Deep tunnel blasting control instruction generation method and system based on cross-scale data

Abstract

The invention relates to the technical field of data processing, discloses a deep tunnel blasting control instruction generation method and system based on cross-scale data, and aims to solve the problems of uncontrollable rock mass damage, high support failure risk and parameter optimization lag in the existing method. According to the scheme, the method mainly comprises the steps that rock mass fracture density, blasting vibration velocity, supporting structure strain and blasting thermal disturbance temperature rise are monitored in real time, and a rock mass fracture vibration frequency band, a supporting structure resonance frequency band and power spectrum density of signals corresponding to the supporting structure strain are determined; calculating a rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, a stripping index and support time-varying stiffness; determining a blasting parameter set based on a genetic algorithm, and predicting a predicted rock mass damage index at a future moment; the blasting parameters in the blasting parameter set are corrected, corresponding engineering measures are determined, and a blasting control instruction is generated. According to the invention, the accuracy of the control instruction, the safety of tunnel construction and the timeliness of control response are improved.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for generating blasting control commands for deep-buried tunnels based on cross-scale data. Background Technology

[0002] With the large-scale construction of deep-buried tunnel projects in the western plateau region, the full-face drill-and-blast method has been widely adopted due to its strong geological adaptability. However, the problem of dynamic disaster chains under high ground stress environments is becoming increasingly prominent: excavation unloading leads to tangential stress concentration in the surrounding rock, which induces the propagation of micro-cracks under dynamic blasting disturbance. When the disturbance energy exceeds the rock mass energy storage threshold, it will trigger a time-delayed rockburst, resulting in serious consequences such as excessive over-excavation rate and support failure rate.

[0003] In deep-buried tunnel blasting construction, blasting energy control is directly related to the stability of the surrounding rock and the safety of the support structure. Traditional blasting parameter design mainly relies on geological exploration experience and static mechanical models, which has the following key defects: First, the problem of fragmented multi-source data: Existing technologies usually independently monitor single parameters such as blasting vibration velocity, fracture development, or support strain, lacking collaborative analysis of the cross-scale dynamic response of the rock mass-support system. For example, key factors such as the weakening effect of blasting thermal disturbance on rock mass strength, rock mass damage induced by seepage field changes, and the coupling mechanism between the time-varying stiffness of the support structure and blasting vibration are not systematically integrated, leading to one-sided parameter decisions. Second, the defect of static damage assessment: Rock mass damage is mostly determined based on instantaneous vibration velocity thresholds or empirical formulas, without considering the dynamic interaction between blasting cumulative effects and environmental disturbances; existing damage models ignore the correction of rock mass failure strain by temperature, causing the damage index calculation to deviate from actual working conditions, exacerbating the risk of support failure. Third, there is a lack of coordinated control between support and rock mass: the stiffness of the support structure is often considered a constant value, and no quantitative correlation has been established between it and rock mass damage; at the same time, the energy distribution of the resonance frequency band of the support structure and the fracture frequency band of the rock mass is not used for dynamic early warning, which can easily lead to structural resonance failure. Fourth, parameter optimization and blasting control are lagging: the adjustment of blasting parameters relies on manual trial and error, and there is a lack of intelligent optimization mechanism based on real-time data; existing methods cannot predict damage trends in advance through time-series data, resulting in slow response of engineering measures and difficulty in curbing the risk of crack propagation or stripping.

[0004] In summary, existing technologies suffer from problems such as uncontrollable rock mass damage, high risk of support failure, and lagging parameter optimization during blasting control due to fragmented data, static models, reliance on experience, and lack of prediction. Therefore, there is an urgent need to establish a blasting control command generation method that integrates multi-source, multi-scale data, dynamically couples rock mass-support response, and integrates intelligent optimization and risk prediction to achieve safe and efficient construction of deep-buried tunnels. Summary of the Invention

[0005] This invention aims to address the problems of uncontrollable rock mass damage, high risk of support failure, and lagging parameter optimization in existing blasting control methods. It proposes a method and system for generating blasting control commands for deep-buried tunnels based on cross-scale data.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems is as follows:

[0007] In a first aspect, the present invention provides a method for generating blasting control commands for deep-buried tunnels based on cross-scale data, the method comprising:

[0008] Real-time monitoring of rock mass fracture density, blasting vibration velocity, support structure strain, and blasting thermal disturbance temperature rise; and determination of the rock mass fracture vibration frequency band, support structure resonance frequency band, and power spectral density of the signal corresponding to support structure strain.

[0009] Based on the blasting vibration velocity, the rock mass plastic strain is inverted; based on the rock mass plastic strain and the temperature rise caused by blasting thermal disturbance, the rock mass damage index is calculated; based on the rock mass fracture vibration frequency band, the rock mass fracture vibration energy is calculated; based on the support structure resonance frequency band, the support structure vibration energy is calculated; based on the power spectral density, the stripping index is calculated; and the support time-varying stiffness is calculated.

[0010] With the optimization objective of minimizing the ratio of rock mass damage index to support time-varying stiffness, the optimal blasting parameter set is searched in the blasting parameter space based on a genetic algorithm. At the same time, the rock mass damage index is predicted based on the time series data of rock mass damage index, support time-varying stiffness and blasting vibration velocity and LSTM to predict future moments.

[0011] Based on the rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, the blasting parameters in the blasting parameter set are corrected and corresponding engineering measures are determined. Blasting control commands are generated based on the corrected blasting parameter set and the determined engineering measures.

[0012] Furthermore, the method for detecting the rock mass fracture density includes:

[0013] Real-time monitoring of microseismic energy, and calculation of rock mass fracture density based on the microseismic energy, using the following formula:

[0014] ;

[0015] in, Indicates the density of rock fractures. This represents the energy of microseismic events. It represents the natural logarithm.

[0016] Furthermore, the inversion formula for the plastic strain of the rock mass is as follows:

[0017] ;

[0018] The method for calculating the rock mass damage index includes: determining the rock mass failure strain based on the uniaxial compressive strength of the rock and the dynamic load constitutive model; correcting the rock mass failure strain based on the temperature rise caused by blasting thermal disturbance; and calculating the rock mass damage index based on the rock mass plastic strain, the temperature rise caused by blasting thermal disturbance, and the corrected rock mass failure strain.

[0019] The corrected formula for the rock mass failure strain is as follows:

[0020] ;

[0021] The formula for calculating the rock mass damage index is as follows:

[0022] ;

[0023] ;

[0024] ;

[0025] in, Indicates the rock mass damage index. Indicates the blasting vibration velocity. Indicates the plastic strain of the rock mass. Indicates the rock mass failure strain. This represents the corrected rock mass failure strain. Indicates the temperature softening factor. This indicates the temperature rise due to the thermal disturbance during the explosion. Indicates the seepage weakening factor. This represents the current permeability coefficient. Represents the initial permeability coefficient. Indicates the lithological scale coefficient. Indicates the lithological morphology coefficient. Represents the natural logarithm. This represents an exponential function.

[0026] Furthermore, the formula for calculating the time-varying stiffness of the support is as follows:

[0027] ;

[0028] in, express Constantly maintain time-varying stiffness. Indicates the initial stiffness of the support. Indicates the strength factor of concrete at different ages. Indicates the age of support, This represents the time constant for stiffness growth. It represents the natural logarithm.

[0029] Furthermore, the logic for generating the blasting parameter set is as follows:

[0030] ;

[0031] in, Represents the set of blasting parameters. Indicates the amount of explosives used in blasting. Indicates the distance between blasting holes. Indicates the time interval between blasts. Indicates the rock mass damage index. express Constantly maintain time-varying stiffness. This means finding a set of explosive parameters to minimize .

[0032] Furthermore, the rock mass vibration frequency band is (100Hz, ∞), and the support structure resonance frequency band is (0, 50Hz).

[0033] The formula for calculating the vibration energy of rock mass fracture is as follows:

[0034] ;

[0035] The formula for calculating the vibration energy of the support structure is as follows:

[0036] ;

[0037] in, This represents the vibrational energy of rock mass fracture. This represents the power spectral density of rock mass fracture vibration. This represents the vibration energy of the support structure. This represents the vibration power spectral density of the support structure. Indicates frequency.

[0038] Furthermore, when calculating the stripping index, the stripping characteristic frequency band is (8Hz, 12Hz).

[0039] The formula for calculating the stripping index is as follows:

[0040] ;

[0041] in, Indicates the stripping index. This represents the power spectral density of the signal corresponding to the strain of the support structure.

[0042] Furthermore, the blasting parameters in the blasting parameter set are corrected, including:

[0043] If the rock mass damage index or the predicted rock mass damage index is greater than the first threshold, the amount of blasting explosive is reduced according to the first preset ratio, and the blasting hole spacing is reduced according to the second preset ratio.

[0044] If the vibration energy of rock mass fracture is greater than the second threshold, the amount of blasting explosive is reduced according to the first preset ratio, the blasting hole spacing is reduced according to the second preset ratio, and the blasting time interval is increased according to the preset duration.

[0045] If the rock mass fracture density is greater than the third threshold, the blasting hole spacing will be reduced according to the third preset ratio.

[0046] Furthermore, the corresponding engineering measures are determined, including:

[0047] If the predicted rock mass damage index is greater than the first threshold or the rock mass fracture vibration energy is greater than the second threshold, the corresponding engineering measure is to initiate nano-silicon injection slurry.

[0048] If the rock mass fracture density is greater than the third threshold, the corresponding engineering measure is to shorten the advance by single-cycle advance.

[0049] If the vibration energy of the support structure is greater than the fourth threshold or the stripping index is greater than the fifth threshold, the corresponding engineering measure is to spray a concrete compensation layer.

[0050] If the temperature rise due to blasting thermal disturbance exceeds the sixth threshold, the corresponding engineering measure is to add a low-temperature regulator to the grouting liquid.

[0051] Secondly, the present invention provides a deep-buried tunnel blasting control command generation system based on cross-scale data, used to implement the deep-buried tunnel blasting control command generation method based on cross-scale data as described in the first aspect, the system comprising:

[0052] The multi-scale data acquisition unit is used to monitor rock fracture density, blasting vibration velocity, support structure strain and blasting thermal disturbance temperature rise in real time, and to determine the rock fracture vibration frequency band, support structure resonance frequency band and power spectral density of the signal corresponding to support structure strain.

[0053] The feedback analysis unit is used to invert the rock mass plastic strain based on the blasting vibration velocity, calculate the rock mass damage index based on the rock mass plastic strain and the temperature rise caused by blasting thermal disturbance, calculate the rock mass fracture vibration energy based on the rock mass fracture vibration frequency band, calculate the support structure vibration energy based on the support structure resonance frequency band, calculate the stripping index based on the power spectral density, and calculate the support time-varying stiffness.

[0054] The decision application unit is used to minimize the ratio of rock mass damage index to support time-varying stiffness as the optimization objective. Based on a genetic algorithm, it searches for the optimal blasting parameter set in the blasting parameter space. Simultaneously, based on the time-series data of rock mass damage index, support time-varying stiffness, and blasting vibration velocity, it predicts the rock mass damage index for future times using LSTM. Based on the rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, it corrects the blasting parameters in the blasting parameter set and determines corresponding engineering measures. Based on the corrected blasting parameter set and the determined engineering measures, it generates blasting control commands.

[0055] The beneficial effects of this invention are as follows: The method and system for generating blasting control commands for deep-buried tunnels based on multi-scale data provided by this invention integrate multi-scale data such as rock mass fracture density, blasting vibration velocity, support structure strain, and blasting thermal disturbance temperature rise, covering internal rock mass damage, dynamic response, and thermal effects. This ensures that blasting control decisions are based on multi-source information, reducing bias. By calculating indicators such as damage index and vibration energy, quantitative assessments are provided to help adjust parameters in a timely manner, achieving dynamic control, avoiding cumulative damage, and improving the safety of tunnel construction. Minimizing the ratio of rock mass damage index to time-varying support stiffness is considered optimal. This invention optimizes the blasting target and uses a genetic algorithm to search for the optimal set of blasting parameters, balancing rock mass damage and the stability of the support structure. This ensures that blasting is carried out within a safe range, preventing support failure or excessive rock mass damage. Furthermore, the genetic algorithm can efficiently find the globally optimal or near-optimal solution, improving blasting efficiency and economy. Based on an LSTM model, future rock mass damage indices are predicted, enabling preventative control and allowing for advance adjustment of blasting parameters to avoid potential risks and enhance system robustness. Multiple indicators are used to correct blasting parameters and determine engineering measures, making blasting control more adaptable to actual conditions, reducing unexpected events, and ensuring construction continuity and quality. Moreover, this invention starts with real-time monitoring, proceeds through data calculation, optimization search, and prediction correction, and finally generates control commands. Each link is tightly coupled: monitoring data drives indicator calculation, indicator calculation supports the optimization target, optimization results are input into the prediction model, prediction output guides parameter correction, and the corrected commands influence the next round of monitoring, thus forming a control closed loop. Simultaneously, the comprehensiveness of cross-scale data, the global optimization capability of the genetic algorithm, and the temporal prediction advantages of LSTM ensure rapid and accurate response, avoiding the data lag problem in traditional methods. Attached Figure Description

[0056] Figure 1 A flowchart illustrating the method for generating blasting control commands for deep-buried tunnels based on cross-scale data, provided in this embodiment;

[0057] Figure 2 This is a schematic diagram of the structure of a deep-buried tunnel blasting control command generation system based on cross-scale data, provided as an example. Detailed Implementation

[0058] Because the current blasting parameter design scheme mainly relies on geological exploration experience and static mechanical models, this scheme has problems such as uncontrollable rock mass damage, high risk of support failure, and lag in parameter optimization during blasting control.

[0059] Based on this, the technical solution of this invention is proposed. In this invention, firstly, multi-dimensional data such as rock mass fracture density, blasting vibration velocity, support structure strain, and blasting thermal disturbance temperature rise are monitored in real time, and the rock mass fracture vibration frequency band, support structure resonance frequency band, and power spectral density are extracted from these data. These data cover internal rock mass damage, dynamic response, and thermal effects, forming a cross-scale data foundation. Multi-source fusion avoids the limitations of a single data source. Simultaneously, by calculating the rock mass fracture vibration energy, support structure vibration energy, and stripping index, the raw data is transformed into quantitative engineering indicators, providing a unified and comparable input for subsequent decision-making. Then, by calculating the rock mass damage index and the time-varying stiffness of the support, the rock mass damage index quantifies the cumulative damage to the rock mass caused by blasting, and the time-varying stiffness of the support reflects the dynamic bearing capacity of the support structure. This invention takes minimizing the ratio of the two as the optimization objective, which is essentially to balance minimizing rock mass damage and maximizing support stability. Minimizing the ratio means seeking the optimal toughness state of the rock mass-support system under given blasting conditions, thereby avoiding the risk of collapse caused by excessive rock mass damage and resonance failure caused by insufficient support stiffness. At the same time, a genetic algorithm is used to solve for the optimal blasting parameter set, which improves the solution efficiency. Then, using time-series data of rock mass damage index, support time-varying stiffness, and blasting vibration velocity, the future rock mass damage index is predicted through an LSTM (Long Short-Term Memory) model. By predicting future states, blasting parameters can be proactively corrected and engineering measures can be triggered, thereby avoiding potential risks and enhancing system robustness. Finally, based on multiple indicators such as rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, blasting parameters are corrected and engineering measures are determined, avoiding the one-sidedness of adjusting a single parameter and ensuring the scientific nature and operability of control commands.

[0060] The technical solutions in this embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0061] Figure 1 A flowchart illustrating a method for generating blasting control commands for deep-buried tunnels based on cross-scale data is shown. Please refer to [link / reference]. Figure 1 The method includes the following steps:

[0062] Step 1: Monitor rock fracture density, blasting vibration velocity, support structure strain, and blasting thermal disturbance temperature rise in real time, and determine the rock fracture vibration frequency band, support structure resonance frequency band, and the power spectral density of the signal corresponding to the support structure strain.

[0063] In this embodiment, the excavation face can be scanned directly using a 3D laser scanner after each blasting cycle, and the rock mass fracture density can be statistically analyzed using Hough transform. Alternatively, the rock mass fracture density can be statistically analyzed in advance using a 3D laser scanner and Hough transform, while simultaneously using a microseismic sensor array to monitor microseismic energy, i.e., the energy peak within a preset time after blasting, to establish a relationship between blasting energy and rock mass fracture density. In practical applications, the microseismic sensor array is used to monitor microseismic energy in real time, and then the rock mass fracture density is calculated based on the microseismic energy. The calculation formula is as follows:

[0064] ;

[0065] in, This indicates the density of rock mass fractures (unit: fractures / cm²). This represents the energy of a microseismic event (unit: J). The value represents the natural logarithm, 0.18 represents the energy-density conversion factor (unit: fragments / (cm³·lnJ)), and 2.4 represents the primary fracture density (unit: fragments / cm²).

[0066] In this embodiment, a microseismic sensor array is used to monitor the blasting vibration velocity in real time. The microseismic sensor array is arranged along the tunnel axis within the range behind the tunnel face. Infrared thermal imager array is used to monitor the temperature rise of blasting thermal disturbance in real time.

[0067] In this embodiment, FBG strain sensors arranged in a rhomboid topology array are used to monitor the strain of the support structure. The vertex spacing of the FBG strain sensors is less than or equal to 10 cm. The FBG strain sensors acquire the raw wavelength signal, and the strain of the support structure is converted according to the FBG wavelength change corresponding to the raw wavelength signal, as shown in the following formula:

[0068] ;

[0069] ;

[0070] in, Indicates the strain of the support structure (unit: ), Indicates the original strain (unit: ), Indicates calibration coefficient (unit: / pm), This indicates the change in FBG wavelength (unit: pm). This indicates the incident angle of the grating (unit: degrees).

[0071] In this embodiment, the rock mass fracture vibration signal is a high-frequency fracture signal, and its corresponding rock mass fracture vibration frequency band is (100Hz, ∞), that is, greater than 100Hz. The support structure resonance signal is a low-frequency structural vibration, and its corresponding support structure resonance frequency band is (0, 50Hz), that is, less than 50Hz.

[0072] Step 2: Invert the rock mass plastic strain based on the blasting vibration velocity; calculate the rock mass damage index based on the rock mass plastic strain and the temperature rise caused by blasting thermal disturbance; calculate the rock mass fracture vibration energy based on the rock mass fracture vibration frequency band; calculate the support structure vibration energy based on the support structure resonance frequency band; calculate the stripping index based on the power spectral density; and calculate the time-varying stiffness of the support.

[0073] In this embodiment, the inversion formula for the plastic strain of the rock mass is as follows:

[0074] ;

[0075] in, Represents the plastic strain of the rock mass (dimensionless). This represents the blasting vibration velocity, and 0.024 represents the velocity-strain conversion factor (unit: ...). ), 0.18 represents the background plastic strain (dimensionless).

[0076] The method for calculating the rock mass damage index includes: determining the rock mass failure strain based on the uniaxial compressive strength of the rock and the dynamic load constitutive model; correcting the rock mass failure strain based on the temperature rise caused by blasting thermal disturbance; and calculating the rock mass damage index based on the rock mass plastic strain, the temperature rise caused by blasting thermal disturbance, and the corrected rock mass failure strain.

[0077] The corrected formula for the rock mass failure strain is as follows:

[0078] ;

[0079] in, Represents the rock mass failure strain (dimensionless). This represents the corrected rock mass failure strain (dimensionless). This indicates the temperature rise due to the thermal disturbance during explosion (unit: °C). This represents the temperature rise weakening coefficient (unit: 1 / ℃). This indicates the reference temperature (unit: °C).

[0080] The formula for calculating the rock mass damage index is as follows:

[0081] ;

[0082] ;

[0083] ;

[0084] in, The rock mass damage index (dimensionless) ranges from [0, 1]. Indicates no loss. Indicates complete destruction. This represents the temperature softening factor (dimensionless). This represents the seepage weakening factor (dimensionless). This represents the current permeability coefficient (dimensionless). This represents the initial permeability coefficient (dimensionless). Represents the lithological scale coefficient (dimensionless). Represents the lithological morphology coefficient (dimensionless). and Calibration is achieved through indoor dynamic load testing, such as the Hopkinson bar test, for granite. Take 1.2, Take 2.5, Represents the natural logarithm. The exponential function is represented by 0.02, which represents the temperature softening index (unit: 1 / ℃), and 0.12 represents the seepage weakening index (dimensionless).

[0085] In the above formula, the rock mass damage index is corrected for temperature and permeability to obtain damage that is more consistent with the actual thermo-mechanical coupling state, which significantly improves the damage assessment accuracy under high geothermal and high permeability pressure conditions and avoids over-excavation and under-excavation accidents.

[0086] In this embodiment, based on the rock mass vibration frequency band of (100Hz, ∞) and the support structure resonance frequency band of (0, 50Hz), the calculation formula for the rock mass fracture vibration energy is as follows:

[0087] ;

[0088] The formula for calculating the vibration energy of the support structure is as follows:

[0089] ;

[0090] in, This represents the vibrational energy of rock mass fracture (unit: J). This represents the power spectral density of rock mass fracture vibration (unit: J / Hz). This represents the vibration energy of the support structure (unit: J). This represents the vibration power spectral density of the support structure (unit: J / Hz). Frequency (unit: Hz).

[0091] By separating the rock mass fracture and support resonance signals using the above formula, the problem of misjudgment due to overlapping can be solved, and the energy-frequency synergy between the rock mass and the support can be achieved.

[0092] In this embodiment, the stripping characteristic frequency band is (8Hz, 12Hz) when calculating the stripping index, and the formula for calculating the stripping index is as follows:

[0093] ;

[0094] in, This represents the stripping index (dimensionless). This represents the power spectral density of the signal corresponding to the strain of the support structure (unit: με² / Hz).

[0095] In this embodiment, the formula for calculating the time-varying stiffness of the support is as follows:

[0096] ;

[0097] in, express Constant-time support time-varying stiffness (unit: GPa). The initial stiffness of the support (unit: GPa) is determined by the concrete grade or the cross-sectional parameters of the steel arch frame. This represents the concrete age-strength factor (dimensionless), with empirical values ​​ranging from 0.05 to 0.12. Indicates the duration of support (unit: days). This represents the stiffness growth time constant (unit: days), with a default value of 1 day. It represents the natural logarithm.

[0098] Step 3: With the optimization objective of minimizing the ratio of rock mass damage index to support time-varying stiffness, the optimal blasting parameter set is searched in the blasting parameter space based on the genetic algorithm. At the same time, the rock mass damage index is predicted for future moments based on the time series data of rock mass damage index, support time-varying stiffness and blasting vibration velocity and LSTM.

[0099] In this embodiment, the logic for generating the blasting parameter set is as follows:

[0100] ;

[0101] in, Represents the set of blasting parameters. This indicates the amount of explosives used in blasting (unit: kg). Indicates the distance between blasting holes (unit: m). Indicates the time interval between blasts (unit: ms). Indicates the rock mass damage index. express Constantly maintain time-varying stiffness. This means finding a set of explosive parameters to minimize .

[0102] Specifically, this embodiment uses a genetic algorithm to search for the optimal solution in the blasting parameter space to minimize the damage-stiffness ratio. Simultaneously, historical window data of rock mass damage index, support time-varying stiffness, and blasting vibration velocity are input into a pre-trained LSTM to obtain a predicted rock mass damage index for future moments (e.g., 200ms). By predicting future states, the scheme can proactively adjust parameters. The predicted rock mass damage index is combined with real-time data (such as rock mass fracture density and stripping index) to correct the blasting parameter set. The comparison between predicted values ​​and real-time data provides a basis for decision-making, ensuring that the corrected parameter set can promptly mitigate potential risks, forming a closed loop of monitoring-prediction-correction. This achieves preventative and adaptive blasting control, enhancing system robustness.

[0103] Step 4: Based on the rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, correct the blasting parameters in the blasting parameter set and determine the corresponding engineering measures. Generate blasting control instructions based on the corrected blasting parameter set and the determined engineering measures.

[0104] In this embodiment, the blasting parameters in the blasting parameter set are corrected, including:

[0105] If the rock mass damage index or the predicted rock mass damage index is greater than the first threshold, the amount of blasting explosive is reduced according to the first preset ratio, and the blasting hole spacing is reduced according to the second preset ratio; if the rock mass fracture vibration energy is greater than the second threshold, the amount of blasting explosive is reduced according to the first preset ratio, the blasting hole spacing is reduced according to the second preset ratio, and the blasting time interval is increased according to the preset duration; if the rock mass fracture density is greater than the third threshold, the blasting hole spacing is reduced according to the third preset ratio.

[0106] In practical applications, when the rock mass damage index or predicted rock mass damage index is greater than 0.7, or the rock mass fracture vibration energy is greater than... When J indicates that the predicted rock mass damage value is too high or the high-frequency vibration energy of the rock mass exceeds the limit, the amount of blasting explosive is reduced by 40% to reduce the total blasting energy, and the spacing between blasting holes is increased by 20% to distribute the blasting energy evenly; when the rock mass fracture vibration energy is greater than Furthermore, the blasting time interval is increased by 15ms to avoid energy accumulation. When the rock mass fracture density exceeds 5 fractures / cm², it indicates excessive fracture density. In this case, the blasting hole spacing is further increased by 15% to avoid the risks caused by fracture penetration. By optimizing blasting parameters through the above process, support costs and accident rates can be significantly reduced while ensuring construction safety.

[0107] In this embodiment, the corresponding engineering measures are determined, including:

[0108] If the predicted rock mass damage index is greater than the first threshold or the rock mass fracture vibration energy is greater than the second threshold, the corresponding engineering measure is to initiate nano-silica grout injection; if the rock mass fracture density is greater than the third threshold, the corresponding engineering measure is to shorten the single-cycle advance; if the support structure vibration energy is greater than the fourth threshold or the stripping index is greater than the fifth threshold, the corresponding engineering measure is to spray a concrete compensation layer; if the blasting thermal disturbance temperature rise is greater than the sixth threshold, the corresponding engineering measure is to add a low-temperature regulator to the grouting liquid.

[0109] In practical applications, when the predicted rock mass damage index is greater than 0.7, or the rock mass fracture vibration energy is greater than... When the value of J indicates that the predicted value of rock mass damage is too high or the high-frequency vibration energy of the rock mass exceeds the limit, nano-silica grouting is also initiated to pre-fill the cracks to inhibit damage propagation and block the vibration energy transmission path. When the rock mass crack density is greater than 5 cracks / cm², it indicates that the crack density is too high. At this time, the single-cycle advance is shortened to 1.5m to reduce the blasting exposure surface and prevent crack penetration. When the vibration energy of the support structure is greater than 200J or the peeling index is greater than 0.15, concrete compensation layer spraying is performed to increase the moment of inertia of the section, improve the bending stiffness, fill the void area and restore the contact stress. When the temperature rise of the blasting thermal disturbance is greater than 15℃, the grouting liquid is mixed with a low-temperature regulator to suppress the temperature stress caused by the heat of cement hydration.

[0110] The above process, based on multiple indicators such as rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, corrects blasting parameters and determines engineering measures. These indicators are interrelated: fracture density and damage index reflect the current state of the rock mass; vibration energy (fracture and support) indicates dynamic risk; the stripping index (based on power spectral density) warns of structural surface failure; and the predicted damage index introduces future conditions. When correcting blasting parameters, high vibration energy may require a reduction in blasting scale, while a high stripping index necessitates stronger support. Simultaneously, engineering measures address specific problems to ensure the corrected blasting parameter set is executable on-site. The blasting control commands generated based on the corrected blasting parameter set and determined engineering measures are not only precise and efficient but also proactively address the uncertainties of deeply buried tunnels, significantly reducing engineering risks and improving overall economic benefits.

[0111] In summary, the deep-buried tunnel blasting control command generation method based on multi-scale data provided in this embodiment integrates multi-scale data such as rock mass fracture density, blasting vibration velocity, support structure strain, and blasting thermal disturbance temperature rise. This covers internal rock mass damage, dynamic response, and thermal effects, ensuring that blasting control decisions are based on multi-source information and reducing bias. Quantitative assessments are provided by calculating indicators such as damage index and vibration energy, helping to adjust parameters in a timely manner, achieving dynamic control, avoiding cumulative damage, and improving the safety of tunnel construction. When calculating the rock mass damage index, temperature and permeability corrections are applied to obtain damage that more closely reflects the actual thermo-mechanical coupling state, significantly improving the damage assessment accuracy under high ground temperature and high permeability pressure conditions, and avoiding... Over- and under-excavation accidents were avoided; minimizing the ratio of rock mass damage index to support time-varying stiffness was used as the optimization objective, and a genetic algorithm was used to search for the optimal blasting parameter set, balancing rock mass damage and support structure stability, ensuring that blasting was carried out within a safe range, preventing support failure or excessive rock mass damage, and the genetic algorithm could efficiently find the global optimal solution or near-optimal solution, improving blasting efficiency and economy; based on the LSTM model to predict future rock mass damage index, preventive control was achieved, blasting parameters were adjusted in advance, potential risks were avoided, and the system robustness was enhanced; based on multiple indicators, blasting parameters were corrected and engineering measures were determined, making blasting control more adaptable to actual conditions, reducing unexpected events, and ensuring construction continuity and quality. Furthermore, this embodiment starts with real-time monitoring, proceeds through data calculation, optimization search, and prediction correction, and finally generates control commands. Each link is tightly coupled: monitoring data drives index calculation, index calculation supports optimization objectives, optimization results are input into the prediction model, prediction output guides parameter correction, and the corrected commands then influence the next round of monitoring, thus forming a control closed loop. At the same time, it utilizes the comprehensiveness of cross-scale data, the global optimization capability of genetic algorithms, and the time-series prediction advantages of LSTM to ensure fast and accurate response, avoiding the data lag problem in traditional methods.

[0112] Based on the above technical solution, this embodiment also proposes a deep-buried tunnel blasting control command generation system based on cross-scale data, used to implement the deep-buried tunnel blasting control command generation method based on cross-scale data described in the embodiment. The system includes:

[0113] The multi-scale data acquisition unit is used to monitor rock fracture density, blasting vibration velocity, support structure strain and blasting thermal disturbance temperature rise in real time, and to determine the rock fracture vibration frequency band, support structure resonance frequency band and power spectral density of the signal corresponding to support structure strain.

[0114] The feedback analysis unit is used to invert the rock mass plastic strain based on the blasting vibration velocity, calculate the rock mass damage index based on the rock mass plastic strain and the temperature rise caused by blasting thermal disturbance, calculate the rock mass fracture vibration energy based on the rock mass fracture vibration frequency band, calculate the support structure vibration energy based on the support structure resonance frequency band, calculate the stripping index based on the power spectral density, and calculate the support time-varying stiffness.

[0115] The decision application unit is used to minimize the ratio of rock mass damage index to support time-varying stiffness as the optimization objective. Based on a genetic algorithm, it searches for the optimal blasting parameter set in the blasting parameter space. Simultaneously, based on the time-series data of rock mass damage index, support time-varying stiffness, and blasting vibration velocity, it predicts the rock mass damage index for future times using LSTM. Based on the rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, it corrects the blasting parameters in the blasting parameter set and determines corresponding engineering measures. Based on the corrected blasting parameter set and the determined engineering measures, it generates blasting control commands.

[0116] It is understood that the deep-buried tunnel blasting control command generation system based on cross-scale data described in this embodiment is a system for implementing the deep-buried tunnel blasting control command generation method based on cross-scale data described in the embodiment. As the system disclosed in the embodiment corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant parts, please refer to the description of the method. It will not be repeated here.

Claims

1. A method for generating blasting control commands for deep-buried tunnels based on cross-scale data, characterized in that, The method includes: Real-time monitoring of rock mass fracture density, blasting vibration velocity, support structure strain, and blasting thermal disturbance temperature rise; and determination of the rock mass fracture vibration frequency band, support structure resonance frequency band, and power spectral density of the signal corresponding to support structure strain. Based on the blasting vibration velocity, the rock mass plastic strain is inverted; based on the rock mass plastic strain and the temperature rise caused by blasting thermal disturbance, the rock mass damage index is calculated; based on the rock mass fracture vibration frequency band, the rock mass fracture vibration energy is calculated; based on the support structure resonance frequency band, the support structure vibration energy is calculated; based on the power spectral density, the stripping index is calculated; and the support time-varying stiffness is calculated. With the optimization objective of minimizing the ratio of rock mass damage index to support time-varying stiffness, the optimal blasting parameter set is searched in the blasting parameter space based on a genetic algorithm. At the same time, the rock mass damage index is predicted based on the time series data of rock mass damage index, support time-varying stiffness and blasting vibration velocity and LSTM to predict future moments. Based on the rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, the blasting parameters in the blasting parameter set are corrected and corresponding engineering measures are determined. Blasting control commands are generated based on the corrected blasting parameter set and the determined engineering measures.

2. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, The method for detecting the density of rock mass fractures includes: Real-time monitoring of microseismic energy, and calculation of rock mass fracture density based on the microseismic energy, using the following formula: ； in, Indicates the density of rock fractures. This represents the energy of microseismic events. It represents the natural logarithm.

3. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, The inversion formula for the plastic strain of the rock mass is as follows: ； The method for calculating the rock mass damage index includes: determining the rock mass failure strain based on the uniaxial compressive strength of the rock and the dynamic load constitutive model; correcting the rock mass failure strain based on the temperature rise caused by blasting thermal disturbance; and calculating the rock mass damage index based on the rock mass plastic strain, the temperature rise caused by blasting thermal disturbance, and the corrected rock mass failure strain. The corrected formula for the rock mass failure strain is as follows: ； The formula for calculating the rock mass damage index is as follows: ； ； ； in, Indicates the rock mass damage index. Indicates the blasting vibration velocity. Indicates the plastic strain of the rock mass. Indicates the rock mass failure strain. This represents the corrected rock mass failure strain. Indicates the temperature softening factor. This indicates the temperature rise due to the thermal disturbance during the explosion. Indicates the seepage weakening factor. This represents the current permeability coefficient. Represents the initial permeability coefficient. Indicates the lithological scale coefficient. Indicates the lithological morphology coefficient. Represents the natural logarithm. This represents an exponential function.

4. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, The formula for calculating the time-varying stiffness of the support is as follows: ； in, express Constantly maintain time-varying stiffness. Indicates the initial stiffness of the support. Indicates the strength factor of concrete at different ages. Indicates the age of support, This represents the time constant for stiffness growth. It represents the natural logarithm.

5. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, The logic for generating the blasting parameter set is as follows: ； in, Represents the set of blasting parameters. Indicates the amount of explosives used in blasting. Indicates the distance between blasting holes. Indicates the time interval between blasts. Indicates the rock mass damage index. express Constantly maintain time-varying stiffness. This means finding a set of explosive parameters to minimize .

6. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, The rock mass vibration frequency band is (100Hz, ∞), and the support structure resonance frequency band is (0, 50Hz). The formula for calculating the vibration energy of rock mass fracture is as follows: ； The formula for calculating the vibration energy of the support structure is as follows: ； in, This represents the vibrational energy of rock mass fracture. This represents the power spectral density of rock mass fracture vibration. This represents the vibration energy of the support structure. This represents the vibration power spectral density of the support structure. Indicates frequency.

7. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, When calculating the stripping index, the stripping characteristic frequency band is (8Hz, 12Hz). The formula for calculating the stripping index is as follows: ； in, Indicates the stripping index. This represents the power spectral density of the signal corresponding to the strain of the support structure.

8. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, The blasting parameters in the blasting parameter set are corrected, including: If the rock mass damage index or the predicted rock mass damage index is greater than the first threshold, the amount of blasting explosive is reduced according to the first preset ratio, and the blasting hole spacing is reduced according to the second preset ratio. If the vibration energy of rock mass fracture is greater than the second threshold, the amount of blasting explosive is reduced according to the first preset ratio, the blasting hole spacing is reduced according to the second preset ratio, and the blasting time interval is increased according to the preset duration. If the rock mass fracture density is greater than the third threshold, the blasting hole spacing will be reduced according to the third preset ratio.

9. The method for generating blasting control commands for deep-buried tunnels based on cross-scale data according to claim 1, characterized in that, Determine the corresponding engineering measures, including: If the predicted rock mass damage index is greater than the first threshold or the rock mass fracture vibration energy is greater than the second threshold, the corresponding engineering measure is to initiate nano-silicon injection slurry. If the rock mass fracture density is greater than the third threshold, the corresponding engineering measure is to shorten the advance by single-cycle advance. If the vibration energy of the support structure is greater than the fourth threshold or the stripping index is greater than the fifth threshold, the corresponding engineering measure is to spray a concrete compensation layer. If the temperature rise due to blasting thermal disturbance exceeds the sixth threshold, the corresponding engineering measure is to add a low-temperature regulator to the grouting liquid.

10. A deep-buried tunnel blasting control command generation system based on cross-scale data, characterized in that, The system is used to implement the method for generating blasting control commands for deep-buried tunnels based on cross-scale data as described in any one of claims 1 to 9, the system comprising: The multi-scale data acquisition unit is used to monitor rock fracture density, blasting vibration velocity, support structure strain and blasting thermal disturbance temperature rise in real time, and to determine the rock fracture vibration frequency band, support structure resonance frequency band and power spectral density of the signal corresponding to support structure strain. The feedback analysis unit is used to invert the rock mass plastic strain based on the blasting vibration velocity, calculate the rock mass damage index based on the rock mass plastic strain and the temperature rise caused by blasting thermal disturbance, calculate the rock mass fracture vibration energy based on the rock mass fracture vibration frequency band, calculate the support structure vibration energy based on the support structure resonance frequency band, calculate the stripping index based on the power spectral density, and calculate the support time-varying stiffness. The decision application unit is used to minimize the ratio of rock mass damage index to support time-varying stiffness as the optimization objective. Based on a genetic algorithm, it searches for the optimal blasting parameter set in the blasting parameter space. Simultaneously, based on the time-series data of rock mass damage index, support time-varying stiffness, and blasting vibration velocity, it predicts the rock mass damage index for future moments using LSTM. Based on the rock mass fracture density, rock mass damage index, rock mass fracture vibration energy, support structure vibration energy, stripping index, and predicted rock mass damage index, it corrects the blasting parameters in the blasting parameter set and determines corresponding engineering measures. Based on the corrected blasting parameter set and the determined engineering measures, it generates blasting control commands.

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