Roof-cutting pressure-relief deep-hole presplitting blasting method for gob-side entry retaining of composite roof

Abstract

The invention belongs to the technical field of mining engineering, and particularly relates to a composite roof gob-side entry retaining roof cutting pressure relief deep hole presplitting blasting method which comprises the steps that geological data are obtained, a discrete element method is combined, a composite roof-roadway space three-dimensional mechanical model is constructed, and the roof fracture and collapse process under different blasting parameters is simulated; a whale optimization algorithm and a BP neural network are fused on the basis of roof fracture and collapse processes under different blasting parameters, and a target blasting parameter combination is dynamically output; the method comprises the following steps: monitoring roof microcrack propagation and stress change data in real time, adjusting blast hole arrangement and a charging structure in combination with a target blasting parameter combination, automatically installing a blasting material and an axial air spacer by adopting an intelligent charging robot based on the adjusted blast hole arrangement and charging structure, and accurately controlling blasting through a high-precision electronic detonator. And an ordered pressure relief process is formed according to the detonation time difference. Therefore, the problems that in the prior art, the parameter adjusting range is narrow, blasting stress distribution is uneven, and construction stability is poor are solved.

Description

Technical Field

[0001] The invention belongs to the technical field of mining engineering, and particularly relates to a method for roof cutting and pressure relief deep-hole pre-splitting blasting for gob-side entry retaining under composite roof conditions. Background Art

[0002] With the development of lignite coal mining towards deeper and more complex conditions, the gob-side entry retaining technology under composite roof conditions faces multiple challenges such as high ground stress, strong mining influence, and complex rock strata structures. There is an urgent need in the market for a roof cutting and pressure relief technology with self-adaptive blasting parameters, precise pressure relief effect, and high construction automation. The current roof cutting and pressure relief technology based on deep-hole blasting mainly focuses on the principle of shaped charge blasting. By arranging deep holes in the roadway roof and charging and blasting, the explosive stress wave is used to damage the roof rock strata structure, realize the orderly collapse of the roof and the transfer of lateral abutment pressure, and construct a surrounding rock control system suitable for the characteristics of the composite roof to meet the precise control requirements of gob-side entry retaining for surrounding rock stability in deep mines.

[0003] However, traditional deep-hole blasting methods generally have a narrow range of blasting parameter adjustment, and the blasting stress distribution is uneven due to the influence of the bedding structure and lithology differences of the composite roof. It is necessary to frequently adjust the charging structure and blasting parameters according to different roof conditions. At the same time, the ability of automatic charging and parameter optimization is weak, relying on manual experience to set the blasting scheme and having poor long-term construction stability, being easily interfered by borehole deviation, borehole blockage, and changes in the water content of the rock strata. With the surge in the demand for safe and efficient gob-side entry retaining technology in coal mining, traditional methods are difficult to adapt to the complex working conditions of variable composite roof strength and large differences in interlayer bonding force, and the decrease in borehole accuracy and charging deviation after long-term blasting operations significantly affect the stability of the pressure relief effect. Summary of the Invention

[0004] This application provides a method for roof cutting and pressure relief deep-hole pre-splitting blasting for gob-side entry retaining under composite roof conditions to solve problems such as narrow parameter adjustment range, uneven blasting stress distribution, and poor construction stability in the prior art.

[0005] An embodiment of the first aspect of the present application provides a method for pre-splitting blasting with roof cutting and pressure relief in gob-side entry retaining of a composite roof, including the following steps: obtaining geological data, where the geological data includes the rock stratum structure, in-situ stress distribution, and surrounding rock deformation rate of the composite roof; constructing a three-dimensional mechanical model of the composite roof-tunnel space based on the geological data and combining the discrete element method to simulate the roof fracture and caving process under different blasting parameters; based on the roof fracture and caving process under different blasting parameters, integrating the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination; real-time monitoring the data of roof micro-crack propagation and stress change, combining with the target blasting parameter combination, adjusting the blast hole arrangement and charge structure, and based on the adjusted blast hole arrangement and charge structure, using an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely controlling the blasting through high-precision electronic detonators, and forming an orderly pressure relief process according to the initiation time difference.

[0006] Preferably, based on the roof fracture and caving process under different blasting parameters, integrating the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination includes: establishing a BP neural network prediction model; inputting the blasting parameters into the BP neural network prediction model to output the reaching standard rate of roof cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate; based on the reaching standard rate of roof cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate, using the whale optimization algorithm to optimize the weights and thresholds of the BP neural network prediction model; through the optimized BP neural network prediction model, dynamically predicting the optimized target values corresponding to different blasting parameter combinations and outputting the target blasting parameter combination.

[0007] Preferably, the formula of the whale optimization algorithm: where is the updated position vector (at time t + 1); is the current global optimal position (at time t); is the distance between the current position and the global optimal position; is the exponential term, b is the spiral shape parameter, is a random number; is the cosine term.

[0008] Preferably, constructing a three-dimensional mechanical model of the composite roof-tunnel space based on the geological data and combining the discrete element method to simulate the roof fracture and caving process under different blasting parameters includes: establishing a rock stratum particle aggregate model; based on the rock stratum particle aggregate model, setting the contact stiffness, bond strength, and friction coefficient between particles to simulate the initial stress state of the roof under self-weight and in-situ stress; according to the initial stress state, by presetting different blast hole positions, hole diameters, and hole depths, simulating the propagation path of the roof fracture surface and the caving shape.

[0009] Preferably, the data of the roof micro-crack propagation and stress change are monitored in real time. Combining with the target blasting parameter combination, the blast hole arrangement and charging structure are adjusted, including: setting distributed optical fiber sensors; according to the distributed optical fiber sensors, the position, length and stress change data of the roof micro-crack propagation are collected in real time; the position, length and stress change data of the roof micro-crack propagation are compared and analyzed with the target blasting parameter combination. Among them, when a deviation is found in the analysis, the key arrangement parameters such as the spacing, angle and depth of the blast holes and the charging structure are adjusted to optimize the blasting effect.

[0010] Preferably, an intelligent charging robot is used to automatically install blasting equipment and axial air spacers for blasting. At the same time, after an orderly pressure relief process is formed according to the initiation time difference, it includes: using a machine vision recognition mechanism to collect images of the roof fracture surface, and dynamically evaluating the pressure relief effect and generating a feedback report in combination with the stress change data.

[0011] Preferably, using a machine vision recognition mechanism to collect images of the roof fracture surface, and dynamically evaluating the pressure relief effect and generating a feedback report in combination with the stress change data, including: constructing a machine vision recognition mechanism; based on the machine vision recognition mechanism, using a high-definition camera to collect image data of the roof fracture surface; according to the image data, in combination with the Snake algorithm, analyzing the characteristics such as the flatness, fracture angle and fracture range of the fracture surface; according to the characteristics such as the flatness, fracture angle and fracture range of the fracture surface, in combination with the stress change data, evaluating indicators such as the compliance rate of the cutting roof height, the stress reduction rate of the pressure relief area, and the surrounding rock deformation control rate, and generating a pressure relief effect feedback report.

[0012] Preferably, the formula of the Snake algorithm: Among them, is the total energy; is the internal energy; is the contour curve, is the contour, s is the parameter; is the external energy; is the differential of the integral variable s.

[0013] In the second aspect of the embodiments of the present application, a roof cutting and pressure relief deep hole pre-splitting blasting system for gob-side entry retaining with a composite roof is provided, including: an acquisition module for acquiring geological data, where the geological data includes the rock stratum structure of the composite roof, the in-situ stress distribution, and the surrounding rock deformation rate; a construction module for constructing a three-dimensional mechanical model of the composite roof - roadway space based on the geological data in combination with the discrete element method to simulate the roof fracture and caving process under different blasting parameters; an output module for dynamically outputting a target blasting parameter combination based on the roof fracture and caving process under different blasting parameters by integrating the whale optimization algorithm and the BP neural network, with the roof cutting height compliance rate, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate as the optimization objectives; an adjustment module for real-time monitoring of the data on the propagation of roof micro-cracks and stress changes, combining with the target blasting parameter combination to adjust the blast hole layout and charging structure, and based on the adjusted blast hole layout and charging structure, using an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely controlling the blasting through high-precision electronic detonators, and at the same time, forming an orderly pressure relief process according to the initiation time difference.

[0014] In the third aspect of the embodiments of the present application, an electronic device is provided, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the roof cutting and pressure relief deep hole pre-splitting blasting method for gob-side entry retaining with a composite roof as described in the above embodiments.

[0015] Therefore, the present application has the following beneficial effects: By acquiring geological data such as the rock stratum structure of the composite roof, the in-situ stress distribution, and the surrounding rock deformation rate, the embodiments of the present application ensure a high degree of fit between the blasting operation and the actual geological conditions. By constructing a three-dimensional mechanical model of the composite roof - roadway space using the discrete element method, the roof fracture and caving process under different blasting parameters can be intuitively simulated, making the blasting effect predictable and analyzable, reducing the blindness of traditional blasting operations, and lowering the trial-and-error cost. By integrating the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination, taking advantage of the intelligent algorithm to quickly optimize under complex geological conditions, improving the accuracy and adaptability of blasting parameters, providing data for efficient pressure relief, real-time monitoring of the data on the propagation of roof micro-cracks and stress changes, and dynamically adjusting the blast hole layout and charging structure in combination with the target blasting parameter combination. At the same time, using an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely controlling the blasting through high-precision electronic detonators, forming an orderly pressure relief process according to the initiation time difference, improving the construction efficiency and safety, precisely controlling the rhythm of roof fracture and caving, reducing the disturbance of blasting to the surrounding rock of the roadway, ensuring the stability of gob-side entry retaining, and creating good conditions for the safe and efficient mining of the mine. Thus, the problems of narrow parameter adjustment range, uneven blasting stress distribution, and poor construction stability in the prior art are solved.

[0016] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0017] The above-mentioned and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of embodiments in conjunction with the drawings, wherein: Figure 1 FIG. is a flowchart of a roof cutting pressure relief deep hole pre-splitting blasting method for gob-side entry retaining with composite roof according to an embodiment of the present application; Figure 2 FIG. is an example diagram of a coal mine roadway driving project according to an embodiment of the present application; Figure 3 FIG. is an example diagram of a gob-side entry retaining project in a deep mine according to an embodiment of the present application; Figure 4 FIG. is an example diagram of a deep hole blasting pressure relief project for a coal mine composite roof roadway according to an embodiment of the present application; Figure 5 FIG. is an example diagram of a metal mine roadway driving project according to an embodiment of the present application; Figure 6 FIG. is an example diagram of a roof cutting pressure relief project for a coal mine roadway according to an embodiment of the present application; Figure 7 FIG. is a flowchart of a roof cutting pressure relief deep hole pre-splitting blasting method for gob-side entry retaining with composite roof according to an embodiment of the present application; Figure 8 FIG. is a schematic structural diagram of a drilling rig for constructing blasting holes according to an embodiment of the present application; Figure 9 FIG. is a schematic structural diagram of a roof cutting pressure relief deep hole pre-splitting blasting system for gob-side entry retaining with composite roof according to an embodiment of the present application; Figure 10 FIG. is a schematic structural diagram of an electronic device according to an embodiment of the present application. Detailed Description of the Embodiments

[0018] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present application, and should not be construed as limiting the present application.

[0019] The following describes a method for roof cutting pressure relief deep-hole pre-splitting blasting in gob-side entry retaining with a composite roof according to an embodiment of the present application. Aiming at the problem of poor construction stability mentioned in the above background technology, the present application provides a method for roof cutting pressure relief deep-hole pre-splitting blasting in gob-side entry retaining with a composite roof. In this method, by accurately obtaining geological data such as the rock stratum structure, in-situ stress distribution, and surrounding rock deformation rate of the composite roof, it is ensured that the blasting operation highly conforms to the actual geological conditions. With the discrete element method, a three-dimensional mechanical model of the composite roof-tunnel space is constructed to visually simulate the roof fracture and caving process under different blasting parameters, making the blasting effect predictable and analyzable, reducing the blindness of traditional blasting operations, and lowering the trial-and-error cost. The whale optimization algorithm and BP neural network are integrated to dynamically output the target blasting parameter combination. Utilizing the advantages of intelligent algorithms, rapid optimization is carried out under complex geological conditions to improve the accuracy and adaptability of blasting parameters, providing data for efficient pressure relief. The microcrack expansion and stress change data of the roof are monitored in real time, and the blast hole layout and charging structure are dynamically adjusted in combination with the target blasting parameter combination. At the same time, an intelligent charging robot is used to automatically install blasting equipment and axial air spacers, and precise control of blasting is achieved through high-precision electronic detonators. An orderly pressure relief process is formed according to the initiation time difference, improving the construction efficiency and safety, precisely controlling the roof fracture and caving rhythm, reducing the disturbance of blasting to the surrounding rock of the tunnel, and ensuring the stability of gob-side entry retaining, creating good conditions for the safe and efficient mining of the mine. Thus, the problems of narrow parameter adjustment range, uneven blasting stress distribution, and poor construction stability in the prior art are solved.

[0020] Specifically, Figure 1 is a schematic flow chart of the method for roof cutting pressure relief deep-hole pre-splitting blasting in gob-side entry retaining with a composite roof provided by the embodiment of the present application.

[0021] As Figure 1 shown, the method for roof cutting pressure relief deep-hole pre-splitting blasting in gob-side entry retaining with a composite roof includes the following steps: In step S101, geological data is obtained, where the geological data includes the rock stratum structure, in-situ stress distribution, and surrounding rock deformation rate of the composite roof.

[0022] Among them, the surrounding rock deformation rate refers to the change speed of the deformation amount of the surrounding rock in an underground project per unit time.

[0023] It can be understood that in the embodiment of the present application, by quantifying the change speed of the deformation amount per unit time, dynamic boundary conditions are provided for constructing the three-dimensional mechanical model, making the simulated roof fracture and caving process closer to the actual engineering conditions. At the same time, combined with the rock stratum structure and in-situ stress distribution data, the surrounding rock deformation sensitive area can be identified, and the blasting parameter combination can be intelligently optimized to ensure that the adjustment of the blast hole layout, charging structure, and initiation timing can dynamically adapt to the real-time deformation characteristics of the surrounding rock, control the disturbance intensity of the blasting pressure relief process to the surrounding rock, and improve the timeliness and pertinence of the surrounding rock stability control in gob-side entry retaining.

[0024] For example, as Figure 2 shown, in the coal mine roadway driving project, total station, multi-point displacement meter and other equipment are used to monitor the displacement of the surrounding rock of the roadway every 2 hours. By calculating the difference between two adjacent monitoring data, the displacement change amount of the surrounding rock per unit time is obtained, and then the deformation rate of the surrounding rock is obtained. If it is found that in a certain area within 36 hours, the surface displacement of the surrounding rock increases from the initial 5 mm to 25 mm, and its average deformation rate is calculated to be about 0.56 mm / h. When the deformation rate exceeds the preset threshold of 0.3 mm / h, the technical personnel timely adjust the support plan accordingly, strengthen the bolt and cable support strength in this area, and optimize the subsequent deep hole blasting parameters, effectively avoiding the roof fall accident caused by excessive deformation of the surrounding rock.

[0025] In step S102, according to the geological data and combined with the discrete element method, a three-dimensional mechanical model of the composite roof-roadway space is constructed to simulate the roof fracture and caving process under different blasting parameters.

[0026] Among them, the three-dimensional mechanical model refers to a mathematical model constructed based on technologies such as the discrete element method, which is used to simulate the stress distribution, deformation law and fracture and caving process of the composite roof and roadway space under the mechanical action in the three-dimensional space.

[0027] It can be understood that in the embodiment of the present application, by simulating the roof fracture and caving process under different blasting parameters, the dynamic evolution law of the stress transmission path, deformation concentration area and rock layer fracture morphology is visually presented, providing a visual mechanical analysis platform for the blasting scheme design, revealing the internal connection between blasting disturbance and surrounding rock stability, enabling technical personnel to predict in advance the roof response that may be caused by different blasting parameters, reducing the blindness of traditional empirical design, and improving the pertinence and efficiency of blasting parameter optimization; at the same time, by comparing the simulation results with the actual monitoring data, the model parameters can be dynamically calibrated, improving the stability rate of the roadway surrounding rock and the safety of deep hole blasting operations.

[0028] For example, as Figure 3As shown in the figure, in the gob-side entry retaining project of deep mines, the technical team constructed a three-dimensional mechanical model using the discrete element method based on geological data such as the composite roof rock structure and in-situ stress distribution obtained from preliminary exploration. By setting different parameters such as the blast hole spacing and charge amount, the process of roof fracture and caving was simulated, clearly presenting the stress transfer path in the rock strata and the deformation concentration area. For example, when the simulated hole spacing was 2 meters and the charge amount was 5 kg, the model showed that the roof had irregular fractures and obvious stress concentration in the surrounding rock of the roadway; after adjusting the hole spacing to 1.5 meters and reducing the charge amount to 4 kg, the roof caving was more orderly and the stress distribution in the surrounding rock of the roadway tended to be uniform. Finally, the blasting parameters were optimized based on the simulation results. After on-site application, the roadway deformation was effectively reduced and the success rate of gob-side entry retaining was improved, verifying the practical value of the three-dimensional mechanical model in the design of blasting schemes.

[0029] In the embodiment of the present application, according to the geological data and combined with the discrete element method, a three-dimensional mechanical model of the composite roof-roadway space is constructed to simulate the roof fracture and caving process under different blasting parameters, including: establishing a rock stratum particle aggregate model; based on the rock stratum particle aggregate model, setting the contact stiffness, bond strength and friction coefficient between particles to simulate the initial stress state of the roof under self-weight and in-situ stress; according to the initial stress state, by presetting different blast hole positions, hole diameters and hole depths, simulating the propagation path of the roof fracture surface and the caving shape.

[0030] Among them, the rock stratum particle aggregate model is a discrete system model based on the discrete element theory, which regards the rock stratum as composed of particle units connected by contact mechanical relations, and simulates the particle movement, deformation failure and energy transfer process of the rock stratum under stress.

[0031] It can be understood that in the embodiment of the present application, by discretizing the continuous rock stratum into a quantifiable particle unit system, accurately depicting the force chain transmission, relative particle movement and energy dissipation mechanism at the mesoscopic scale inside the rock stratum based on the contact mechanical relations, providing micro-mechanical data for simulating the fracture and caving process of the roof under complex geological conditions, dealing with the non-continuity, anisotropy and non-linear deformation characteristics of the rock stratum, and by setting mesoscopic parameters such as the contact stiffness, bond strength and friction coefficient between particles, the initial stress balance state of the roof under self-weight stress and tectonic stress can be truly restored, providing a reliable numerical carrier for the dynamic evolution process of bond failure between particles, crack initiation and propagation, and rock mass migration under blasting disturbance, breaking through the simplified assumptions of the traditional continuous medium model for the fracture and failure process, revealing the meso-mechanical mechanism of blasting stress wave propagation and rock stratum caving from the level of particle interaction, enabling technicians to intuitively quantify the influence of different blasting parameters on the stability of the roof particle system, providing simulation data for optimizing the blast hole layout and controlling the fracture surface shape, and improving the scientificity and engineering reliability of deep hole blasting pressure relief design.

[0032] For example, as Figure 4As shown, in the deep-hole blasting pressure relief project of a coal mine composite roof roadway, the technical team used the rock particle aggregate model to discretize the roof rock into a large number of particle units, and set the contact stiffness between particles to 1.2×10 9 N / m, the bonding strength to 3.5 MPa, and the friction coefficient to 0.4 according to the geological exploration data, and accurately simulated the initial state of the roof under its own weight and in-situ stress. Subsequently, by presetting different blasting hole positions, hole diameters, and hole depths, the simulation showed that when the blasting hole spacing was 1.8 m, the hole diameter was 45 mm, and the hole depth was 8 m, the bonding between the roof particles in the model gradually failed, and cracks spread fan-shaped along the blast holes, and finally a stable caving shape was formed. After optimizing the blasting plan based on the model simulation results, the caving effect of the roof during on-site construction was highly consistent with the simulation, and the deformation of the roadway surrounding rock was reduced by 40%, successfully verifying the practical value of the rock particle aggregate model in guiding blasting design and ensuring project safety.

[0033] In step S103, based on the roof fracture and caving process under different blasting parameters, the whale optimization algorithm and the BP neural network are fused to dynamically output the target blasting parameter combination.

[0034] Among them, the BP neural network is a multi-layer feedforward neural network based on the error backpropagation algorithm.

[0035] It can be understood that in the embodiments of the present application, through the learning and training of the three-dimensional mechanical model simulation data or on-site monitoring data, an accurate blasting effect prediction model is constructed, and the network weights are continuously optimized using the error backpropagation mechanism, and the blasting parameter-roof response law under different geological conditions is deeply fitted, providing an evaluation basis for the target function of the whale optimization algorithm. The BP neural network can transform the multi-dimensional search space of the blasting parameter combination into a quantifiable fitness function, enabling the whale optimization algorithm to quickly locate the optimal solution globally, improving the optimization efficiency and accuracy of the target blasting parameters under complex working conditions, making the blasting parameters meet the pressure relief requirements of the roof for orderly caving, and dynamically adapting to the real-time mechanical state of the surrounding rock.

[0036] For example, in the blasting project of mine roadways under complex geological conditions, technicians use the BP neural network to optimize blasting parameters. First, a large amount of past blasting parameter data under different geological conditions, as well as the monitoring data of the corresponding roof fracture and caving process, such as roof fragmentation degree, caving range, roadway surrounding rock deformation amount, etc., are collected. Then, these data are sorted into a training set and a test set and input into the BP neural network model. The network gradually learns the complex relationship between blasting parameters and roof response by continuously adjusting the connection weights between neurons. After multiple iterative trainings, when a new set of mine geological data and preliminary blasting parameters are input, the BP neural network can quickly predict the possible roof fracture and caving effect. For example, in a simulation, the preliminary set parameters such as the spacing between blasting holes and the charge amount are predicted by the BP neural network, and the roof fragmentation effect is not good and the deformation amount of the roadway surrounding rock exceeds the safety range; after the network optimization suggestions, the spacing between blasting holes and the charge amount are adjusted, and the re-prediction shows that the roof caving form is good and the surrounding rock deformation is controllable. Finally, the mine conducts actual blasting operations according to the blasting parameters optimized by the BP neural network, which not only improves the blasting efficiency, but also ensures the stability of the roadway, reduces the support cost and potential safety hazards.

[0037] In the embodiment of the present application, based on the roof fracture and caving process under different blasting parameters, the whale optimization algorithm and the BP neural network are fused to dynamically output the target blasting parameter combination, including: establishing a BP neural network prediction model; inputting the blasting parameters into the BP neural network prediction model, and outputting the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate; based on the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate, using the whale optimization algorithm to optimize the weights and thresholds of the BP neural network prediction model; through the optimized BP neural network prediction model, dynamically predicting the optimized target values corresponding to different blasting parameter combinations, and outputting the target blasting parameter combination.

[0038] Among them, the blasting parameter combination refers to a set of parameters formed by scientifically matching multiple parameters such as the position of the blast hole, the hole diameter, the hole depth, the charge amount, the charge structure, and the initiation time difference in deep hole blasting operations to achieve a specific blasting target (such as the orderly fracture and caving of a composite roof, pressure relief).

[0039] It can be understood that in the embodiment of the present application, by quantifying key indicators such as the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate, the mapping relationship between the blasting effect and the parameters is dynamically constructed, reducing the parameter deviation caused by traditional empirical design, and accurately designing the blasting plan; improving the controllability of roof fracture and caving, ensuring the pressure relief effect and the stability of the surrounding rock, reducing the consumption of blasting materials and the support cost, and reducing the safety risks caused by improper parameters, providing a comprehensive solution that takes into account both safety and efficiency for the deep hole blasting pressure relief project.

[0040] In the embodiments of the present application, the formula of the whale optimization algorithm is as follows: Wherein, is the position vector after update (at time t+1); is the current global optimal position (at time t); is the distance between the current position and the global optimal position; is the exponential term, b is the spiral shape parameter, is a random number; is the cosine term.

[0041] It can be understood that in the embodiments of the present application, by simulating the unique spiral bubble net predation behavior of humpback whales, during the optimization process of blasting parameters, with a powerful global search ability, it efficiently explores in the complex parameter solution space. Relying on evaluation indicators such as the passing rate of the cut-off height and the stress reduction rate in the pressure relief area output by the BP neural network prediction model, it continuously adjusts the search strategy to prevent falling into local optimal solutions. Through iterative optimization of the weights and thresholds of the BP neural network, it accurately locates the blasting parameter combination that meets the requirements of roof pressure relief and roadway stability, shortens the parameter optimization time, and improves the efficiency and accuracy of blasting parameter optimization.

[0042] For example, in a deep-hole blasting project in a lignite mine with complex strata structures, technicians first input multiple groups of blasting parameters and corresponding roof caving data collected into the BP neural network prediction model to obtain evaluation indicators such as the passing rate of the cut-off height and the stress reduction rate in the pressure relief area. Then, guided by these indicators, the whale optimization algorithm simulates the spiral predation path of humpback whales and explores in the huge solution space of blasting parameter combinations. At the initial stage of the algorithm operation, parameters such as the hole diameter, hole depth, and initiation time difference set by the system, after being evaluated by the BP neural network, are difficult to meet the pressure relief requirements. After more than 50 iterations of the whale optimization algorithm, adjusting the weights and thresholds of the BP neural network, the finally determined new blasting parameter combination increases the stress reduction rate in the pressure relief area from the initial 40% to 65%, and the passing rate of the cut-off height from 70% to 92%. Blasting operations are carried out according to the optimized parameters, and the roof collapses smoothly as expected, and the roadway deformation is controlled within the safe range. Compared with the traditional method of manually adjusting parameters, it saves nearly 40% of the time cost and 25% of the blasting material cost.

[0043] In step S104, the data of the roof microcrack propagation and stress change are monitored in real time. Combining with the target blasting parameter combination, the hole layout and charging structure are adjusted. Based on the adjusted hole layout and charging structure, an intelligent charging robot is used to automatically install blasting equipment and axial air spacers, and precise blasting is controlled through high-precision electronic detonators to form an orderly pressure relief process according to the initiation time difference.

[0044] Among them, the axial air spacer is a device used in deep-hole blasting operations. It is arranged axially along the blast hole between explosives to form an air spacer layer, so as to adjust the distribution of explosion energy and control the blasting effect.

[0045] It can be understood that in the embodiments of the present application, by arranging an air spacer layer axially along the blast hole, the temporal and spatial distribution of the explosion energy of the explosive is adjusted, the concentrated detonation shock is converted into a continuously loaded stress wave, and the sudden fragmentation of the roof or excessive damage to the surrounding rock caused by excessive energy concentration is avoided. By controlling the expansion speed and pressure decay law of the detonation products, the explosion energy acts more uniformly on the roof rock formation, promoting the orderly expansion and penetration of microcracks, improving the compliance rate of the cutting height and the stress reduction effect in the pressure relief area; at the same time, the air spacer layer can buffer the initial impact load of the explosion, reduce the dynamic disturbance of the blasting vibration to the surrounding rock of the roadway, cooperate with the initiation time difference control of high-precision electronic detonators, ensure the deep-hole blasting pressure relief efficiency, and at the same time, reduce the consumption of blasting materials and the cost of surrounding rock support.

[0046] For example, in the deep roadway blasting project of a metal mine, in view of the high-stress composite roof condition, the technical team installed an axial air spacer between the explosive columns in the blast hole to form an air spacer layer with a thickness of 0.3 meters. The distance between the spacers was accurately controlled by an intelligent charging robot, and segmented initiation was carried out in cooperation with high-precision electronic detonators. The monitoring after blasting showed that the explosion energy was evenly distributed along the axial direction of the blast hole, the microcracks on the roof extended orderly in a network pattern, the compliance rate of the cutting height increased from 80% of the conventional blasting to 95%, the average stress in the pressure relief area decreased by 35%, and the peak vibration velocity of the surrounding rock of the roadway decreased by 30% compared with the traditional continuous charging blasting, effectively protecting the roadway support structure. At the same time, the application of the air spacer reduced the explosive consumption by 20%. While realizing the directional collapse of the roof, the safety and economy of the blasting operation were significantly improved.

[0047] In the embodiments of the present application, the real-time monitoring of the microcrack propagation and stress change data of the roof, combined with the target blasting parameter combination, is used to adjust the blast hole arrangement and charging structure, including: setting distributed fiber optic sensors; according to the distributed fiber optic sensors, the position, length and stress change data of the microcrack propagation on the roof are collected in real time; the position, length and stress change data of the microcrack propagation on the roof are compared and analyzed with the target blasting parameter combination. Among them, when a deviation is found in the analysis, the key arrangement parameters such as the distance, angle and depth of the blast holes and the charging structure are adjusted to optimize the blasting effect.

[0048] Among them, the distributed fiber optic sensor is a sensing technology based on the optical fiber transmission characteristics. By monitoring the scattering, interference and other changes of the optical signal when it propagates in the optical fiber, the physical quantities such as temperature, strain and vibration distributed along the optical fiber are continuously and real-time monitored.

[0049] It can be understood that in the embodiments of the present application, by using a distributed optical fiber sensor, based on the optical fiber transmission characteristics, the position, length, and stress changes of the roof microcrack propagation can be continuously and real-time monitored. By capturing the mesoscopic deformation and mechanical state evolution of the rock formation through the optical signal scattering / interference characteristics, full-coverage dynamic perception of the area along the optical fiber distribution can be achieved, providing high-density and high-precision real-time data for optimizing blasting parameters. By combining and real-time comparing and analyzing the monitoring data with the target blasting parameters, abnormal microcrack propagation or stress distribution deviation can be quickly identified, and the hole spacing, angle, depth, and charging structure can be dynamically adjusted to improve the controllability and accuracy of the blasting effect, prevent the risk of roof instability caused by lagging parameter adjustment, and reduce the manual monitoring cost at the same time.

[0050] For example, as Figure 5 shown, in the roadway driving project of a metal mine, the technical team implanted a distributed optical fiber sensor in the composite roof to monitor the propagation of rock formation microcracks and stress changes in real time. When the sensor feedback shows that the growth rate of the microcrack length in a certain section of the roof is 15% lower than the expected value and the stress reduction in the local stress concentration area is less than 60%, the system quickly compares the target blasting parameters and judges that the insufficient hole depth leads to a limited energy action range. Immediately, the hole depth in this area is adjusted from the original design of 6 meters to 8 meters, and the charging structure is optimized. After the second blasting, the sensor shows that the penetration rate of the microcracks along the predetermined direction reaches 90%, the stress reduction rate in the pressure relief area increases to 78%, and the maximum deformation of the surrounding rock of the roadway decreases from 45 mm to 28 mm, fully demonstrating the key effectiveness of the distributed optical fiber sensor in accurately capturing the dynamic response of the rock formation and real-time optimizing blasting parameters, providing a typical example of intelligent monitoring and control for roadway blasting operations under complex geological conditions.

[0051] In the embodiments of the present application, an intelligent charging robot is used to automatically install blasting equipment and axial air spacers for blasting. At the same time, after forming an orderly pressure relief process according to the initiation time difference, it includes: using a machine vision recognition mechanism to collect images of the roof fracture surface, dynamically evaluating the pressure relief effect in combination with stress change data, and generating a feedback report.

[0052] It can be understood that in the embodiments of the present application, by using a distributed optical fiber sensor, based on the optical fiber transmission characteristics, the position, length, and stress changes of the roof microcrack propagation can be continuously and real-time monitored. By capturing the mesoscopic deformation and mechanical state evolution of the rock formation through the optical signal scattering / interference characteristics, full-coverage dynamic perception of the area along the optical fiber distribution can be achieved, providing high-density and high-precision real-time data for optimizing blasting parameters. By combining and real-time comparing and analyzing the monitoring data with the target blasting parameters, abnormal microcrack propagation or stress distribution deviation can be quickly identified, and the hole spacing, angle, depth, and charging structure can be dynamically adjusted to improve the controllability and accuracy of the blasting effect, prevent the risk of roof instability caused by lagging parameter adjustment, and reduce the manual monitoring cost at the same time.

[0053] In the embodiment of the present application, a machine vision recognition mechanism is used to collect images of the roof fracture surface, and the pressure relief effect is dynamically evaluated in combination with stress change data and a feedback report is generated, including: constructing a machine vision recognition mechanism; based on the machine vision recognition mechanism, using a high-definition camera to collect image data of the roof fracture surface; according to the image data, in combination with the Snake algorithm, analyzing features such as the flatness, fracture angle, and fracture range of the fracture surface; according to features such as the flatness, fracture angle, and fracture range of the fracture surface, in combination with stress change data, evaluating indicators such as the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate, and generating a feedback report on the pressure relief effect.

[0054] Among them, the machine vision recognition mechanism is a technical mechanism for automatically detecting, classifying, measuring the size, or analyzing the state of an object through image acquisition devices such as cameras, and through preprocessing, feature extraction, and pattern recognition algorithms.

[0055] It can be understood that in the embodiment of the present application, the high-definition camera is used to automatically collect images of the roof fracture surface. In combination with pattern recognition technologies such as the Snake algorithm, the microscopic features such as the flatness, fracture angle, and range of the fracture surface are accurately extracted, a visual evaluation system for the blasting effect is constructed, the image features and stress change data are deeply fused, the key indicators such as the compliance rate of the cutting height and the stress reduction rate in the pressure relief area are dynamically quantified, a feedback report including parameter deviation analysis and optimization suggestions is generated in real time, the difference between the fracture surface shape and the design target is quickly identified, intuitive visual data is provided for the adjustment of blasting parameters, and the evaluation efficiency is improved through automated processing, reducing manual measurement errors and subjective misjudgments.

[0056] For example, as Figure 6 shown, in the roof cutting and pressure relief project of a coal mine roadway, the technical team applied the machine vision recognition mechanism to collect the fracture surface images after blasting through the explosion-proof high-definition camera installed on the roof. Using a deep learning algorithm (such as YOLO) to preprocess and extract features from the images, it was found through analysis that the fracture angle deviation reached 15°, and the local fracture range was reduced by 22% compared with the design value. After comprehensive evaluation in combination with stress data, the system generated a feedback report, suggesting adjusting the inclination angle of the blast holes to the design value and optimizing the charge structure. After adjusting the parameters according to the suggestions and blasting again, the images collected by the machine vision showed that the fracture angle deviation was reduced to 3°, the fracture range coverage rate was increased to 92%, the stress reduction rate in the pressure relief area was increased from 60% to 80%, and the evaluation time was shortened from 4 hours of manual detection to 1.5 hours, significantly improving the timeliness of the blasting effect evaluation and the accuracy of parameter optimization, and providing an efficient visual solution for roof control under complex coal seam conditions.

[0057] In the embodiment of the present application, the Snake algorithm formula: in, is the total energy; for internal energy; is the contour curve, is the contour, s is the parameter; For external energy; is the differential of the integral variable s.

[0058] It can be understood that the embodiment of the present application constructs an energy function to drive the contour curve to automatically fit the target boundary in the roof fracture surface image, accurately captures the complex geometric features of the fracture surface, and aims at the irregular cracks, local broken areas and other complex forms that may exist on the fracture surface after blasting. By iteratively optimizing the position and shape of the contour curve, the key parameters such as the flatness, fracture angle and range of the fracture surface are adaptively extracted, thereby reducing the contour fracture or misjudgment problem of the traditional edge detection algorithm under noise interference. After combining with high-definition image data, the extraction accuracy of the fracture surface features is improved to the sub-pixel level, providing high-precision visual data for the quantitative evaluation of indicators such as the cutting height compliance rate and the stress reduction rate in the pressure relief area. At the same time, automated processing is used instead of manual measurement to improve the efficiency of feature analysis and reduce human interpretation deviations.

[0059] For example, in the lignite mine roof management scenario, a machine vision recognition mechanism is first built to collect roof fracture surface image data with the help of a high-definition camera. Then, the Snake algorithm comes into play to give "elasticity" to the initial contour. With the internal energy ensuring the reasonable shape of the contour and the external energy guided by the image gradient, the contour gradually approaches the real boundary of the fracture surface like an intelligent tracker, thereby accurately analyzing the key features of the fracture surface such as flatness, angle and range. Finally, these image analysis results are integrated with stress change data to evaluate indicators such as the top cutting height compliance rate, stress reduction rate in the pressure relief zone, and surrounding rock deformation control rate, and generate a detailed pressure relief effect feedback report to help mines scientifically plan pressure relief operations and ensure safe production.

[0060] According to the roof cutting and pressure relief deep-hole pre-splitting blasting method for gob-side entry retaining with composite roof proposed in the embodiments of the present application, by accurately obtaining geological data such as the rock stratum structure, in-situ stress distribution, and surrounding rock deformation rate of the composite roof, it ensures that the blasting operation highly conforms to the actual geological conditions. With the help of the discrete element method, a three-dimensional mechanical model of the composite roof - roadway space is constructed to visually simulate the roof fracture and caving process under different blasting parameters, making the blasting effect predictable and analyzable, reducing the blindness of traditional blasting operations, and lowering the trial-and-error cost. Integrating the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination, taking advantage of the intelligent algorithm to quickly optimize under complex geological conditions, improving the accuracy and adaptability of blasting parameters, providing data for efficient pressure relief, real-time monitoring the data of roof micro-crack propagation and stress change, and dynamically adjusting the blast hole layout and charging structure in combination with the target blasting parameter combination. At the same time, using an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely controlling the blasting through high-precision electronic detonators, forming an orderly pressure relief process according to the initiation time difference, improving the construction efficiency and safety, precisely controlling the rhythm of roof fracture and caving, reducing the disturbance of blasting to the roadway surrounding rock, and ensuring the stability of gob-side entry retaining, creating good conditions for the safe and efficient mining of the mine. Thus, the problems of narrow parameter adjustment range, uneven blasting stress distribution, and poor construction stability in the prior art are solved.

[0061] The roof cutting and pressure relief deep-hole pre-splitting blasting method for gob-side entry retaining with composite roof will be elaborated through a specific embodiment as follows. Figure 7 As shown in the figure, it includes: At a position 0.5 m from the roof on the goaf return air side roadway rib, use a drill rig with automatic deviation correction function as shown in Figure 8 the figure to construct blasting holes. The hole depth penetrates the sandy mudstone to the lower coal seam to cut off the key bearing layer of the roof. Set reasonable hole spacing and inclination angle pointing to the goaf. Arrange a set of holes in a plum blossom shape every 10 m, and use a laser angle measuring instrument to calibrate the angle in real time to control the error of the hole opening position.

[0062] Select Φ70 mm low-blast velocity emulsion explosive with a blast velocity ≤ 3000 m / s, which can effectively reduce the vibration damage of blasting to the roadway surrounding rock. The single-hole charge amount is 8 kg, divided into 4 segments of charging, 2 kg for each segment, and separated by a 150 mm thick high-strength plastic spacer between segments to ensure uniform distribution of explosive energy. The hole mouth is sealed with quick-setting cement for 3 m. The initial setting time of this cement is only 15 minutes, and the final setting time is 30 minutes, which can quickly form an effective plugging structure. Install an MS-15 segment millisecond delay detonator with a delay time of 1400 ms to achieve hole-by-hole millisecond delay blasting and reduce the superposition effect of blasting vibration.

[0063] Adopt the "hole-by-hole decoupled initiation" method. First, detonate the middle holes, and then detonate the two side holes after a 50-ms interval. This initiation sequence can form a good free face and improve the blasting effect. Through pre-simulation with blasting design software, strictly control the single-hole initiation energy within 120 kJ / m³. At the same time, use a BCJ-2 type blasting vibration monitor to monitor the blasting vibration velocity in real time to ensure that the monitored value ≤ 5 cm / s, avoiding damage to the roadway support structure and surrounding rock mass caused by blasting. Before blasting, take shockproof reinforcement measures for the mechanical and electrical equipment in adjacent roadways, and protect cables, pipelines, etc. to prevent equipment damage or pipeline leakage caused by blasting vibration.

[0064] Quickly complete the reinforcement support for the roadway side within 2 h after blasting. Use Φ22mm×2500mm left-handed threaded steel bolts with a row and column spacing of 800mm×800mm. The pre-tightening force of the bolts shall not be less than 150 N·m, and cooperate with a steel mesh (mesh 100mm×100mm) to enhance the integrity of the surrounding rock mass of the roadway side. The roof adopts a combined support of "anchor cable beam + steel strip". The specification of the anchor cable is Φ17.8mm×8300mm, and the row spacing is 1600mm. The pre-tightening force of the anchor cable reaches 200 kN. Connect the anchor cables into a whole through the steel strip to effectively control the roof subsidence. Set up a ZQL2000 / 25 / 35 type pneumatic advanced support 30 m behind the working face. The support strength of this support reaches 0.35 MPa, and it can be quickly lifted and lowered to adapt to the advancing speed of the working face. In the advanced support area, set obvious warning signs and strictly prohibit irrelevant personnel from entering. At the same time, arrange special personnel to monitor the support pressure in real time. When the pressure exceeds the warning value, take reinforcement measures in time.

[0065] During the roadway retention period, use monitoring equipment such as multi-point displacement gauges and anchor (cable) force gauges arranged on the roof, floor and two sides of the roadway to monitor the roadway deformation in real time. The monitoring data shows that the cumulative roof subsidence is 120 mm (allowable value 200 mm), and the roadway side displacement is 85 mm, both within the safe allowable range. Scan the roof on the goaf side through 3D laser scanning technology. The results show that a continuous caving zone is formed with a height of 10.2 m, effectively cutting off the connection between the roof and the goaf, relieving the lateral pressure, and achieving the goal of "cutting the roof - relieving the lateral pressure - stabilizing the retained roadway". In addition, during the implementation of this method, the drilling efficiency can reach 1.2 m / min, and the single-hole blasting cost is reduced by 18% compared with the traditional method, significantly improving the economic efficiency and construction efficiency while ensuring construction safety and engineering quality.

[0066] In summary, the present invention accurately obtains geological data such as the rock stratum structure, in-situ stress distribution, and surrounding rock deformation rate of the composite roof, ensuring a high degree of fit between the blasting operation and the actual geological conditions. By using the discrete element method to construct a three-dimensional mechanical model of the composite roof-tunnel space, it intuitively simulates the roof fracture and caving process under different blasting parameters, making the blasting effect predictable and analyzable, reducing the blindness of traditional blasting operations, and lowering the trial-and-error cost. It integrates the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination, utilizes the advantages of intelligent algorithms to quickly optimize under complex geological conditions, improves the accuracy and adaptability of blasting parameters, provides data for efficient pressure relief, real-time monitors the data of roof microcrack propagation and stress change, and dynamically adjusts the blast hole layout and charging structure in combination with the target blasting parameter combination. At the same time, it uses an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely controls the blasting through high-precision electronic detonators, forms an orderly pressure relief process according to the initiation time difference, improves the construction efficiency and safety, precisely controls the roof fracture and caving rhythm, reduces the disturbance of blasting to the roadway surrounding rock, and ensures the stability of gob-side entry retaining, creating good conditions for the safe and efficient mining of the mine. Thus, the problems of narrow parameter adjustment range, uneven blasting stress distribution, and poor construction stability in the prior art are solved.

[0067] Next, a gob-side entry retaining roof cutting and pressure relief deep-hole pre-splitting blasting system according to an embodiment of the present application is described with reference to the accompanying drawings.

[0068] Figure 9 It is a block diagram of the gob-side entry retaining roof cutting and pressure relief deep-hole pre-splitting blasting system according to an embodiment of the present application.

[0069] As Figure 9 shown, the gob-side entry retaining roof cutting and pressure relief deep-hole pre-splitting blasting system 10 includes: an acquisition module 100, a construction module 200, an output module 300, and an adjustment module 400.

[0070] Among them, the acquisition module 100 is used to acquire geological data, where the geological data includes the rock layer structure of the composite roof, the ground stress distribution, and the surrounding rock deformation rate; the construction module 200 is used to construct a three-dimensional mechanical model of the composite roof-tunnel space according to the geological data and in combination with the discrete element method to simulate the roof fracture and caving process under different blasting parameters; the output module 300 is used to fuse the whale optimization algorithm and the BP neural network based on the roof fracture and caving process under different blasting parameters, and dynamically output the target blasting parameter combination with the passing rate of the cutting height, the stress reduction rate of the pressure relief area, and the surrounding rock deformation control rate as the optimization objectives; the adjustment module 400 is used to monitor the data of the roof microcrack propagation and stress change in real time, combine with the target blasting parameter combination to adjust the blast hole arrangement and the charging structure, and based on the adjusted blast hole arrangement and the charging structure, use an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely control the blasting through high-precision electronic detonators, and at the same time, form an orderly pressure relief process according to the initiation time difference.

[0071] It should be noted that the foregoing explanation of the embodiment of the roof cutting and pressure relief deep hole pre-splitting blasting method for gob-side entry retaining with a composite roof also applies to the roof cutting and pressure relief deep hole pre-splitting blasting system of this embodiment, and will not be elaborated here.

[0072] The roof cutting and pressure relief deep hole pre-splitting blasting system proposed according to the embodiment of the present application ensures a high degree of fit between the blasting operation and the actual geological conditions by acquiring geological data such as the rock layer structure of the composite roof, the ground stress distribution, and the surrounding rock deformation rate. By using the discrete element method to construct a three-dimensional mechanical model of the composite roof-tunnel space, it intuitively simulates the roof fracture and caving process under different blasting parameters, making the blasting effect predictable and analyzable, reducing the blindness of traditional blasting operations, and lowering the trial-and-error cost. It fuses the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination, takes advantage of the intelligent algorithm to quickly optimize under complex geological conditions, improves the accuracy and adaptability of the blasting parameters, provides data for efficient pressure relief, monitors the data of the roof microcrack propagation and stress change in real time, and dynamically adjusts the blast hole arrangement and the charging structure in combination with the target blasting parameter combination. At the same time, it uses an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely controls the blasting through high-precision electronic detonators, forms an orderly pressure relief process according to the initiation time difference, improves the construction efficiency and safety, precisely controls the rhythm of roof fracture and caving, reduces the disturbance of blasting to the surrounding rock of the roadway, and ensures the stability of gob-side entry retaining, creating good conditions for the safe and efficient mining of the mine. Thus, the problems of narrow parameter adjustment range, uneven blasting stress distribution, and poor construction stability in the prior art are solved.

[0073] Figure 10 The structural schematic diagram of the electronic device provided by the embodiment of the present application. The electronic device may include: A memory 1001, a processor 1002, and a computer program stored on the memory 1001 and executable on the processor 1002.

[0074] When the processor 1002 executes the program, it implements the roof cutting and pressure relief deep hole pre-splitting blasting method for gob-side entry retaining of the composite roof provided in the above embodiments.

[0075] Furthermore, the electronic device further includes: A communication interface 1003 for communication between the memory 1001 and the processor 1002.

[0076] The memory 1001 is used to store a computer program executable on the processor 1002.

[0077] The memory 1001 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0078] If the memory 1001, the processor 1002, and the communication interface 1003 are implemented independently, the communication interface 1003, the memory 1001, and the processor 1002 can be interconnected through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 only a thick line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0079] Optionally, in specific implementation, if the memory 1001, the processor 1002, and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002, and the communication interface 1003 can communicate with each other through an internal interface.

[0080] The processor 1002 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0081] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for roof cutting and pressure relief deep-hole pre-splitting blasting in gob-side entry retaining with composite roof is implemented.

[0082] In the description of this specification, the descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0083] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of these features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0084] Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of the present application.

[0085] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following well-known technologies in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0086] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A method for pre-splitting blasting of deep holes with roof cutting and pressure relief along the gob-side entry retaining of a composite roof, characterized in that Including: Obtain geological data, where the geological data includes the rock stratum structure of the composite roof, the in-situ stress distribution, and the surrounding rock deformation rate; According to the geological data, combined with the discrete element method, construct a three-dimensional mechanical model of the composite roof - roadway space to simulate the roof fracture and caving process under different blasting parameters; Based on the roof fracture and caving process under the different blasting parameters, fuse the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination; Real-time monitor the data of roof microcrack propagation and stress change, combine with the target blasting parameter combination, adjust the blast hole layout and charge structure. Based on the adjusted blast hole layout and charge structure, use an intelligent charging robot to automatically install blasting equipment and axial air spacers, precisely control the blasting through high-precision electronic detonators, and form an orderly pressure relief process according to the initiation time difference.

2. The roof cutting and pressure relief deep hole pre-splitting blasting method for gob-side entry retaining of the composite roof according to claim 1, characterized in that, Based on the roof fracture and caving process under the different blasting parameters, fuse the whale optimization algorithm and the BP neural network to dynamically output the target blasting parameter combination, including: Establish a BP neural network prediction model; Input the blasting parameters into the BP neural network prediction model to output the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate; Based on the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate, use the whale optimization algorithm to optimize the weights and thresholds of the BP neural network prediction model; Through the optimized BP neural network prediction model, dynamically predict the optimized target values corresponding to different blasting parameter combinations and output the target blasting parameter combination.

3. The roof cutting and pressure relief deep-hole pre-splitting blasting method for gob-side entry retaining of the composite roof according to claim 2, characterized in that, The formula of the whale optimization algorithm: Among them, is the position vector after update (at time t + 1); is the current global optimal position (at time t); is the distance between the current position and the global optimal position; is the exponential term, b is the spiral shape parameter, is a random number; is the cosine term.

4. The roof cutting and pressure relief deep-hole pre-splitting blasting method for gob-side entry retaining of the composite roof according to claim 1, characterized in that, The constructing a three-dimensional mechanical model of the composite roof - roadway space according to the geological data, combined with the discrete element method, to simulate the roof fracture and caving process under different blasting parameters, includes: Establish a rock stratum particle aggregate model; Based on the rock stratum particle aggregate model, set the contact stiffness, bond strength, and friction coefficient between particles to simulate the initial stress state of the roof under self-weight and in-situ stress; According to the initial stress state, by presetting different blast hole positions, hole diameters, and hole depths, simulate the propagation path of the roof fracture surface and the caving shape.

5. The roof cutting and pressure relief deep hole pre-splitting blasting method for gob-side entry retaining of the composite roof according to claim 1, characterized in that Real-time monitor the data of roof microcrack propagation and stress change, combine with the target blasting parameter combination, adjust the blast hole layout and charge structure, including: Set distributed fiber optic sensors; According to the distributed fiber optic sensors, collect the position, length, and stress change data of roof microcrack propagation in real time; Compare and analyze the position, length, and stress change data of the roof microcrack propagation with the target blasting parameter combination. Among them, when a deviation is found in the analysis, adjust the key layout parameters such as the spacing, angle, and depth of the blast holes and the charge structure to optimize the blasting effect.

6. The roof cutting and pressure relief deep hole pre-splitting blasting method for gob-side entry retaining of the composite roof according to claim 1, characterized in that, After using an intelligent charging robot to automatically install blasting equipment and axial air spacers for blasting, and at the same time forming an orderly pressure relief process according to the initiation time difference, including: using a machine vision recognition mechanism to collect the roof fracture surface image, dynamically evaluate the pressure relief effect in combination with the stress change data, and generate a feedback report.

7. The roof cutting and pressure relief deep hole pre-splitting blasting method for gob-side entry retaining of the composite roof according to claim 6, characterized in that Collect the image of the roof fracture surface using a machine vision recognition mechanism, dynamically evaluate the pressure relief effect in combination with stress change data, and generate a feedback report, including: Construct a machine vision recognition mechanism; Based on the machine vision recognition mechanism, use a high-definition camera to collect image data of the roof fracture surface; According to the image data, combined with the Snake algorithm, analyze the characteristics such as the flatness, fracture angle, and fracture range of the fracture surface; According to the characteristics such as the flatness, fracture angle, and fracture range of the fracture surface, combined with stress change data, evaluate indicators such as the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate, and generate a feedback report on the pressure relief effect.

8. The roof cutting and pressure relief deep-hole pre-splitting blasting method for gob-side entry retaining of the composite roof according to claim 7, characterized in that The formula of the Snake algorithm: Among them, is the total energy; is the internal energy; is the contour curve, is the contour, s is the parameter; is the external energy; is the differential of the integration variable s.

9. A gob-side entry retaining roof cutting and pressure-relieving deep-hole pre-splitting blasting system for a composite roof, characterized in that, Including: An acquisition module for acquiring geological data, where the geological data includes the rock layer structure of the composite roof, the in-situ stress distribution, and the surrounding rock deformation rate; A construction module for constructing a three-dimensional mechanical model of the composite roof - roadway space based on the geological data in combination with the discrete element method, and simulating the roof fracture and caving process under different blasting parameters; An output module for dynamically outputting a target blasting parameter combination based on the roof fracture and caving process under different blasting parameters, integrating the whale optimization algorithm and the BP neural network, with the compliance rate of the cutting height, the stress reduction rate in the pressure relief area, and the surrounding rock deformation control rate as the optimization objectives; An adjustment module for real-time monitoring of the extension of roof microcracks and stress change data, adjusting the blast hole layout and charging structure in combination with the target blasting parameter combination, automatically installing blasting materials and axial air spacers using an intelligent charging robot based on the adjusted blast hole layout and charging structure, precisely controlling blasting through high-precision electronic detonators, and at the same time, forming an orderly pressure relief process according to the initiation time difference.

10. An electronic device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the method for roof cutting and pressure relief deep hole pre-splitting blasting for gob-side entry retaining of composite roofs as claimed in claims 1-8.

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