A detection device for blasting surrounding rock of super-large cross-section oil storage caverns

Abstract

The present invention discloses a detection device for blasting surrounding rock in ultra-large-section oil storage caverns. This device, belonging to the field of blasting detection technology, comprises a device body and a blasting analysis system within an integrated controller. The system includes: a blasting environment analysis module that outputs an environmental state coefficient based on peak vibration velocity and dust concentration; a blasting damage analysis module that outputs a damage coefficient based on displacement, inclination, and acoustic wave amplitude reduction; a blasting damage prediction module that outputs a destruction coefficient based on the environment, the damage coefficient, charge density, and hole spacing index; and a charge analysis module that dynamically optimizes the target charge based on the destruction coefficient. The present invention enables real-time blasting damage assessment and precise charge quantity control, ensuring surrounding rock stability.

Description

Technical Field

[0001] The invention belongs to the technical field of blasting detection, and in particular relates to a detection device for blasting surrounding rocks of an ultra-large-section oil storage cavern. Background Art

[0002] Controlling surrounding rock stability is crucial during blasting construction of ultra-large cross-section oil storage caverns. Traditional methods rely primarily on empirical formulas and static safety thresholds, making it difficult to quantify the synergistic effects of blasting damage and environmental risks in real time. Existing detection devices have the following limitations:

[0003] 1) It is impossible to integrate multi-dimensional environmental parameters such as vibration speed and dust concentration;

[0004] 2) Lack of comprehensive damage assessment of borehole displacement, inclination, and acoustic wave drop;

[0005] 3) Delayed adjustment of charge quantity leads to the risk of over-excavation or under-excavation of surrounding rock.

[0006] Therefore, there is an urgent need for a detection device that can dynamically predict blasting damage and intelligently optimize the charge amount. Summary of the Invention

[0007] In view of the deficiencies in the prior art, the present invention provides a detection device for blasting surrounding rocks of an ultra-large cross-section oil storage cavern, which solves the above-mentioned problems.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern, comprising a device body and further comprising:

[0009] The blasting analysis system is integrated into the controller embedded in the upper cover of the device body and includes:

[0010] The blasting environment analysis module builds an environmental state model based on the blasting vibration velocity peak and dust concentration and outputs the environmental state coefficient;

[0011] The blasting damage analysis module builds a blasting damage model and outputs a blasting damage coefficient based on the deepest borehole displacement and borehole inclination measured by a multi-point displacement meter, as well as the ultrasonic sound wave drop amplitude of the borehole before and after blasting.

[0012] The blasting damage prediction and analysis module builds a blasting damage model based on the current environmental state coefficient, the single-point charge density under the blasting damage coefficient, and the blasting point spacing to output the blasting damage coefficient;

[0013] The charge analysis module builds a charge analysis model based on the blasting damage coefficient and the current single-point charge to output the target single-point charge.

[0014] On the basis of the above technical solutions, the present invention also provides the following optional technical solutions:

[0015] Further technical solution: The charge analysis model is expressed as:

[0016]

[0017] in, represents the target charge amount, Indicates the current charge amount. represents the blasting damage prediction coefficient, Indicates the safety value of the blasting damage coefficient, represents the blasting damage coefficient, Indicates the warning value of blasting damage coefficient, Indicates excessive bandwidth.

[0018] Further technical solution: The steps of the blasting damage prediction and analysis module are as follows:

[0019] The blasting point distance index is obtained by ratioing the reference hole distance with the current blasting point distance, and the charge density index is obtained by ratioing the current single point charge density with the maximum allowable charge density;

[0020] Based on the current environmental state coefficient, the charge density index under the blasting damage coefficient, and the blasting point distance index, a blasting damage model is constructed to output the blasting damage prediction coefficient;

[0021] The blasting damage model is expressed as:

[0022]

[0023] in, represents the blasting damage prediction coefficient, represents the environmental state coefficient, represents the blasting damage coefficient, Indicates charge density index, represents the blasting point distance index, represents the sensitivity coefficient, Represents the hole spacing sensitivity index.

[0024] Further technical solution: The steps of the blasting environment analysis module are:

[0025] The vibration index is obtained by ratioing the obtained blasting vibration velocity peak value with the maximum vibration safety value;

[0026] The dust concentration index is obtained by performing a ratio process on the obtained dust concentration and the maximum dust concentration value;

[0027] Build an environmental state model based on the vibration index and dust concentration index to output the environmental state coefficient;

[0028] The environmental state model is expressed as:

[0029]

[0030] in, represents the environmental state coefficient, represents the vibration index, Represents the dust concentration index, represents the weight coefficient and .

[0031] Further technical solution: The steps of the blasting damage analysis module are:

[0032] The displacement at the deepest point of the borehole, the inclination angle, and the ultrasonic wave drop amplitude are respectively compared with the corresponding maximum values ​​to obtain the displacement index, angular deviation index, and wave velocity drop amplitude index;

[0033] Based on the displacement index (the most direct indicator for quantifying blast damage), the angular deviation index (reflecting shear damage) and the wave velocity drop index (reflecting rock mass integrity), a blast damage model is constructed to output the blast damage coefficient.

[0034] The blasting damage model is expressed as:

[0035]

[0036] in, represents the blasting damage coefficient, represents the displacement index, Indicates the angle deviation index, represents the wave velocity reduction index, represents the damage sensitivity index, represents the weight coefficient and .

[0037] Further technical solutions include:

[0038] Multi-point displacement meter, which can be detachably installed in the device body and is used to quantify the deformation of the surrounding rock caused by blasting;

[0039] Inclinometer, which can be embedded in the device body and used to measure the inclination of the drill hole after blasting;

[0040] The acoustic wave sensor can be embedded in the device body to obtain the ultrasonic sound wave drop in the borehole before and after blasting.

[0041] Further technical solution: The device body is provided with a slot A for placing a multi-point displacement meter, a slot B for placing an inclinometer, and a slot C for placing an acoustic wave sensor.

[0042] The present invention provides a detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern, which has the following beneficial effects compared with the prior art:

[0043] 1. This invention achieves comprehensive quantification of the surrounding rock state through the collaboration of environmental state models (blasting vibration, dust) and damage models (displacement, tilt, and acoustic amplitude drop). Simultaneously, based on the environmental state coefficient and damage coefficient, combined with charge density and hole spacing index, a nonlinear damage prediction model is constructed, significantly improving prediction accuracy. Finally, through the charge quantity analysis model, the safe charge quantity is output in real time based on the damage prediction coefficient and damage warning threshold, thereby reducing the risk of surrounding rock damage. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 It is a schematic diagram of the flow of the blasting analysis system of the present invention.

[0045] Figure 2 Schematic diagram of the three-dimensional structure of the device body of the present invention.

[0046] Notes on the accompanying figures: 1. Device body; 101. Slot A; 102. Slot B; 103. Slot C; 2. Controller. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0048] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0049] See also Figures 1 to 2 , provided in one embodiment of the present invention, is a detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern, comprising a device body 1, and further comprising:

[0050] The multi-point displacement meter can be detachably installed in the slot A101 provided in the device body 1 and is used to quantify the deformation of the surrounding rock caused by blasting;

[0051] The inclinometer can be embedded in the slot B102 provided in the device body 1 and is used to measure the inclination of the drill hole after blasting;

[0052] The acoustic wave sensor can be embedded in the slot C103 provided in the device body 1 and is used to obtain the amplitude reduction of ultrasonic sound waves in the borehole before and after blasting;

[0053] The blasting analysis system is integrated into the controller 2 embedded in the upper cover of the device body 1 and includes:

[0054] The blasting environment analysis module builds an environmental state model based on the blasting vibration velocity peak and dust concentration and outputs the environmental state coefficient;

[0055] The blasting damage analysis module builds a blasting damage model and outputs a blasting damage coefficient based on the deepest borehole displacement and borehole inclination measured by a multi-point displacement meter, as well as the ultrasonic sound wave drop amplitude of the borehole before and after blasting.

[0056] The blasting damage prediction and analysis module builds a blasting damage model based on the current environmental state coefficient, the single-point charge density under the blasting damage coefficient, and the blasting point spacing to output the blasting damage coefficient;

[0057] The charge analysis module builds a charge analysis model based on the blasting damage coefficient and the current single-point charge to output the target single-point charge.

[0058] Specifically, the blasting environment analysis module collects vibration velocity and dust concentration data in real time, normalizes them with preset safety thresholds, and generates a comprehensive environmental state coefficient. The blasting damage analysis module simultaneously acquires measurement data from multi-point displacement meters, inclinometers, and acoustic wave sensors, and generates a blasting damage coefficient by quantifying the coordinated changes in displacement, tilt angle, and acoustic wave drop amplitude. The blasting damage prediction analysis module inputs the environmental state coefficient and blasting damage coefficient into a nonlinear model and calculates the blasting damage prediction coefficient based on the current charge density and blasting point spacing parameters. The charge analysis module uses the model to output the optimized target charge in real time based on the dynamic relationship between the damage prediction coefficient and the current charge.

[0059] Compared with existing technologies, traditional methods use a single parameter threshold warning mechanism and are unable to handle the coupling of multiple factors. This solution achieves a multi-dimensional assessment of blasting risk by establishing a dynamic correlation model between the environmental state coefficient and the blasting damage coefficient. In existing technologies, the adjustment of charge quantity relies on manual experience and judgment. This solution forms an automatic feedback adjustment mechanism by constructing a mathematical relationship between the damage prediction coefficient and the charge quantity. Traditional detection devices operate independently in each monitoring module. This solution integrates a multi-module analysis system through a controller, realizing real-time data interaction and collaborative processing.

[0060] Through the above technical solution, this application solves the challenge of integrating multidimensional environmental parameters and avoids the risk of misjudging a single parameter through an environmental state model. This overcomes the one-sided nature of traditional damage assessment and enables comprehensive monitoring of the surrounding rock structure through a multi-index fusion model. It eliminates the lag in charge adjustment and ensures dynamic optimization of blasting parameters through a real-time feedback mechanism. Ultimately, this achieves precise and intelligent control of surrounding rock stability, effectively reducing the incidence of over-excavation and under-excavation accidents.

[0061] Preferably, the steps of the blasting environment analysis module are:

[0062] The vibration index is obtained by ratioing the obtained blasting vibration velocity peak value with the maximum vibration safety value;

[0063] The dust concentration index is obtained by performing a ratio process on the obtained dust concentration and the maximum dust concentration value;

[0064] Build an environmental state model based on the vibration index and dust concentration index to output the environmental state coefficient;

[0065] The environmental state model is expressed as:

[0066]

[0067] in, represents the environmental state coefficient, represents the vibration index, Represents the dust concentration index, represents the weight coefficient and .

[0068] The peak value of blasting vibration velocity refers to the maximum vibration velocity value monitored during the blasting operation. Specifically, a vibration sensor can be used to collect data in real time, and the impact of vibration on the surrounding rock can be quantified by performing a ratio calculation with the preset maximum vibration safety value.

[0069] Among them, the maximum dust concentration value refers to the preset dust concentration safety threshold. Specifically, a dust concentration sensor can be used to measure the dust concentration in the blasting area, and the pollution risk of blasting dust to the working environment can be reflected by calculating the ratio with the safety threshold.

[0070] Among them, the weight coefficient refers to the contribution ratio of the vibration index and the dust concentration index in the environmental state model. It can be specifically adjusted using empirical values ​​or dynamically based on historical data, and the balanced influence of different environmental parameters can be ensured through normalization constraints.

[0071] Specifically, the vibration index is generated by dividing the measured peak vibration velocity by the safety threshold. The dust concentration index uses the same ratio processing method. The environmental state coefficient is calculated using a linear combination formula, where weighting factors can be dynamically assigned based on different operating conditions. For example, in dust-sensitive areas, the dust concentration weight can be set to 0.6 and the vibration weight to 0.4. Through normalization constraints, evaluation distortion caused by abnormal fluctuations in a single parameter is avoided, thereby achieving comprehensive quantification of vibration and dust pollution.

[0072] Compared with existing technologies, traditional methods typically use only a single vibration velocity threshold to determine environmental risks, lack dynamic assessment of dust pollution, and fail to quantify the synergistic effects between parameters. This solution combines the dust concentration index with the vibration index to construct a linear weighted model that simultaneously reflects the dual impact of blasting operations on surrounding rock stability and the operating environment. The contribution of different parameters is dynamically adjusted using weight coefficients.

[0073] Through the above technical solution, this application achieves a fusion analysis of multi-dimensional environmental parameters for blasting vibration and dust pollution, resolving the problem of traditional methods relying on static thresholds and being unable to dynamically quantify environmental synergy risks. By introducing normalized weight coefficients, this ensures the balanced impact of different environmental parameters on the comprehensive assessment results, avoiding the risk of misjudgment caused by a single parameter dominating the assessment results, thereby providing accurate environmental status input for subsequent charge optimization.

[0074] Preferably, the steps of the blasting damage analysis module are:

[0075] The displacement at the deepest point of the borehole, the inclination angle, and the ultrasonic wave drop amplitude are respectively compared with the corresponding maximum values ​​to obtain the displacement index, angular deviation index, and wave velocity drop amplitude index;

[0076] Based on the displacement index (the most direct indicator for quantifying blast damage), the angular deviation index (reflecting shear damage) and the wave velocity drop index (reflecting rock mass integrity), a blast damage model is constructed to output the blast damage coefficient.

[0077] The blasting damage model is expressed as:

[0078]

[0079] in, represents the blasting damage coefficient, represents the displacement index, Indicates the angle deviation index, represents the wave velocity reduction index, represents the damage sensitivity index, represents the weight coefficient and .

[0080] Among them, the displacement at the deepest part of the borehole refers to the maximum deformation of the surrounding rock caused by blasting. It can be measured by a multi-point displacement meter. The displacement index is generated by ratio processing to quantify the degree of compression or tension deformation. The tilt angle refers to the angle at which the borehole axis deviates from the vertical direction. It can be measured by an inclinometer. The nonlinear effect of shear failure is highlighted by square operation. The ultrasonic sound wave drop refers to the reduction in the sound wave propagation speed before and after blasting. It can be measured by an acoustic wave sensor and combined with the damage sensitivity index. Adjust the sensitivity of wave velocity changes to rock mass integrity. The displacement index refers to the ratio of displacement to the maximum allowable displacement, which directly reflects the degree of deformation of the surrounding rock. The angular deviation index refers to the ratio of the inclination angle to the maximum allowable angle, which is used to characterize the shear failure risk. The wave velocity drop index refers to the ratio of the ultrasonic wave drop to the maximum allowable drop, which is used to evaluate the expansion state of cracks inside the rock mass. Weight coefficient It is used to balance the contribution of different damage mechanisms and ensure the physical significance and computational stability of the model.

[0081] Specifically, after obtaining the deepest displacement of the borehole through a multi-point displacement meter, it is ratioed with the preset maximum allowable displacement to generate a displacement index, which directly reflects the degree of compression or tensile deformation of the surrounding rock. The inclination angle measured by the inclinometer is ratioed with the maximum allowable angle to generate an angular offset index, and the nonlinear effect of shear failure is enhanced by square operation. The acoustic wave sensor collects ultrasonic sound wave velocity data before and after blasting, calculates the drop, and then ratios it with the maximum allowable drop to generate a wave velocity drop index, which is adjusted in combination with the damage sensitivity index α to adjust its sensitivity to rock integrity. The displacement index, the square term of the angular offset index, and the power function term of the wave velocity drop index are weighted and superimposed to comprehensively reflect the compression deformation, shear failure and crack expansion state of the surrounding rock. Weight coefficient Normalization constraints are used to ensure that the contributions of different damage mechanisms are reasonably distributed, and the dynamic blasting damage coefficient is finally output.

[0082] Compared to existing technologies, traditional methods rely solely on single indicators to assess surrounding rock damage, such as displacement or acoustic wave velocity changes. These methods fail to comprehensively reflect the synergistic effects of compression deformation, shear failure, and crack propagation. Existing technologies do not include nonlinear processing of tilt angles and lack dynamic adjustment of sensitivity to wave velocity reduction, leading to biased damage assessments.

[0083] Through the above technical solution, this application integrates three parameters: borehole displacement, inclination angle, and acoustic wave drop amplitude. Through nonlinear calculations and dynamic weight distribution, it accurately quantifies the extent of surrounding rock blasting damage. This solution overcomes the limitations of traditional single-metric evaluation methods, avoids assessment errors caused by ignoring the nonlinear effects of shear failure and differences in wave velocity sensitivity, and provides a reliable damage coefficient input for subsequent charge optimization.

[0084] Preferably, the steps of the blasting damage prediction and analysis module are:

[0085] The blasting point distance index is obtained by ratioing the reference hole distance with the current blasting point distance, and the charge density index is obtained by ratioing the current single point charge density with the maximum allowable charge density;

[0086] Based on the current environmental state coefficient, the charge density index under the blasting damage coefficient, and the blasting point distance index, a blasting damage model is constructed to output the blasting damage prediction coefficient;

[0087] The blasting damage model is expressed as:

[0088]

[0089] in, represents the blasting damage prediction coefficient, represents the environmental state coefficient, represents the blasting damage coefficient, Indicates charge density index, represents the blasting point distance index, represents the sensitivity coefficient (used to control the intensity of the synergistic effect of environment and damage), Represents the hole spacing sensitivity index.

[0090] The blasting point spacing index is the ratio of the baseline hole spacing to the current blasting point spacing. This can be achieved by measuring the actual hole spacing using a distance sensor and dividing it by a preset baseline value. It reflects the degree to which the actual hole spacing deviates from the baseline. The charge density index is the ratio of the current single-point charge density to the maximum allowable charge density. This can be achieved by collecting charge density data using a density sensor. It is used to dynamically assess whether the charge density exceeds the safe range. The environmental state coefficient is a weighted parameter that combines vibration velocity and dust concentration, representing the overall risk level of the blasting environment. The blasting damage coefficient is a nonlinear parameter that combines displacement, inclination, and acoustic wave drop amplitude, quantifying the degree of damage to the surrounding rock structure. The sensitivity coefficient is a modulating factor that adjusts for the combined effects of environmental and damage parameters. It can be determined through experimental calibration or machine learning optimization and is used to control the steepness of the nonlinear response curve. The hole spacing sensitivity index is the weight of the impact of blasting point spacing on damage prediction. Different power parameters can be set based on rock mass types to reflect the sensitivity of different geological conditions to changes in hole spacing.

[0091] Specifically, by collecting the ratio of the current blasting point spacing to the benchmark hole spacing in real time, the degree to which the actual hole spacing deviates from the design value can be dynamically monitored to avoid uneven energy distribution due to excessive spacing or stress concentration caused by too small spacing. The charge density index can promptly identify the risk of rock crushing caused by overly dense charges by monitoring the proportional relationship between the charge density and the maximum allowable value in real time. The environmental state coefficient and the blasting damage coefficient are coupled and calculated in the form of an exponential function, which strengthens the nonlinear superposition effect of the two in high-value areas, and the sensitivity coefficient adjusts the response rate of this coupling effect. The power function relationship between the blasting point spacing index and the hole spacing sensitivity index can differentially adjust the influence weight of hole spacing changes on damage prediction according to rock mass characteristics. For example, a higher γ value can be set in weak rock formations to enhance hole spacing sensitivity. The blasting damage model constructed in this way realizes multi-dimensional dynamic matching of environmental parameters, damage state, charging conditions and geometric layout.

[0092] Compared with existing technologies, traditional methods only use fixed hole spacing thresholds and static charge density limits, which cannot reflect in real time the impact of dynamic changes in hole spacing deviation and charge density on surrounding rock stability during blasting operations. Existing technologies use a linear superposition method to calculate the damage coefficient of environmental parameters and damage parameters, making it difficult to accurately characterize the nonlinear synergistic effect of the two under high-risk conditions. This solution effectively captures the interaction between environmental risk and structural damage by introducing a coupling calculation term in the form of an exponential function. Combined with the differentiated setting of the hole spacing sensitivity index, it significantly improves prediction accuracy under different geological conditions.

[0093] Through the above technical solution, this application solves the problem that traditional blasting detection methods cannot dynamically predict the blasting damage coefficient, resulting in a lag in the adjustment of the charge quantity, and realizes the real-time calculation of the blasting damage coefficient and the dynamic early warning of the surrounding rock stability risk. By dynamically quantifying the relative state of the blasting point spacing and the charge density, combined with the nonlinear coupling calculation of the environment and damage parameters, it is possible to identify the hole spacing arrangement deviation and the risk of over-dense charging in real time during the charging operation, and provide accurate damage prediction coefficient input for the dynamic adjustment of the charge quantity. The parametric design based on the sensitivity coefficient and the hole spacing sensitivity index can adapt to the customized needs of the blasting damage model for different rock types and geological conditions, and effectively avoid the occurrence of over-excavation or under-excavation accidents of the surrounding rock.

[0094] Preferably, the charge analysis model is expressed as:

[0095]

[0096] in, represents the target charge amount, Indicates the current charge amount. represents the blasting damage prediction coefficient, Indicates the safety value of the blasting damage coefficient, represents the blasting damage coefficient, Indicates the warning value of blasting damage coefficient, Indicates excessive bandwidth (the upper and lower floating values ​​of the blasting damage coefficient warning value).

[0097] Specifically, the model uses the exponential function term Achieve a dynamic association between the damage prediction coefficient and the safety value. When the blasting damage prediction coefficient exceeds the safety value, the exponential term decreases, thereby reducing the target charge amount and preventing over-excavation of the surrounding rock. Hyperbolic tangent function term It is used to dynamically respond to the damage state. When the blasting damage coefficient approaches or exceeds the warning value, the function value decays rapidly, forcing the charge to be reduced to avoid under-excavation. The excessive bandwidth parameter is the core safety buffer of the charge control model and is closely related to the warning value. Together they constitute a "soft threshold" that replaces the traditional hard threshold alarm, achieving dynamic and progressive optimization of the charge amount. Furthermore, they can control the response speed of charge amount adjustment by adjusting the slope of the hyperbolic tangent curve. For example, when the value is small, the charge amount adjustment is more sensitive to changes in the damage coefficient.

[0098] Compared to existing technologies, traditional methods rely on fixed safety thresholds to adjust charge weight, failing to differentiate between the synergistic effects of the damage prediction coefficient and the damage coefficient. This solution, however, integrates the damage prediction coefficient and the damage warning mechanism through a combination of nonlinear functions, dynamically generating a charge weight correction factor based on real-time blasting data. While existing technologies typically use linear scaling for charge weight adjustment, this model achieves nonlinear regulation of charge weight as risk level changes through the combined action of exponential and hyperbolic tangent functions.

[0099] Through the above-mentioned technical solution, this application solves the problem of over-excavation or under-excavation of surrounding rock caused by the lag in traditional charge adjustment. During the blasting construction process, when the blasting damage coefficient is monitored to be close to the warning value, the model automatically reduces the target charge to avoid cumulative damage to the rock mass. When the blasting damage prediction coefficient exceeds the safety threshold, the exponential decay mechanism immediately takes effect to suppress excessive charging. By dynamically balancing the dual constraints of damage prediction and damage warning, this model ensures that the charge is always within the safe construction range, thereby maintaining the stability of the surrounding rock structure.

[0100] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0101] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern, comprising a device body, characterized in that: Also includes: The blasting analysis system is integrated into the controller embedded in the upper cover of the device body and includes: The blasting environment analysis module builds an environmental state model based on the blasting vibration velocity peak and dust concentration and outputs the environmental state coefficient; The blasting damage analysis module builds a blasting damage model and outputs a blasting damage coefficient based on the deepest borehole displacement and borehole inclination measured by a multi-point displacement meter, as well as the ultrasonic sound wave drop amplitude of the borehole before and after blasting. The blasting damage prediction and analysis module builds a blasting damage model based on the current environmental state coefficient, the single-point charge density under the blasting damage coefficient, and the blasting point spacing to output the blasting damage coefficient; The charge analysis module builds a charge analysis model based on the blasting damage coefficient and the current single-point charge to output the target single-point charge.

2. The detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern according to claim 1 is characterized in that: The steps of the blasting damage prediction and analysis module are as follows: The blasting point distance index is obtained by ratioing the reference hole distance with the current blasting point distance, and the charge density index is obtained by ratioing the current single point charge density with the maximum allowable charge density; Based on the current environmental state coefficient, the charge density index under the blasting damage coefficient, and the blasting point distance index, a blasting damage model is constructed to output the blasting damage prediction coefficient; The blasting damage model is expressed as: in, represents the blasting damage prediction coefficient, represents the environmental state coefficient, represents the blasting damage coefficient, Indicates charge density index, represents the blasting point distance index, represents the sensitivity coefficient used to control the intensity of the synergistic effect between environment and damage, Represents the hole spacing sensitivity index.

3. The detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern according to claim 2 is characterized in that: The steps of the blasting environment analysis module are: The vibration index is obtained by ratioing the obtained blasting vibration velocity peak value with the maximum vibration safety value; The dust concentration index is obtained by performing a ratio process on the obtained dust concentration and the maximum dust concentration value; Build an environmental state model based on the vibration index and dust concentration index to output the environmental state coefficient; The environmental state model is expressed as: in, represents the environmental state coefficient, represents the vibration index, Represents the dust concentration index, represents the weight coefficient and .

4. The detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern according to claim 2 is characterized in that: The steps of the blasting damage analysis module are: The displacement at the deepest point of the borehole, the inclination angle, and the ultrasonic wave drop amplitude are respectively compared with the corresponding maximum values ​​to obtain the displacement index, angular deviation index, and wave velocity drop amplitude index; According to the displacement index, angular deviation index and wave velocity reduction index, a blasting damage model is constructed to output the blasting damage coefficient; The blasting damage model is expressed as: in, represents the blasting damage coefficient, represents the displacement index, Indicates the angle deviation index, represents the wave velocity reduction index, represents the damage sensitivity index, represents the weight coefficient and .

5. The detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern according to claim 1 is characterized in that: Also includes: Multi-point displacement meter, which can be detachably installed in the device body and is used to quantify the deformation of the surrounding rock caused by blasting; Inclinometer, which can be embedded in the device body and used to measure the inclination of the drill hole after blasting; The acoustic wave sensor can be embedded in the device body to obtain the ultrasonic sound wave drop in the borehole before and after blasting.

6. The detection device for blasting surrounding rock of an ultra-large cross-section oil storage cavern according to claim 5 is characterized in that: The device body is provided with a slot A for placing a multi-point displacement meter, a slot B for placing an inclinometer, and a slot C for placing an acoustic wave sensor.

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