Static blasting process optimization method and system based on digital twinning

Abstract

The invention provides a static blasting process optimization method and system based on digital twinning, and relates to the technical field of blasting process optimizing.The method comprises the steps that historical static blasting data are obtained, data before blasting and data during blasting are analyzed to obtain disturbance fluctuation characteristics and disturbance stress characteristics, and the disturbance fluctuation characteristics and the disturbance stress characteristics are analyzed; analyzing the disturbance fluctuation characteristics and the disturbance stress characteristics to obtain a disturbance fluctuation interference coefficient and a disturbance stress interference coefficient, and analyzing the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient to obtain a rock burst occurrence coefficient; establishing a rock burst prediction model according to the pre-burst data and the rock burst occurrence coefficient, and calculating the rock burst occurrence coefficient by using the rock burst prediction model; the maximum rock burst generation coefficient is used as an optimization target, the genetic algorithm is used for optimizing the blasting setting data, and the group with the minimum rock burst generation coefficient is selected as the optimal blasting setting data; and the digital twin platform previews the blasting effect.

Description

Technical Field

[0001] The present invention relates to the technical field of blasting process optimization, and specifically to a static blasting process optimization method and system based on digital twin. Background Art

[0002] Static blasting is a safe and efficient rock crushing technology, which is widely used in mining, building demolition and engineering construction. However, the dynamic disturbance during static blasting may cause instability of the surrounding rock mass or even rock burst, leading to safety hazards and economic losses. In the prior art, the research on the fluctuation characteristics and stress characteristics of the disturbance caused by blasting is relatively scattered, and it is impossible to effectively quantify the impact of these disturbances on the stability of the rock mass. At the same time, traditional blasting process optimization methods mostly rely on empirical design, lack of comprehensive consideration of the complex mechanical properties of the rock mass in the blasting area, and the optimization results are not accurate enough to meet the requirements of efficiency and safety. Therefore, how to effectively analyze the impact of dynamic disturbances on the rock mass during blasting and realize blasting process optimization based on data-driven has become a key issue that needs to be solved in the current technical field.

[0003] In the prior art, the publication number CN118070670A discloses an intelligent optimization system for smooth blasting of tunnel drilling and blasting based on digital twins, specifically to obtain surrounding rock parameters, tunnel parameters, hole layout parameters and charge parameters; parameter optimization module, import parameters, and optimize construction process parameters; based on the optimized construction process parameters, construct explosive state model, rock state model and air and water medium state model, and preview and verify the blasting effect of each blasting scheme; collect images of gravel and rock wall after blasting, detect the gravel particle size and obtain the degree of over-excavation and under-excavation and half-hole retention as reference indicators for blasting quality evaluation, and evaluate the blasting effect. However, the prior art still has certain limitations, and its focus is mostly on the evaluation of blasting effects, while the potential impact of blasting on the surrounding environment is not fully considered. This neglect may lead to safety hazards and engineering losses, bringing risks to practical applications.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The purpose of the present invention is to provide a static blasting process optimization method and system based on digital twin to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions: A static blasting process optimization method based on digital twin, the specific steps include: Step 1: Acquire historical static blasting data, wherein the static blasting data includes pre-blasting data and blasting data; the pre-blasting data includes lithology data and blasting setting data; Step 2: Analyze the data before blasting and during blasting to obtain the disturbance fluctuation characteristics and disturbance stress characteristics, analyze the disturbance fluctuation characteristics to obtain the disturbance fluctuation interference coefficient, analyze the disturbance stress characteristics to obtain the disturbance stress interference coefficient, and analyze the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient to obtain the blasting rock burst generation coefficient; Step 3: Use the pre-blasting data as the training set and the blasting rock burst generation coefficient as the label to build a blasting rock burst prediction model, and use the training set to train the blasting rock burst prediction model; Step 4: Obtain the lithology data of the area to be blasted, and select a set of historical blasting setting data with the closest fracture toughness as the initial solution. Take the minimum blasting rock burst generation coefficient as the optimization goal, use the genetic algorithm to optimize the blasting setting data, and select the set with the smallest blasting rock burst generation coefficient as the optimal blasting setting data; Step 5: Build a blasting rock model on the digital twin platform based on the lithology data of the area to be blasted and the blasting setting data to preview the blasting effect.

[0007] Furthermore, the lithology data include the density, shear modulus, elastic modulus, Poisson's ratio, porosity and fracture toughness of the rock mass, and the static stress of the surrounding rock mass; the blasting setting data include the borehole diameter, hole depth, hole spacing, the mixing ratio of the expansion agent and water, and the expansion agent filling rate; the blasting data include the dynamic disturbance wave of the surrounding rock mass and the dynamic stress of the surrounding rock mass; The density, shear modulus, elastic modulus, Poisson's ratio, porosity and fracture toughness are measured by sending rock samples to a laboratory, and the static blasting area is expanded to one-third and one-fourth of the original area. The non-static blasting area in the expanded area is defined as the surrounding area, and the rock mass in the surrounding area is the surrounding rock mass; the static stress and dynamic stress of the surrounding rock mass are obtained through stress sensors evenly arranged in the surrounding area after blasting; the dynamic disturbance wave of the surrounding rock mass is obtained through a seismic detector; the dynamic disturbance wave and dynamic stress of the surrounding rock mass are collected until the static blasting is completed or a rock burst occurs.

[0008] Furthermore, the disturbance fluctuation characteristics include rise and decay time difference, power spectrum density and spectrum entropy, and the disturbance stress characteristics include average dynamic and static stress difference and average dynamic stress variance; The specific logic for calculating the rise-decay time difference is: respectively count the time from the trough to the peak or from the trough to the peak of each section of the dynamic disturbance wave of the surrounding rock mass, and record it as the rise-decay time; calculate the distribution variance of the rise-decay time, and record the distribution variance of the rise-decay time as the rise-decay time difference. The specific formula is: ; in, is the rise and decay time difference, For the Rise and fall time, is the total rise and decay time, is the index of rise and decay time; The specific logic for obtaining the power spectrum density and spectrum entropy is: Fourier transform the surrounding rock mass dynamic disturbance wave to transform the amplitude of the surrounding rock mass dynamic disturbance wave into the form of the sum of a series of complex numbers, and obtain the spectrum of the surrounding rock mass dynamic disturbance wave. The spectrum of the surrounding rock mass dynamic disturbance wave is analyzed to obtain the power spectrum density and spectrum entropy. The specific formula is: ; ; ; in, is the power spectrum density of the dynamic disturbance wave of the surrounding rock mass, is the spectrum entropy of the dynamic disturbance wave of the surrounding rock mass, is the spectrum of the dynamic disturbance wave of the surrounding rock mass, is the spectrum energy; is the total number of frequencies, is the frequency index; The specific logic for obtaining the disturbance stress characteristics is as follows: calculate the average value of the dynamic stress collected by each stress sensor during the static blasting process, first subtract the average value of the dynamic stress from the static stress, and then take the absolute value to obtain the average dynamic and static stress difference; first take the average value of the dynamic stress collected by each stress sensor during the static blasting process, and then calculate the variance of the average dynamic stress of all stress sensors to obtain the average dynamic stress variance; the specific formula is: ; ; ; in, is the average dynamic and static stress difference, is the average dynamic stress variance, For the The average dynamic stress of the stress sensors, For the The stress sensor The dynamic stress collected is For the The static stress of each sensor, is the total number of stress sensors, is the number of collections, is the stress sensor index, The index of the number of collections.

[0009] Furthermore, the specific formula for generating the disturbance fluctuation interference coefficient is: ; in, is the disturbance fluctuation interference coefficient, is the rise and decay time difference, is the power spectrum density of the dynamic disturbance wave of the surrounding rock mass, is the spectrum entropy of the dynamic disturbance wave of the surrounding rock mass; The specific formula for generating the disturbance stress interference coefficient is: ; in, is the disturbance stress interference coefficient, is the average dynamic and static stress difference, is the mean dynamic stress variance.

[0010] Furthermore, the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient are analyzed to obtain the blasting rock burst generation coefficient; the specific formula for generating the blasting rock burst generation coefficient is: ; in, is the blasting rock burst generation coefficient, is the disturbance fluctuation interference coefficient, is the disturbance stress interference coefficient.

[0011] The present invention further provides a static blasting process optimization system based on digital twins, and the system is used to implement the static blasting process optimization method based on digital twins, which specifically includes: A data acquisition module, used to acquire historical static blasting data, wherein the static blasting data includes pre-blasting data and blasting data; the pre-blasting data includes lithology data and blasting setting data; Comprehensive analysis module, used to analyze the data before blasting and during blasting to obtain disturbance fluctuation characteristics and disturbance stress characteristics, analyze the disturbance fluctuation characteristics to obtain the disturbance fluctuation interference coefficient, analyze the disturbance stress characteristics to obtain the disturbance stress interference coefficient, and analyze the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient to obtain the blasting rock burst generation coefficient; A model building module is used to build a blasting rock burst prediction model using pre-blasting data as a training set and blasting rock burst generation coefficients as labels, and to train the blasting rock burst prediction model using the training set; The data optimization module is used to obtain the lithology data of the area to be blasted, and select a group of historical blasting setting data with the closest fracture toughness as the initial solution. The blasting setting data is optimized using a genetic algorithm with the minimum blasting rock burst generation coefficient as the optimization goal, and the group with the minimum blasting rock burst generation coefficient is selected as the optimal blasting setting data; The digital twin module is used to build a blasting rock model on the digital twin platform based on the lithology data of the area to be blasted and the blasting setting data, and to preview the blasting effect.

[0012] Compared with the prior art, the present invention has the following beneficial effects: The present invention optimizes the design of blasting parameters through the precise quantification and prediction of disturbance fluctuations and disturbance stress characteristics throughout the blasting process. It not only significantly reduces the risk of rockburst while improving blasting efficiency, but also fully considers the potential impact of blasting on the surrounding environment. Through preview and simulation verification, it achieves dual guarantees of safety and efficiency, thereby effectively avoiding safety hazards and engineering losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the overall method flow of the present invention.

[0014] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0015] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0016] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0017] Example: See also Figure 1 , the present invention provides a technical solution: A static blasting process optimization method based on digital twin, the specific steps include: Step 1: Acquire historical static blasting data, wherein the static blasting data includes pre-blasting data and blasting data; the pre-blasting data includes lithology data and blasting setting data; Furthermore, the lithology data include the density, shear modulus, elastic modulus, Poisson's ratio, porosity and fracture toughness of the rock mass, and the static stress of the surrounding rock mass; the blasting setting data include the borehole diameter, hole depth, hole spacing, the mixing ratio of the expansion agent and water, and the expansion agent filling rate; the blasting data include the dynamic disturbance wave of the surrounding rock mass and the dynamic stress of the surrounding rock mass; The density, shear modulus, elastic modulus, Poisson's ratio, porosity and fracture toughness are measured by sending rock samples to the laboratory, and the static blasting area is expanded to one-third and one-fourth of the original area. The non-static blasting area in the expanded area is defined as the surrounding area, and the rock mass in the surrounding area is the surrounding rock mass; the static stress and dynamic stress of the surrounding rock mass are obtained by stress sensors evenly arranged in the surrounding area; the dynamic disturbance wave of the surrounding rock mass is obtained by a seismic detector; the dynamic disturbance wave and dynamic stress of the surrounding rock mass are collected until the static blasting is completed or a rock burst occurs.

[0018] Step 2: Analyze the data before blasting and during blasting to obtain the disturbance fluctuation characteristics and disturbance stress characteristics, analyze the disturbance fluctuation characteristics to obtain the disturbance fluctuation interference coefficient, analyze the disturbance stress characteristics to obtain the disturbance stress interference coefficient, and analyze the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient to obtain the blasting rock burst generation coefficient; The disturbance fluctuation characteristics include rise and decay time difference, power spectrum density and spectrum entropy, and the disturbance stress characteristics include average dynamic and static stress difference and average dynamic stress variance; The specific logic for calculating the rise-decay time difference is: respectively count the time from the trough to the peak or from the trough to the peak of each section of the dynamic disturbance wave of the surrounding rock mass, and record it as the rise-decay time; calculate the distribution variance of the rise-decay time, and record the distribution variance of the rise-decay time as the rise-decay time difference. The specific formula is: ; in, is the rise and decay time difference, For the Rise and fall time, is the total rise and decay time, It is the index of rise-decay time; the rise-decay time difference reflects the distribution consistency of the rise-decay time in the dynamic disturbance wave of the surrounding rock mass; the smaller its value is, the stronger the distribution consistency of the rise-decay time in the dynamic disturbance wave of the surrounding rock mass is.

[0019] The specific logic for obtaining the power spectrum density and spectrum entropy is: Fourier transform the surrounding rock mass dynamic disturbance wave to transform the amplitude of the surrounding rock mass dynamic disturbance wave into the form of the sum of a series of complex numbers, and obtain the spectrum of the surrounding rock mass dynamic disturbance wave. The spectrum of the surrounding rock mass dynamic disturbance wave is analyzed to obtain the power spectrum density and spectrum entropy. The specific formula is: ; ; ; in, is the power spectrum density of the dynamic disturbance wave of the surrounding rock mass, is the spectrum entropy of the dynamic disturbance wave of the surrounding rock mass, is the spectrum of the dynamic disturbance wave of the surrounding rock mass, is the spectrum energy; is the total number of frequencies, It is a frequency index. The power spectrum density of the dynamic disturbance wave of the surrounding rock mass reflects the energy concentration of the dynamic disturbance wave of the surrounding rock mass. The larger the value, the more concentrated the energy of the dynamic disturbance wave of the surrounding rock mass, and it is more likely to cause rock bursts. The spectrum entropy indicates the uniformity of energy distribution in the spectrum, which reflects the complexity or disorder of the dynamic disturbance wave of the surrounding rock mass in the frequency domain. The larger the value, the more uniform the energy is distributed in the spectrum, and the signal contains more frequency components, indicating that the disturbance is more complex and random, and it is more likely to cause rock bursts. The specific logic for obtaining the disturbance stress characteristics is as follows: calculate the average value of the dynamic stress collected by each stress sensor during the static blasting process, first subtract the average value of the dynamic stress from the static stress, and then take the absolute value to obtain the average dynamic and static stress difference; first take the average value of the dynamic stress collected by each stress sensor during the static blasting process, and then calculate the variance of the average dynamic stress of all stress sensors to obtain the average dynamic stress variance; the specific formula is: ; ; ; in, is the average dynamic and static stress difference, is the average dynamic stress variance, For the The average dynamic stress of the stress sensors, For the The stress sensor The dynamic stress collected is For the The static stress of each sensor, is the total number of stress sensors, is the number of collections, is the stress sensor index, The index of the number of collections.

[0020] The average dynamic and static stress difference measures the overall difference between the dynamic stress generated during the blasting and the static stress before the blasting. The comparison of static stress and dynamic stress can reveal the degree of disturbance of the rock mass during the blasting process. The larger the value, the greater the overall difference, the greater the degree of disturbance, and the more likely it is to cause rock burst; the average dynamic stress variance reflects the degree of fluctuation of the average dynamic stress of each sensor relative to the overall average. If It is larger, indicating that the dynamic stress distribution of different sensors is uneven, which means that the disturbance intensity of blasting on different positions of the rock mass is quite different, which is more likely to cause rock burst.

[0021] Furthermore, the specific formula for generating the disturbance fluctuation interference coefficient is: ; in, is the disturbance fluctuation interference coefficient, is the rise and decay time difference, is the power spectrum density of the dynamic disturbance wave of the surrounding rock mass, is the spectrum entropy of the dynamic disturbance wave of the surrounding rock mass; the disturbance wave interference coefficient reflects the influence of the dynamic disturbance wave on the rock mass. The larger its value is, the greater the influence of the dynamic disturbance wave on the rock mass is, and the rock mass is more likely to have rock burst; the generation of this coefficient can provide an important basis for judging whether rock burst will occur during static blasting, and can provide an important basis for optimizing static blasting process parameters.

[0022] The rise and decay time difference reflects the distribution consistency of the rise and decay time in the dynamic disturbance wave of the surrounding rock mass; the smaller its value is, the stronger the distribution consistency of the rise and decay time in the dynamic disturbance wave of the surrounding rock mass, the more regular the disturbance signal is, and the lower the probability of causing rock burst. The power spectrum density of the dynamic disturbance wave of the surrounding rock mass reflects the energy concentration of the dynamic disturbance wave of the surrounding rock mass. The larger the value is, the more concentrated the energy of the dynamic disturbance wave of the surrounding rock mass is, and it is more likely to cause rock burst. The spectrum entropy indicates the uniformity of energy distribution in the spectrum, which reflects the complexity or disorder of the dynamic disturbance wave of the surrounding rock mass in the frequency domain. The larger its value is, the more uniform the energy is distributed in the spectrum, and the signal contains more frequency components, indicating that the disturbance is more complex and random; it is more likely to cause rock burst; among them, and The regularity of dynamic disturbance waves is reflected from different levels, so they are coupled by multiplication. Dynamic disturbance signals (such as dynamic disturbance waves of surrounding rock masses) usually have a natural physical phenomenon: when the energy intensity, complexity or asymmetry of the disturbance wave increases, its interference effect on the system will rapidly become stronger; conversely, when these characteristics are weak, the interference effect will be sharply weakened. The exponential form happens to be able to capture this nonlinear response characteristic very well, so the invention adopts Represents the disturbance fluctuation interference coefficient.

[0023] The specific formula for generating the disturbance stress interference coefficient is: ; in, is the disturbance stress interference coefficient, is the average dynamic and static stress difference, is the average dynamic stress variance. The disturbance stress interference coefficient is used to quantify the comprehensive impact of stress disturbance on the rock mass. The smaller the value, the greater the interference of stress disturbance on the rock mass, and the more likely it is to cause rock burst. The average dynamic and static stress difference measures the overall difference between the dynamic stress generated during the blasting and the static stress before the blasting. The comparison of static stress and dynamic stress can reveal the degree of disturbance of the rock mass by the blasting process. The larger the value, the greater the overall difference, the greater the degree of disturbance, and the more likely it is to cause rock burst; the average dynamic stress variance reflects the degree of fluctuation of the average dynamic stress of each sensor relative to the overall average. If The larger the value, the uneven distribution of dynamic stress of different sensors means that the disturbance intensity of blasting on different locations of the rock mass is quite different, which is more likely to cause rock burst. Describes the uneven distribution of dynamic stress among different sensors. It is large, indicating that the distribution of dynamic stress has fluctuated significantly in space, and the stress difference at different locations is large. Endow Nonlinear amplification effect: When the dynamic stress fluctuates When it increases, the disturbance effect will be more significant, so Will decrease rapidly.

[0024] Furthermore, the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient are analyzed to obtain the blasting rock burst generation coefficient; the specific formula for generating the blasting rock burst generation coefficient is: ; in, is the blasting rock burst generation coefficient, is the disturbance fluctuation interference coefficient, is the disturbance stress interference coefficient.

[0025] The blasting rockburst generation coefficient reflects the influence of the dynamic disturbance and stress disturbance generated by static blasting on the rockburst phenomenon in the surrounding rock mass. The larger the value, the easier it is to have rockburst. The disturbance wave interference coefficient reflects the influence of the dynamic disturbance wave on the rock mass. When it is larger, it indicates that the disturbance wave generated by the blasting has higher intensity, complexity or asymmetry, and the interference effect on the rock mass is significantly increased, which will cause the microcracks inside the rock mass to expand and stress redistribute, and may eventually trigger instantaneous destruction of the rock mass; The disturbance stress interference coefficient is used to quantify the comprehensive influence of stress disturbance on the rock mass, specifically including the strong change of the static stress field of the rock mass caused by the blasting disturbance (average dynamic and static stress difference) and the uneven dynamic stress distribution of the rock mass, which causes the surrounding rock mass to bear higher disturbance loads (average dynamic stress variance). Strong stress disturbance may lead to local stress concentration in the rock mass, reaching the fracture conditions, thereby triggering rock burst.

[0026] Step 3: Use the pre-blasting data as the training set and the blasting rock burst generation coefficient as the label to build a blasting rock burst prediction model, and use the training set to train the blasting rock burst prediction model; The blasting rock burst prediction model adopts a feedforward neural network, takes the blasting rock burst generation coefficient as the label, and uses the training set to train and optimize the blasting rock burst prediction model; specifically, it includes: input layer, hidden layer, output layer and activation function, the input layer is responsible for receiving the data of the training set; the hidden layer is used to process the data of the training set; it is composed of multiple layers, each layer contains 4 time nodes, and the time node of each hidden layer is connected to the previous layer through weights, which is used to perform feature abstraction and nonlinear transformation on the input training set; by using the ReLu activation function, the nonlinear relationship is introduced so that the model can fit complex feature relationships; an independent neuron is set in the output layer, which is responsible for converting the local and high-level feature representations extracted by the hidden layer to output the blasting rock burst generation coefficient; the root mean square error loss function is adopted; the input data is calculated once through the network to obtain the output result, the loss function is calculated according to the predicted value and the true value, the gradient of the loss function for each weight and bias is calculated by the chain rule, and the weights and biases of the network are updated using the gradient descent algorithm to minimize the loss function.

[0027] Step 4: Obtain the lithology data of the area to be blasted, and select a set of historical blasting setting data with the closest fracture toughness as the initial solution. Take the minimum blasting rock burst generation coefficient as the optimization goal, use the genetic algorithm to optimize the blasting setting data, and select the set with the smallest blasting rock burst generation coefficient as the optimal blasting setting data; Genetic algorithms can be used to optimize blasting setting data using existing technologies, including: obtaining lithology data of the area to be blasted, selecting a set of historical blasting setting data that is closest to the blasting data based on fracture toughness as the initial solution, and constructing an initial population containing multiple sets of blasting setting data. The calculated value of the blasting rock burst generation coefficient is used as the optimization target, and the problem is set as a minimization problem. A new population is iteratively generated through the selection, crossover, and mutation operations of the genetic algorithm. In each generation, individuals in the population are screened according to the size of the blasting rock burst generation coefficient, and the blasting parameters are continuously optimized until the preset convergence conditions or the maximum number of iterations are reached. Finally, a set of blasting parameters that minimize the blasting rock burst generation coefficient is selected as the optimal solution; Step 5: Build a blasting rock model on the digital twin platform based on the lithology data of the area to be blasted and the blasting setting data to preview the blasting effect.

[0028] The present invention further provides a static blasting process optimization system based on digital twins, and the system is used to implement the static blasting process optimization method based on digital twins, which specifically includes: A data acquisition module, used to acquire historical static blasting data, wherein the static blasting data includes pre-blasting data and blasting data; the pre-blasting data includes lithology data and blasting setting data; Comprehensive analysis module, used to analyze the data before blasting and during blasting to obtain disturbance fluctuation characteristics and disturbance stress characteristics, analyze the disturbance fluctuation characteristics to obtain the disturbance fluctuation interference coefficient, analyze the disturbance stress characteristics to obtain the disturbance stress interference coefficient, and analyze the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient to obtain the blasting rock burst generation coefficient; A model building module is used to build a blasting rock burst prediction model using pre-blasting data as a training set and blasting rock burst generation coefficients as labels, and to train the blasting rock burst prediction model using the training set; The data optimization module is used to obtain the lithology data of the area to be blasted, and select a group of historical blasting setting data with the closest fracture toughness as the initial solution. The blasting setting data is optimized using a genetic algorithm with the minimum blasting rock burst generation coefficient as the optimization goal, and the group with the minimum blasting rock burst generation coefficient is selected as the optimal blasting setting data; The digital twin module is used to build a blasting rock model on the digital twin platform based on the lithology data of the area to be blasted and the blasting setting data, and to preview the blasting effect.

[0029] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0030] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0031] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0032] The above is only a specific implementation method of the present application, but the protection scope of the present application is not limited thereto. Any technical personnel familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A static blasting process optimization method based on digital twin, characterized in that: The specific steps include: Step 1: Acquire historical static blasting data, wherein the static blasting data includes pre-blasting data and blasting data; the pre-blasting data includes lithology data and blasting setting data; Step 2: Analyze the data before blasting and during blasting to obtain the disturbance fluctuation characteristics and disturbance stress characteristics, analyze the disturbance fluctuation characteristics to obtain the disturbance fluctuation interference coefficient, analyze the disturbance stress characteristics to obtain the disturbance stress interference coefficient, and analyze the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient to obtain the blasting rock burst generation coefficient; Step 3: Use pre-blasting data as input and blasting rock burst generation coefficient as label to build blasting rock burst prediction model, use pre-blasting data in historical static blasting data as training set, and use the training set to train blasting rock burst prediction model; Step 4: Obtain the lithology data of the area to be blasted, and select a set of historical blasting setting data with the closest fracture toughness as the initial solution. Take the minimum blasting rock burst generation coefficient as the optimization goal, use the genetic algorithm to optimize the blasting setting data, and select the set with the smallest blasting rock burst generation coefficient as the optimal blasting setting data; Step 5: Build a blasting rock model on the digital twin platform based on the lithology data of the area to be blasted and the blasting setting data to preview the blasting effect.

2. The static blasting process optimization method based on digital twin according to claim 1 is characterized in that: The lithology data include the density, shear modulus, elastic modulus, Poisson's ratio, porosity and fracture toughness of the rock mass, and the static stress of the surrounding rock mass; the blasting setting data include the borehole diameter, hole depth, hole spacing, the mixing ratio of the expansion agent and water, and the expansion agent filling rate; the blasting data include the dynamic disturbance wave of the surrounding rock mass and the dynamic stress of the surrounding rock mass; The density, shear modulus, elastic modulus, Poisson's ratio, porosity and fracture toughness are obtained by sending rock samples to the laboratory for measurement. The static blasting area is expanded to one-third and one-fourth of the original area. The non-static blasting area in the expanded area is defined as the surrounding area, and the rock mass in the surrounding area is the surrounding rock mass. The static stress and dynamic stress of the surrounding rock mass are obtained by stress sensors evenly arranged in the surrounding area. The dynamic disturbance wave of the surrounding rock mass is obtained by a seismic detector. The dynamic disturbance wave and dynamic stress of the surrounding rock mass are collected until the static blasting is completed or a rock burst occurs.

3. The static blasting process optimization method based on digital twin according to claim 2 is characterized in that: The disturbance fluctuation characteristics include rise and decay time difference, power spectrum density and spectrum entropy, and the disturbance stress characteristics include average dynamic and static stress difference and average dynamic stress variance; The specific logic for calculating the rise-decay time difference is: respectively count the time from the trough to the peak or from the trough to the peak of each section of the dynamic disturbance wave of the surrounding rock mass, and record it as the rise-decay time; calculate the distribution variance of the rise-decay time, and record the distribution variance of the rise-decay time as the rise-decay time difference. The specific formula is: ； in, is the rise and decay time difference, For the Rise and fall time, is the total rise and decay time, is the index of rise and decay time; The specific logic for obtaining the power spectrum density and spectrum entropy is: Fourier transform the dynamic disturbance wave of the surrounding rock mass to transform the amplitude of the dynamic disturbance wave of the surrounding rock mass into the form of the sum of a series of complex numbers, and obtain the spectrum of the dynamic disturbance wave of the surrounding rock mass. The spectrum of the dynamic disturbance wave of the surrounding rock mass is analyzed to obtain the power spectrum density and spectrum entropy. The specific formula is: ； ； ； in, is the power spectrum density of the dynamic disturbance wave of the surrounding rock mass, is the spectrum entropy of the dynamic disturbance wave of the surrounding rock mass, is the spectrum of the dynamic disturbance wave of the surrounding rock mass, is the spectrum energy; is the total number of frequencies, is the frequency index; The specific logic for obtaining the disturbance stress characteristics is as follows: calculate the average value of the dynamic stress collected by each stress sensor during the static blasting process, first subtract the average value of the dynamic stress from the static stress, and then take the absolute value to obtain the average dynamic and static stress difference; first take the average value of the dynamic stress collected by each stress sensor during the static blasting process, and then calculate the variance of the average dynamic stress of all stress sensors to obtain the average dynamic stress variance; the specific formula is: ； ； ； in, is the average dynamic and static stress difference, is the average dynamic stress variance, For the The average dynamic stress of the stress sensors, For the The stress sensor The dynamic stress collected is For the The static stress of each sensor, is the total number of stress sensors, is the number of collections, The sensor is the stress sensor index, The index of the number of collections.

4. The static blasting process optimization method based on digital twin according to claim 3 is characterized in that: The specific formula for generating the disturbance fluctuation interference coefficient is: ； in, is the disturbance fluctuation interference coefficient, is the rise and decay time difference, is the power spectrum density of the dynamic disturbance wave of the surrounding rock mass, is the spectrum entropy of the dynamic disturbance wave of the surrounding rock mass; The specific formula for generating the disturbance stress interference coefficient is: ； in, is the disturbance stress interference coefficient, is the average dynamic and static stress difference, is the mean dynamic stress variance.

5. The static blasting process optimization method based on digital twin according to claim 4 is characterized in that: The disturbance fluctuation interference coefficient and the disturbance stress interference coefficient are analyzed to obtain the blasting rock burst generation coefficient; the specific formula for generating the blasting rock burst generation coefficient is: ； in, is the blasting rock burst generation coefficient, is the disturbance fluctuation interference coefficient, is the disturbance stress interference coefficient.

6. A static blasting process optimization system based on digital twin, characterized by: The system is used to implement the static blasting process optimization method based on digital twins as described in any one of claims 1 to 5, specifically comprising: A data acquisition module, used to acquire historical static blasting data, wherein the static blasting data includes pre-blasting data and blasting data; the pre-blasting data includes lithology data and blasting setting data; Comprehensive analysis module, used to analyze the data before blasting and during blasting to obtain disturbance fluctuation characteristics and disturbance stress characteristics, analyze the disturbance fluctuation characteristics to obtain the disturbance fluctuation interference coefficient, analyze the disturbance stress characteristics to obtain the disturbance stress interference coefficient, and analyze the disturbance fluctuation interference coefficient and the disturbance stress interference coefficient to obtain the blasting rock burst generation coefficient; A model building module is used to build a blasting rock burst prediction model using pre-blasting data as a training set and blasting rock burst generation coefficients as labels, and to train the blasting rock burst prediction model using the training set; The data optimization module is used to obtain the lithology data of the area to be blasted, and select a group of historical blasting setting data with the closest fracture toughness as the initial solution. The blasting setting data is optimized using a genetic algorithm with the minimum blasting rock burst generation coefficient as the optimization goal, and the group with the minimum blasting rock burst generation coefficient is selected as the optimal blasting setting data; The digital twin module is used to build a blasting rock model on the digital twin platform based on the lithology data of the area to be blasted and the blasting setting data, and to preview the blasting effect.

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