Medium-hard rock tunnel excavation blasting lumpiness intelligent regulation and control method and device and electronic equipment

Abstract

The invention relates to the technical field of tunnel excavation engineering, in particular to a medium-hard rock tunnel excavation blasting lumpiness intelligent regulation and control method and device and electronic equipment.The method comprises the steps that the target lumpiness particle size range and the excavation volume are determined according to the medium-hard rock tunnel excavation requirement, and a correlation function of the blasting lumpiness and blasting unit consumption is determined; the method comprises the following steps: establishing a medium-hard rock tunnel blasting contour hole explosive load calculation function and a medium-hard rock tunnel slotting hole explosive load calculation function, and establishing a correlation function model of tunnel blasting lumpiness and blast hole final explosive load at each section of a tunnel face according to the functions; and finally, according to the target lumpiness particle size range, the excavation volume and the related function model, determining a blast hole general explosive quantity scheme, and according to the field blasting data of the medium-hard rock tunnel, optimizing the blast hole general explosive quantity scheme. Therefore, the problems that a traditional blasting method mainly depends on experience of constructors, blasting lumpiness is not uniform, and construction efficiency and safety are affected are solved.

Description

Technical Field

[0001] The present application relates to the technical field of tunnel excavation engineering, and in particular to a method, device and electronic equipment for intelligently controlling blasting block size in medium-hard rock tunnel excavation. Background Art

[0002] In tunnel engineering, blasting technology is one of the main means to achieve tunnel excavation. Traditional blasting methods mainly rely on the experience of construction workers and lack precise scientific basis, resulting in uneven blasting size, affecting construction efficiency and safety. With the development of intelligent technology, the need for intelligent control of blasting size is becoming increasingly urgent. Summary of the invention

[0003] The present application provides a method, device and electronic equipment for intelligently controlling blasting block size in medium-hard rock tunnel excavation, so as to solve the problem that traditional blasting methods mainly rely on the experience of construction personnel, resulting in uneven blasting block size and affecting construction efficiency and safety.

[0004] The first aspect of the present application provides an intelligent control method for blasting block size in medium-hard rock tunnel excavation, comprising the following steps: determining a target block size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel; determining a correlation function between blasting block size and blasting unit consumption based on a historical database of blasting block size and unit consumption statistics and actual detection data of blasting block size in the medium-hard rock tunnel; establishing a charge calculation function for blasting contour holes of the medium-hard rock tunnel based on the tunnel contour forming blasting principle; establishing a charge calculation function for slot hole excavation of the medium-hard rock tunnel based on the slot excavation blasting cavity forming principle; establishing a correlation function model between tunnel blasting block size and total charge of blast holes at each section of the face according to the correlation function between blasting block size and blasting unit consumption, the charge calculation function for blasting contour holes and the charge calculation function for slot holes; determining a total charge plan for blast holes according to the target block size range and excavation volume, and the correlation function model, and optimizing the total charge plan for blast holes according to on-site blasting data of the medium-hard rock tunnel.

[0005] Optionally, the historical database includes multiple data on blasthole arrangement, hole grid parameters, blasting unit consumption, block size, contour forming effect and rock mass properties.

[0006] Optionally, the blasting fragmentation is characterized by the median particle size, and a correlation function between the blasting fragmentation and the blasting unit consumption is obtained by least squares regression analysis fitting.

[0007] Optionally, the following conditions are met when the charge calculation function of the blasting contour hole is established: the peak value of the tensile stress superimposed between adjacent contour holes is greater than the tensile strength of the rock mass; the peak value of the load pressure applied to the blasthole wall should be less than the compressive strength of the rock mass.

[0008] Optionally, the charge calculation function for the cut hole is established based on the position, size and rock conditions of the cut hole, and satisfies the condition that the groove cavity rock mass reaches a limit equilibrium under the static action of the blasting gas.

[0009] Optionally, the total charge of blast holes at each section of the caving hole area of ​​the face decreases linearly from the inside to the outside.

[0010] Optionally, the optimization goal of the total charge plan for the blasthole is to increase the content of target particle size in the block material after blasting.

[0011] Optionally, the blasting fragmentation size of medium-hard rock tunnels is detected by image recognition or mechanical screening technology.

[0012] The second aspect of the present application provides an intelligent control device for blasting block size in medium-hard rock tunnel excavation, including: a first determination module for determining a target block size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel; a second determination module for determining a correlation function between blasting block size and blasting unit consumption based on a historical database of blasting block size and unit consumption statistics and actual detection data of medium-hard rock tunnel blasting block size; a first establishment module for establishing a charge calculation function for medium-hard rock tunnel blasting contour holes based on the tunnel contour forming blasting principle; and a second establishment module for determining a target block size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel; a second establishment module for determining a target block size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel; and a second establishment module for determining a target block size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel. The first block is used to establish a calculation function for the charge of slot holes in medium-hard rock tunnels based on the principle of slot blasting cavity formation; the third establishment module is used to establish a correlation function model between tunnel blasting block size and the total charge of blast holes at each section of the face according to the correlation function between blasting block size and blasting unit consumption, the charge calculation function of blasting contour holes and the charge calculation function of slot holes; the optimization module is used to determine the total charge plan for blast holes according to the target block size range and excavation volume, as well as the correlation function model, and optimize the total charge plan for blast holes according to the on-site blasting data of medium-hard rock tunnels.

[0013] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the first aspect of the method for intelligently controlling blasting block size in medium-hard rock tunnel excavation.

[0014] Therefore, this application includes the following beneficial effects:

[0015] The embodiment of the present application can determine the target block size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel, and then determine the correlation function between blasting block size and blasting unit consumption, the charge calculation function of the blasting contour hole of the medium-hard rock tunnel, and the charge calculation function of the slot hole of the medium-hard rock tunnel based on the historical database of blasting block size and unit consumption statistics and the actual detection data of the blasting block size of the medium-hard rock tunnel, as well as the tunnel contour forming blasting principle, and then, according to the above functions, establish a correlation function model between the tunnel blasting block size and the total charge of the blast holes at each section of the face, finally determine the total charge plan for the blast holes according to the target block size range and excavation volume, and the correlation function model, and optimize the total charge plan for the blast holes according to the on-site blasting data of the medium-hard rock tunnel, thereby improving the uniformity of the blasting block size, reducing the waste of resources in the blasting process, realizing the intelligence and automation of the blasting process, reducing the dependence on manual experience, and improving the efficiency and safety of tunnel excavation. This solves the problem that the traditional blasting method mainly relies on the experience of construction workers, resulting in uneven blasting blocks and affecting construction efficiency and safety.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0018] Figure 1 It is a flow chart of the intelligent control method of blasting block size for medium-hard rock tunnel excavation provided according to an embodiment of the present application;

[0019] Figure 2 A flow chart of a method for intelligently controlling the block size of tunnel excavation blasting provided according to an embodiment of the present application;

[0020] Figure 3 A diagram of a calculation model for slotting and blasting provided according to an embodiment of the present application;

[0021] Figure 4 A graph showing a corresponding relationship between cumulative mass fraction and particle size according to an embodiment of the present application;

[0022] Figure 5 This is an example diagram of an intelligent control device for blasting block size in medium-hard rock tunnel excavation provided according to an embodiment of the present application;

[0023] Figure 6 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0025] The following describes the intelligent control method, device and electronic device for blasting block size in medium-hard rock tunnel excavation according to the embodiments of the present application with reference to the accompanying drawings. In view of the problem that the traditional blasting method mentioned in the above background technology mainly relies on the experience of construction personnel, resulting in uneven blasting block size, which affects construction efficiency and safety, the present application provides an intelligent control method for blasting block size in medium-hard rock tunnel excavation. In this method, the target block size range and excavation volume can be determined according to the excavation requirements of the medium-hard rock tunnel, and then based on the historical database of blasting block size and unit consumption statistics and the actual detection data of blasting block size in medium-hard rock tunnels, as well as the tunnel contour forming blasting principle, the blasting block size and blasting unit consumption correlation function, the medium-hard rock tunnel blasting contour, etc. are determined. The calculation function of the charge amount of the hole and the charge amount calculation function of the slot hole in the medium-hard rock tunnel are then established based on the above functions. Then, the correlation function model between the tunnel blasting block size and the total charge amount of the blastholes at each section of the face is established. Finally, the total charge amount plan of the blasthole is determined according to the target block size range and excavation volume, as well as the correlation function model. The total charge amount plan of the blasthole is optimized according to the on-site blasting data of the medium-hard rock tunnel, which improves the uniformity of the blasting block size, reduces the waste of resources in the blasting process, realizes the intelligence and automation of the blasting process, reduces the dependence on manual experience, and improves the efficiency and safety of tunnel excavation. In this way, the problem that the traditional blasting method mainly relies on the experience of the construction personnel, resulting in uneven blasting block size and affecting the construction efficiency and safety is solved.

[0026] Specifically, Figure 1 A flow chart of an intelligent control method for blasting block size in medium-hard rock tunnel excavation provided in an embodiment of the present application.

[0027] like Figure 1 As shown, the intelligent control method for blasting block size in medium-hard rock tunnel excavation includes the following steps:

[0028] In step S101, the target particle size range and excavation volume are determined according to the excavation requirements of the medium-hard rock tunnel.

[0029] It can be understood that the embodiment of the present application can determine the size range and excavation volume of the target block size of the block stone obtained after blasting according to the actual needs of medium-hard rock tunnel excavation.

[0030] In step S102, a correlation function between blasting size and blasting unit consumption is determined based on a historical database of blasting size and unit consumption statistics and actual detection data of blasting size in medium-hard rock tunnels.

[0031] Among them, blasting fragmentation refers to the degree of rock fragmentation after blasting, which is usually measured by the maximum size of rock fragments. The actual detection method of blasting fragmentation will be described in detail below and will not be repeated here; blasting unit consumption refers to the amount of explosives required per unit volume or weight of rock, which is an important indicator for evaluating blasting efficiency; the historical database of blasting fragmentation and unit consumption statistics will be described in detail below and will not be repeated here.

[0032] It can be understood that the embodiment of the present application can determine a correlation function describing the relationship between blasting size and blasting unit consumption by analyzing the statistical data of blasting size and unit consumption in the historical database and combining the blasting size information of medium-hard rock tunnel actually detected.

[0033] In the embodiment of the present application, the historical database includes multiple data on blasthole arrangement, hole network parameters, blasting unit consumption, block size, contour forming effect and rock mass properties.

[0034] Specifically, the blasthole layout refers to the position, direction and arrangement of the holes drilled during the blasting operation. Common layouts include linear, rectangular, and plum blossom shapes. The hole grid parameters refer to parameters such as the spacing between blastholes and the depth of the blastholes. The contour forming effect is used to describe whether the flatness and shape of the tunnel wall after blasting meet the design requirements. The rock properties include rock type, hardness, degree of crack development, and other rock characteristics.

[0035] In the embodiments of the present application, the blasting fragmentation is characterized by the median particle size, and the correlation function between the blasting fragmentation and the blasting unit consumption is obtained by least squares regression analysis fitting.

[0036] The median particle size is obtained by taking the median of the sizes of all particles in the blasting fragmentation distribution.

[0037] It can be understood that the embodiment of the present application adopts the median particle size as the characterization index of the fragmentation, and combines it with the on-site tunnel blasting fragmentation detection test, and obtains a correlation function that can accurately describe the relationship between blasting fragmentation and blasting unit consumption through least squares regression analysis and fitting.

[0038] In step S103, a charge calculation function for the blasting contour hole of a medium-hard rock tunnel is established based on the tunnel contour forming blasting principle.

[0039] Among them, tunnel contour blasting is a blasting technology designed to accurately control the shape and size of tunnel excavation. Its core goal is to minimize over-excavation and under-excavation while ensuring safety and efficiency, protect the surrounding rock structure from damage, and ensure that the tunnel wall is smooth and stable.

[0040] It can be understood that the embodiment of the present application can establish a charge calculation function for the contour hole of a medium-hard rock tunnel blasting through the tunnel contour forming blasting principle to determine the amount of explosives to be loaded in each contour hole. The method for establishing the function will be described in detail below and will not be repeated here.

[0041] In the embodiment of the present application, the following conditions are met when the blasting contour hole charge calculation function is established: the peak value of the tensile stress superimposed between adjacent contour holes is greater than the tensile strength of the rock mass; the peak value of the load pressure applied to the blasthole wall should be less than the compressive strength of the rock mass.

[0042] Among them, the peak value of tensile stress refers to the maximum value of these tensile stresses. The tensile strength of rock mass is the ability of rock to resist tensile fracture, that is, the maximum stress that rock can withstand when under tension. The peak value of load pressure is the maximum compressive stress generated by the explosion on the wall of the blasthole. The compressive strength of rock mass is the ability of rock to resist compression damage, that is, the maximum stress that rock can withstand when under compression.

[0043] It can be understood that the charge calculation function for the contour hole of medium-hard rock tunnel blasting established in the embodiment of the present application must ensure that two conditions are met: first, the peak value of the tensile stress superimposed between adjacent contour holes must be greater than the tensile strength of the rock mass to ensure that the rock is effectively broken along the designed contour line; second, the peak value of the load pressure applied to the blasthole wall should be less than the compressive strength of the rock mass to avoid excessive damage to the surrounding rock structure.

[0044] In step S104, a charge calculation function for the slot hole of a medium-hard rock tunnel is established based on the principle of cavity formation by slot blasting.

[0045] Among them, slotting blasting is a blasting technology commonly used in tunnel excavation. It first performs high-intensity blasting at the center of the predetermined excavation section to form an initial cavity, namely the slotting, thereby creating a free surface for the subsequent blasting of peripheral holes and auxiliary holes.

[0046] It can be understood that the embodiment of the present application can establish a charge calculation function for the slot hole of a medium-hard rock tunnel based on the principle of slot blasting cavity formation. The establishment method will be described in detail below and will not be repeated here.

[0047] In the embodiment of the present application, the calculation function of the charge amount of the slot hole is established based on the position, size and rock conditions of the slot hole, and meets the condition that the slot rock mass reaches the limit equilibrium under the static action of the explosive gas.

[0048] Among them, the trough rock mass is the rock mass around the initial cavity formed after the slot hole blasting; the static effect of the explosive gas refers to the pressure effect of the explosive gas on the surrounding medium during the explosion process; for the trough rock mass, the limit equilibrium means that the explosive gas pressure it withstands just reaches the maximum value it can withstand, which will neither cause excessive damage nor fail to fully break the rock due to insufficient pressure.

[0049] It can be understood that the charging calculation function of the slot hole in the embodiment of the present application is established based on the position, size and rock conditions of the slot hole, and needs to satisfy the requirement that the slot rock mass reaches the ultimate equilibrium under the static action of the explosive gas, that is, the explosive gas pressure borne by the slot rock mass just reaches the maximum value it can withstand, which will neither cause excessive damage to the rock mass nor fail to fully crush the rock due to insufficient pressure.

[0050] In step S105, a correlation function model between tunnel blasting block size and total charge of blast holes at each section of the face is established based on the correlation function between blasting block size and blasting unit consumption, blasting contour hole charge calculation function and slot hole charge calculation function.

[0051] Among them, the method for establishing a correlation function model between the tunnel blasting block size and the total charge of the blast holes in each section of the tunnel face according to the above function is to establish a correlation function model between the tunnel blasting block size and the total charge of the blast holes in each section of the tunnel face based on the above function through a machine learning algorithm and a historical database; the machine learning algorithm can be a suitable algorithm such as a support vector machine and a neural network; the tunnel face, also called the working face or excavation face, refers to the part of the rock surface where excavation operations are currently being carried out in the tunnel engineering.

[0052] It can be understood that the embodiment of the present application is based on the correlation function between blasting block size and blasting unit consumption, the blasting contour hole charge calculation function and the slot hole charge calculation function obtained by the above calculation, and uses the data in the historical database and machine learning algorithms, such as support vector machines and neural networks, to analyze the blasting effects produced under different charging conditions, establish a model, and obtain a correlation function model between the tunnel blasting block size and the total charge of the blast holes in each section of the face.

[0053] In the embodiment of the present application, the total charge of the blast holes at each section of the caving hole area of ​​the tunnel face decreases linearly from the inside to the outside.

[0054] Among them, on the tunnel face, in order to achieve effective crushing and removal of rock, a series of blastholes will be arranged for blasting. The collapse hole area specifically refers to the collection area of ​​these blastholes used for collapse rock; the collapse hole area is further subdivided according to position or function, usually divided into multiple sections from the center of the tunnel face to the outside, such as the groove area, the peripheral area, etc. The blastholes in each section are the blastholes of each section; the linear decrease of the total charge means that from the inside to the outside, the charge in the blastholes of each section gradually decreases according to a certain linear proportion.

[0055] It can be understood that the embodiment of the present application has a larger amount of explosives in the slot hole area close to the center of the face to form an initial cavity and guide subsequent blasting; as the distance increases and transitions to the surrounding area, the amount of explosives gradually decreases and eventually reaches a minimum value, ensuring that the internal rock can be fully crushed, and effectively reducing the impact force on the external rock mass, avoiding unnecessary damage, thereby improving the safety and efficiency of blasting.

[0056] In step S106, the total charge plan of the blasthole is determined according to the target particle size range and excavation volume and the related function model, and the total charge plan of the blasthole is optimized according to the on-site blasting data of the medium-hard rock tunnel.

[0057] It can be understood that the embodiments of the present application can determine the total charge plan for the blasthole based on the target particle size range and excavation volume, as well as the related function model established above, carry out on-site blasting and perform particle size detection according to the plan, obtain on-site blasting data for medium-hard rock tunnels and evaluate the rationality of the charge, and optimize the total charge plan for the blasthole.

[0058] In the embodiment of the present application, the optimization goal of the total charge plan of the blasthole is to increase the content of target particle size in the block stone after blasting.

[0059] It can be understood that the embodiment of the present application detects the target particle size in the block rock after blasting, and takes the block rock with the target particle size as the optimization target, and performs intelligent optimization design on the total charge amount scheme of the blasthole.

[0060] In the embodiments of the present application, the blasting fragmentation of medium-hard rock tunnel is detected by image recognition or mechanical screening technology.

[0061] It can be understood that image recognition technology is a technology that uses computer vision algorithms to automatically extract information from images of rock fragments after blasting; mechanical screening technology is a traditional physical screening method that screens materials through sieves with different apertures to separate materials of different particle size levels, and can directly measure the blockiness.

[0062] According to the intelligent control method of blasting block size for medium-hard rock tunnel excavation proposed in the embodiment of the present application, the target block size particle size range and excavation volume can be determined according to the excavation requirements of the medium-hard rock tunnel, and then based on the historical database of blasting block size and unit consumption statistics and the actual detection data of blasting block size of the medium-hard rock tunnel, as well as the tunnel contour forming blasting principle, the blasting block size and blasting unit consumption correlation function, the medium-hard rock tunnel blasting contour hole charge calculation function and the medium-hard rock tunnel slot hole charge calculation function are determined. Then, according to the above functions, a correlation function model between the tunnel blasting block size and the total charge of the blast holes at each section of the face is established. Finally, according to the target block size particle size range and excavation volume, and the correlation function model, the total charge plan for the blast holes is determined, and the total charge plan for the blast holes is optimized according to the on-site blasting data of the medium-hard rock tunnel, thereby improving the uniformity of the blasting block size, reducing the waste of resources in the blasting process, realizing the intelligence and automation of the blasting process, reducing the dependence on manual experience, and improving the efficiency and safety of tunnel excavation.

[0063] The following is a further description of the intelligent control method for blasting block size in medium-hard rock tunnel excavation by a specific embodiment. Figure 2 As shown in FIG. 1 , the intelligent control method for blasting block size in tunnel excavation includes the following steps:

[0064] Step S201: Determine the target blockiness range.

[0065] According to the actual situation of the project, determine the size range and demand of the target blocks of stone obtained after blasting.

[0066] Step S202: data collection and analysis.

[0067] The blasting scheme and blasting effect information of medium-hard rock tunnels were collected, and a historical database including blasthole arrangement, hole network parameters, blasting unit consumption, block size, contour forming effect, etc. was established. At the same time, combined with the on-site tunnel blasting block size detection test, the correlation function between blasting block size and blasting unit consumption was obtained through least squares regression analysis and fitting.

[0068] Step S203: Establishing a charge quantity calculation function.

[0069] Based on the tunnel contour forming blasting principle, a calculation function for the charge amount of tunnel blasting contour holes is established. This function should meet the conditions that the peak value of the tensile stress superimposed between adjacent contour holes is greater than the tensile strength of the rock mass, and the peak value of the load pressure applied on the blasthole wall should be less than the compressive strength of the rock mass. The specific calculation formula includes:

[0070]

[0071] Among them, Q e The charge amount per hole is shown in the outline; l = l e +l ais the blasthole depth, l e is the length of the charging section, l a is the length of the air segment; r b is the diameter of the blast hole; K d is the radial uncoupling coefficient; ρ0 is the explosive density; n is the pressure increase factor; μ is the Poisson's ratio of the rock mass, L is the hole spacing, σ r is the compressive strength of rock mass, σ θ is the tensile strength of rock mass.

[0072] like Figure 3 The slot blasting model shown in the figure is based on the slot blasting cavity formation principle, and establishes a calculation function for the charge amount of the slot hole in a medium-hard rock tunnel. This function is established based on factors such as the location, size and rock conditions of the slot hole, and at the same time, it must meet the condition that the slot rock mass reaches the limit equilibrium under the static action of the blasting gas. The specific calculation formula is as follows:

[0073] F=6P d L c d b cosθ

[0074]

[0075] Need to satisfy: F ≥ Q

[0076] Among them, F is the cutting force, Q is the rock resistance, P d is the static pressure acting on the blasthole wall, L c is the charge length, d b is the diameter of the blasthole, θ is the angle between the blasthole and the free surface, c is the rock cohesion, is the internal friction angle of the rock, σ1 is the normal stress, b is the slot width, B is the slot bottom width, L is the blasthole depth, σ2 is the normal stress, a is the slot hole spacing, σ t is the uniaxial ultimate tensile strength of the rock mass.

[0077] Step S204: Establishment of correlation function model and optimization of charge quantity.

[0078] Using machine learning algorithms (such as support vector machines and neural networks) and historical databases, based on the above calculation functions and correlation functions, a correlation function model between the tunnel blasting block size and the total charge of the blastholes at each section of the tunnel face is established. Figure 4 As shown, the total charge plan for the blasthole is intelligently optimized with the optimization goal of obtaining the maximum number of lumps of target particle size.

[0079] Step S205: Propose a preliminary plan and conduct on-site verification.

[0080] The established correlation function model is used to propose a preliminary charge plan for the blasthole. On-site blasting is carried out, and block size detection is performed through image recognition or mechanical screening technology to evaluate the rationality of the charge.

[0081] Step S206: Dynamically update the database.

[0082] The database is dynamically updated to collect new blasting plans, blasting effect information and blasting block size detection data to optimize and adjust relevant function models.

[0083] Through the above steps, the present invention provides a method for intelligent control of blasting size in medium-hard rock tunnel excavation, which deeply integrates on-site monitoring, machine learning and other technologies, realizes precise control of blasting size, improves the efficiency and safety of tunnel excavation, and has important practical application value.

[0084] Next, the intelligent control device for blasting block size in medium-hard rock tunnel excavation proposed in accordance with an embodiment of the present application will be described with reference to the accompanying drawings.

[0085] Figure 5 It is a block diagram of an intelligent control device for blasting block size in medium-hard rock tunnel excavation according to an embodiment of the present application.

[0086] like Figure 5 As shown, the medium hard rock tunnel excavation blasting block size intelligent control device 10 includes: a first determination module 301, a second determination module 302, a first establishment module 303, a second establishment module 304, a third establishment module 305 and an optimization module 306.

[0087] Among them, the first determination module 301 is used to determine the target particle size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel; the second determination module 302 is used to determine the correlation function between blasting block size and blasting unit consumption based on the historical database of blasting block size and unit consumption statistics and the actual detection data of medium-hard rock tunnel blasting block size; the first establishment module 303 is used to establish the charge calculation function of the blasting contour hole of the medium-hard rock tunnel based on the tunnel contour forming blasting principle; the second establishment module 304 is used to establish the charge calculation function of the blasting contour hole of the medium-hard rock tunnel based on the cavity forming principle of the slotting blasting A charge calculation function for slot holes in medium-hard rock tunnels is established; the third establishment module 305 is used to establish a correlation function model between tunnel blasting block size and the total charge of blast holes at each section of the face based on the correlation function between blasting block size and blasting unit consumption, the charge calculation function for blasting contour holes and the charge calculation function for slot holes; the optimization module 306 is used to determine the total charge plan for blast holes based on the target block size range and excavation volume, as well as the correlation function model, and optimize the total charge plan for blast holes based on the on-site blasting data of medium-hard rock tunnels.

[0088] In the embodiment of the present application, the historical database includes multiple data on blasthole arrangement, hole network parameters, blasting unit consumption, block size, contour forming effect and rock mass properties.

[0089] In the embodiments of the present application, the blasting fragmentation is characterized by the median particle size, and the correlation function between the blasting fragmentation and the blasting unit consumption is obtained by least squares regression analysis fitting.

[0090] In the embodiment of the present application, the following conditions are met when the blasting contour hole charge calculation function is established: the peak value of the tensile stress superimposed between adjacent contour holes is greater than the tensile strength of the rock mass; the peak value of the load pressure applied to the blasthole wall should be less than the compressive strength of the rock mass.

[0091] In the embodiment of the present application, the calculation function of the charge amount of the slot hole is established based on the position, size and rock conditions of the slot hole, and meets the condition that the slot rock mass reaches the limit equilibrium under the static action of the explosive gas.

[0092] In the embodiment of the present application, the total charge of the blast holes at each section of the caving hole area of ​​the tunnel face decreases linearly from the inside to the outside.

[0093] In the embodiment of the present application, the optimization goal of the total charge plan of the blasthole is to increase the content of target particle size in the block stone after blasting.

[0094] In the embodiments of the present application, the blasting fragmentation of medium-hard rock tunnel is detected by image recognition or mechanical screening technology.

[0095] It should be noted that the aforementioned explanation of the embodiment of the method for intelligently controlling blasting block size in medium-hard rock tunnel excavation is also applicable to the intelligently controlling device for blasting block size in medium-hard rock tunnel excavation of this embodiment, and will not be repeated here.

[0096] According to the intelligent control device for blasting block size in medium-hard rock tunnel excavation proposed in the embodiment of the present application, through the coordinated action of the first determination module, the second determination module, the first establishment module, the second establishment module, the third establishment module and the optimization module, the target block size range and excavation volume can be determined according to the excavation requirements of the medium-hard rock tunnel, and then based on the historical database of blasting block size and unit consumption statistics and the actual detection data of blasting block size in medium-hard rock tunnels, as well as the tunnel contour forming blasting principle, the blasting block size and blasting unit consumption correlation function, the charge calculation of the medium-hard rock tunnel blasting contour hole, etc. are determined. The function and the charge calculation function for the slot holes in medium-hard rock tunnels are proposed. Then, based on the above function, a correlation function model between the tunnel blasting block size and the total charge of the blastholes at each section of the face is established. Finally, the total charge plan for the blastholes is determined according to the target block size range and excavation volume, as well as the correlation function model. The total charge plan for the blastholes is optimized according to the on-site blasting data of the medium-hard rock tunnel, which improves the uniformity of the blasting block size, reduces the waste of resources in the blasting process, realizes the intelligence and automation of the blasting process, reduces the dependence on manual experience, and improves the efficiency and safety of tunnel excavation.

[0097] Figure 6A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0098] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0099] When the processor 402 executes the program, the intelligent control method of blasting block size for medium-hard rock tunnel excavation provided in the above embodiment is implemented.

[0100] Furthermore, the electronic device further comprises:

[0101] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0102] The memory 401 is used to store computer programs that can be run on the processor 402 .

[0103] The memory 401 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.

[0104] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) 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 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0105] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0106] The processor 402 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.

[0107] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction 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 N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0108] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0109] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0110] It should be understood that the various parts of the present application can be implemented in hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, the steps or methods can be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array, a field programmable gate array, etc.

[0111] A person of ordinary skill in the art may understand that all or part of the steps carried by the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the above-mentioned program may be stored in a computer-readable storage medium, which, when executed, includes one of the steps of the method embodiment or a combination thereof.

[0112] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An intelligent control method for blasting block size in medium-hard rock tunnel excavation, characterized in that: The following steps are involved: Determine the target particle size range and excavation volume based on the excavation requirements of medium-hard rock tunnels; Based on the historical database of blasting block size and unit consumption statistics and the actual detection data of blasting block size in medium-hard rock tunnels, the correlation function between blasting block size and unit consumption is determined; Based on the tunnel contour shaping blasting principle, a charge calculation function for the blasting contour hole of a medium-hard rock tunnel is established; Based on the principle of cavity formation by slot blasting, a calculation function for charging amount of slot holes in medium-hard rock tunnels is established; According to the correlation function between the blasting block size and the blasting unit consumption, the charge calculation function of the blasting contour hole and the charge calculation function of the slot hole, a correlation function model between the tunnel blasting block size and the total charge of the blasting holes at each section of the face is established; The total charge scheme for the blastholes is determined according to the target particle size range and excavation volume, as well as the correlation function model, and is optimized according to the on-site blasting data of the medium-hard rock tunnel.

2. The method for intelligently controlling the blasting block size for medium-hard rock tunnel excavation according to claim 1 is characterized in that: The historical database includes multiple data on blasthole arrangement, hole network parameters, blasting unit consumption, block size, contour forming effect and rock mass properties.

3. The intelligent control method for blasting block size in medium-hard rock tunnel excavation according to claim 1 is characterized in that: The blasting fragmentation is characterized by the median particle size, and the correlation function between the blasting fragmentation and the blasting unit consumption is obtained by least squares regression analysis fitting.

4. The method for intelligently controlling the blasting block size for medium-hard rock tunnel excavation according to claim 1 is characterized in that: The following conditions are met when the blasting contour hole charge calculation function is established: The peak value of the superimposed tensile stress between adjacent contour holes is greater than the tensile strength of the rock mass; The peak load pressure applied to the blasthole wall should be less than the compressive strength of the rock mass.

5. The method for intelligently controlling the blasting block size for medium-hard rock tunnel excavation according to claim 1 is characterized in that: The charge calculation function for the slot hole is established based on the position, size and rock conditions of the slot hole, and meets the condition that the slot rock mass reaches a limit equilibrium under the static action of the blasting gas.

6. The method for intelligently controlling the blasting block size for medium-hard rock tunnel excavation according to claim 1 is characterized in that: The total charge amount of each section of the blastholes in the caving hole area of ​​the face decreases linearly from the inside to the outside.

7. The intelligent control method for blasting block size in medium-hard rock tunnel excavation according to claim 1 is characterized in that: The optimization goal of the total charge amount scheme of the blasthole is to increase the content of target particle size in the block material after blasting.

8. The method for intelligently controlling the blasting block size for medium-hard rock tunnel excavation according to claim 1 is characterized in that: The size of medium-hard rock tunnel blasting fragments can be detected through image recognition or mechanical screening technology.

9. An intelligent control device for blasting block size in medium-hard rock tunnel excavation, characterized in that: include: The first determination module is used to determine the target particle size range and excavation volume according to the excavation requirements of the medium-hard rock tunnel; The second determination module is used to determine the correlation function between blasting block size and blasting unit consumption based on the historical database of blasting block size and unit consumption statistics and the actual detection data of blasting block size in medium-hard rock tunnels; The first establishment module is used to establish a charge calculation function for the blasting contour hole of a medium-hard rock tunnel based on the tunnel contour forming blasting principle; The second establishment module is used to establish a charge calculation function for the slot hole of a medium-hard rock tunnel based on the principle of slot blasting cavity formation; The third establishment module is used to establish a correlation function model between the tunnel blasting block size and the total charge of the blast holes at each section of the face according to the correlation function between the blasting block size and the blasting unit consumption, the blasting contour hole charge calculation function and the slot hole charge calculation function; The optimization module is used to determine the total charge plan of the blasthole according to the target particle size range and excavation volume, and the related function model, and optimize the total charge plan of the blasthole according to the on-site blasting data of the medium-hard rock tunnel.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for intelligently controlling blasting block size in medium-hard rock tunnel excavation as described in any one of claims 1 to 8.

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