surface condition score; Step 5.2: Based on the logarithmic response relationship model, obtain the partial derivative analysis results of the logarithmic response relationship model. S e / lg(L); Step 5.3: Analyze the results based on partial derivatives. S e / lg(L) generates parameter optimization suggestions; Specifically: when the partial derivative analysis results Se / When lg(L) < 0, the generated optimization suggestions include reducing the hole spacing L; when the partial derivative analysis results S e / lg(P e When )>0 and the marginal effect is significant, the generated optimization suggestions include appropriately increasing the amount of drug per orifice P. e ; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area; Step 6 specifically involves: calculating the moving average and standard deviation of the blasting effectiveness index for consecutive blasting events in the same area, establishing a trend early warning mechanism and triggering an early warning through the trend monitoring and early warning platform; The trend early warning mechanism is established and triggered as follows: a yellow warning is triggered when the moving average decreases by more than 2 standard deviations; a yellow warning is triggered when the explosive efficiency index S appears. e A red alert is triggered when the value is less than 0.

[0027] Example 8 The method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect proposed in this embodiment is applied to a deep-hole pre-splitting blasting project on the roof of a mine working face. Based on the aforementioned quantitative evaluation and dynamic control system for deep-hole pre-splitting blasting effect, as follows... Figure 1 As shown, it includes the following steps: Steps S1-S2: Determine the blasting effectiveness index S e And adopt a four-level evaluation standard (S e When S < 0, the corresponding effect is poor; when S ≤ 0, the effect is poor. e When S < 0.3, the effect is moderate; when S ≤ 0.3, the effect is moderate. e The corresponding effect is good when S <0.7. e (The effect is better when ≥ 0.7).

[0028] S3-S4, Parameter Determination and Reference Calibration: Basic parameters: The explosive is a Class II permissible emulsion explosive for coal mines, E Pi Take 4.5 × 10^6 J / kg; take K according to lithology. s =0.2; K is taken according to the geostress state. r =1.1.

[0029] Standard for haulage roadways: Select the first 30 standard blasting groups (hole spacing 10m, single hole charge P) e =100kg), monitoring average E s =1.03 × 10^3 J. Calculate the average E. p =E Pi ×P e ×K s=9.0 × 10^7 J. The average S for these 30 groups was calculated. e0 ≈0.25 (belongs to the "medium" level).

[0030] Return airway benchmark: 30 standard blasting groups were selected (hole spacing 8m, single hole charge P). e =150kg), monitoring average E s =2.71 × 10^3 J. Calculate the average E. p =1.35×10^8 J. The average S for these 30 groups was calculated. e0 ≈0.52 (belongs to the "good" level).

[0031] S5. Evaluation, Diagnosis, and Regulation Applications: Evaluation of the effect of the duct: Calculate the Se value of 30 blasts in this duct, such as... Figure 2 As shown. According to the statistical standards of this invention: no cases of Se < 0 occurred; the Se values ​​were distributed between 0.1 and 0.6, of which approximately 70% belonged to the "medium" level (0 ≤ Se < 0.3), approximately 30% belonged to the "good" level (0.3 ≤ Se < 0.7), and there were no "excellent" levels. This accurately reflects the low overall efficiency caused by the difference in free surface area and insufficient stress accumulation on the solid coal side. Through analysis using model (III), it was found that... Se / The negative value of log10(L) is significant. Optimization suggestion: Reduce the spacing between blast holes from 10m to 6~8m.

[0032] Comparison of different processes in the return airway: (1) Conventional blasting: Se values ​​from 30 blasts, statistically analyzed according to the standards of this invention: no cases with Se < 0; approximately 20% were of the "medium" grade (0.3 ≤ Se < 0.7), approximately 80% were of the "good" grade (0.3 ≤ Se < 0.7), and no cases of the "excellent" grade with Se ≥ 0.7. This indicates that the conditions on the goaf side are better, but the conventional process has bottlenecks.

[0033] (2) Axial slot blasting: Two blasting operations using the new axial slot blasting technology achieved an average Es of 9.02 × 10^3 J. The calculated Se values ​​were 0.72 and 0.75 respectively (Pe = 100 kg, Ep = 9.0 × 10^7 J). According to the standard of this invention, both satisfy Se ≥ 0.7 and are rated as "excellent". Figure 2 The comprehensive comparison shows that its Se value is significantly higher than that of conventional blasting groups, and it is clearly located in the high-value area on the right end of the distribution map.

[0034] (3) Diagnosis and Conclusion: The evaluation criteria of this invention clearly classify the effect of the axial kerfing process as the highest level, "Excellent," which undeniably proves the significant superiority and breakthrough of this new technology from a quantitative perspective. Analysis shows that this process effectively increases the free surface condition score F by significantly improving the free surface condition score F. Se / F, thus achieving a leap in efficiency level.

[0035] Example of trend early warning: During the construction of a section of the return airway, the 35th to 50th blasts were continuously monitored. After the 46th blast, the moving average S... e The value continuously decreased from 0.55 to 0.40, and was below the historical mean minus two standard deviations (0.52-2). The system triggered a yellow alert when the borehole depth reached the critical value of 0.08 (0.36). Inspection revealed that a drilling rig malfunction caused insufficient drilling depth. After timely repair, S... e The value recovered to above 0.50.

[0036] This embodiment fully verifies the comprehensive effectiveness of the method of the present invention in accurate quantitative evaluation, root cause diagnosis and analysis, intelligent optimization feedback and trend early warning. In particular, the four-level evaluation standard of Se<0, 0≤Se<0.3, 0.3≤Se<0.7 and Se≥0.7 adopted by the present invention can clearly, reasonably and powerfully identify the differences in blasting effects under different geological conditions and technological levels, and realize the scientific and intelligent upgrade of blasting engineering management.

Claims

1. A quantitative evaluation and dynamic control system for deep hole pre-splitting blasting effects, characterized in that, It includes a data acquisition and input module, an efficiency index calculation engine, an intelligent evaluation and grading module, a parameter response relationship analyzer, a dynamic optimization recommendation engine, and a trend monitoring and early warning platform; the data acquisition and input module acquires microseismic energy, theoretical blasting energy, explosive quantity, and adjustment coefficients; Performance index calculation engine calculates the blasting performance index; intelligent evaluation and grading module automatically evaluates the blasting performance index. A parameter response relationship analyzer establishes a response model between the blasting effectiveness index and blasting parameters; a dynamic optimization recommendation engine generates blasting parameter optimization schemes based on evaluation results and response models. The trend monitoring and early warning platform tracks changes in the blasting effectiveness index sequence and triggers early warnings.

2. A method for quantitative evaluation and dynamic control of deep-hole pre-splitting blasting effect, characterized in that, The deep-hole pre-fracture blasting effect quantitative evaluation and dynamic control system according to claim 1 includes the following steps: Step 1: Construct a response model based on the nonlinear propagation law of blasting energy and define the blasting effectiveness index; Step 2: Establish a quantitative grading standard for blasting effectiveness based on the blasting efficiency index; Step 3: Conduct blasting in the target area and collect data to calculate the blasting effectiveness index; Step 4: Evaluate the blasting effectiveness index according to the grading standard and obtain the evaluation results; Step 5: Based on the evaluation results, dynamically feedback and optimize the blasting design parameters; Step 6: Establish a trend early warning mechanism and trigger an early warning for consecutive blasting events in the same area.

3. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 2, characterized in that, Step 1 specifically involves: constructing a response model based on the nonlinear propagation law of blasting energy using a parameter response relationship analyzer, and defining the blasting effectiveness index as S. e .

4. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 3, characterized in that, Step 2 specifically involves: establishing a quantitative grading standard for blasting effectiveness index through an intelligent evaluation and grading module. The grading standard is: S e When S < 0, the corresponding effect is poor; 0 ≤ S e When S < 0.3, the corresponding effect is moderate; when S ≤ 0.3 e When S < 0.7, the corresponding effect is good; e When the value is ≥ 0.7, the corresponding effect is excellent.

5. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 4, characterized in that, Step 3 specifically involves: using the data acquisition and input module to perform blasting in the target area and collect data; and using the efficiency index calculation engine to calculate the blasting efficiency index S for each blast. e ; The collected data includes measured microseismic energy, theoretical blasting energy, explosive charge, and adjustment coefficients.

6. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 5, characterized in that, The blasting effectiveness index is calculated according to formulas (1) and (2); WITH e =[K r lg(E s )-lg(E p )] / lg(P e ) (1); AND p = And Pi ×P e ×K s (2); Among them, S e E represents the blasting effectiveness index for each blast. s E represents the measured microseismic energy. p P is the theoretical blast energy. e K represents the amount of explosives. r The adjustment factor ranges from 1.0 to 1.3; E Pi K represents the theoretical energy of a unit of explosive. s The energy conversion coefficient has a value ranging from 0.1 to 0.

3.

7. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 6, characterized in that, Step 4 specifically involves: evaluating the blasting effectiveness index of the blasting according to the grading standard through the intelligent evaluation and grading module, and obtaining the evaluation result.

8. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 7, characterized in that, Step 5 specifically involves: Step 5.1: By dynamically optimizing the recommendation engine, based on the evaluation results, establish the blasting effectiveness index S as shown in equation (3). e A model of the logarithmic response relationship between the value and the blasting parameters; S e = f( lg(L), lg(P e ), C, F ) (3); Where L is the hole spacing, C is the charge structure score, and F is the free surface condition score; Step 5.2: Based on the logarithmic response relationship model, obtain the partial derivative analysis results of the logarithmic response relationship model. S e / lg(L); Step 5.3: Analyze the results based on partial derivatives. S e / lg(L) generates parameter optimization suggestions; Specifically: when the partial derivative analysis results S e / When lg(L) < 0, the generated optimization suggestions include reducing the hole spacing L; when the partial derivative analysis results S e / lg(P e When )>0 and the marginal effect is significant, the generated optimization suggestions include appropriately increasing the amount of drug per orifice P. e .

9. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 8, characterized in that, Step 6 specifically involves: calculating the moving average and standard deviation of the blasting effectiveness index for consecutive blasting events in the same area, establishing a trend early warning mechanism and triggering an early warning through a trend monitoring and early warning platform; The establishment of the trend early warning mechanism and the triggering of the early warning are specifically as follows: when the moving average decreases by more than 2 standard deviations, a yellow warning is triggered; when the blasting efficiency index S appears... e A red alert is triggered when the value is less than 0.

10. The method for quantitative evaluation and dynamic control of deep hole pre-fracture blasting effect according to claim 9, characterized in that, Step 3 further includes calculating the relative efficiency index S' according to equation (4). e This is used for longitudinal trend comparison of blasting effects within the area; S' e = S e / S e0 (4); Among them, S' e S is the relative efficiency index. e0 S is the baseline performance index determined in the target area. e This refers to the blasting effectiveness index.

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