355 results about "Blasting vibration" patented technology

The invention discloses a safety risk assessment system based on slope construction, relates to the technical field of slope construction, constructs a complete system composed of a data acquisition module, a dynamic disturbance identification module, a risk assessment module and an intelligent early warning and decision-making module, and acquires multi-source data such as displacement, cracks, pore water pressure, seismic disturbance and blasting vibration at high frequency. The time sequence analysis and disturbance feature extraction algorithm is combined to realize the rapid identification of the sudden disturbance event; risk score prediction is further carried out based on a polynomial regression model, and multi-level early warning is generated through comparison with a set threshold value; and finally, by combining the construction state, the support parameters and the disturbance intensity, a safety control suggestion is dynamically generated, so that the response speed and prediction precision of the system to the sudden slope instability risk are effectively improved, and real-time perception and intelligent management and control of the risk in the slope construction process are realized.

The invention relates to the technical field of artificial intelligence and data processing, in particular to a method for predicting the influence of tunneling blasting on surface building vibration, which specifically comprises the following steps of: collecting a vibration signal generated by blasting operation in a tunneling process and denoising the vibration signal as a sample; according to the energy distribution characteristics of the signals, a spectral clustering algorithm is adopted to generate meta-tasks; performing feature extraction on samples in each meta-task through a neural network model; a hierarchical attention mechanism is adopted to capture time response characteristics of the vibration signals in different time durations, key characteristics of all stages are obtained and fused, a classification feature vector is obtained, and then a classification prediction result of the influence of blasting on surface building vibration is determined according to the classification feature vector; and optimizing the classification prediction process. According to the method, the tunneling blasting vibration data is processed based on the neural network model of the improved meta-learning strategy, and the accuracy of the vibration influence on the surface building and the construction safety can be improved.

The invention relates to the technical field of tunnel construction, in particular to a tunnel blasting quality evaluation method based on multi-source data fusion, which comprises the following steps: synchronously acquiring multi-source data such as blasting vibration waveform, shock wave overpressure, dust concentration time sequence and excavation face image through a sensor network and image acquisition equipment arranged in a tunnel; performing preprocessing and time sequence alignment on the data to form a standardized data sample set; multi-dimensional feature vectors representing the blasting effect are extracted from the multi-dimensional feature vectors respectively; fusing various features into a high-dimensional feature vector, training a machine learning model by using historical data and quality grade labels evaluated by experts to establish a mapping relationship between the features and quality grades, and constructing an intelligent evaluation model; and online automatic evaluation and visual result output of the new blasting event are realized. According to the invention, automation, objectization and systematization of blasting quality evaluation are realized, and an efficient and reliable data-driven solution is provided for tunnel construction quality control.

The invention provides a blast vibration peak velocity prediction model generation method, a blast vibration peak velocity prediction method, a blast vibration peak velocity prediction device and blast vibration peak velocity prediction equipment, and belongs to the technical field of engineering blasting. Loss functions of the first physical information neural network model comprise a physical information neural network prediction loss function and an empirical formula prediction loss function, and the empirical formula prediction loss function is used for calculating a mean square error between a prediction value of a preset empirical formula and a preset training set actual value; training the first physical information neural network model based on a preset training set; optimizing and training the complete first physical information neural network model based on a particle swarm optimization algorithm to obtain a second physical information neural network model; and training the second physical information neural network model based on the training set to obtain a blasting vibration peak velocity prediction model. The technical problem that a prediction model generated in the prior art seriously depends on the number of data is effectively solved.

The invention provides a shallow-buried tunnel blasting surface vibration waveform prediction method and system, and relates to the technical field of surface vibration waveform prediction, and the method comprises the steps: collecting geological, blasting and environmental parameters of historical and current blasting, and forming a standardized parameter set through three-stage index storage, abnormal value screening and standardization processing; establishing a mapping relation between blasting parameters and a vibration waveform time domain sequence based on an LSTM neural network, and predicting a current blasting vibration waveform; fusing the geological condition influence factors, the environmental sensitivity influence factors and the predicted waveform features to generate a comprehensive safety assessment index; and according to a matching result of the index and a multi-level safety threshold value, the risk is divided into four levels of safety, low risk, medium risk and high risk, and differential management and control are executed, so that accurate prediction and dynamic regulation and control of the blasting vibration risk are realized. According to the invention, the vibration wave propagation process in the shallow tunnel blasting construction can be accurately simulated, so that the influence of blasting can be better evaluated.

The invention discloses a blasting vibration test-based rock mass wave velocity field measurement method, which is applied to a blasting operation area, is consistent with a tunnel blasting propulsion direction, does not need to independently open up a test area, and can obtain rock mass vibration data of a measurement point corresponding to a vibration measurement instrument after blasting only by arranging the vibration measurement instrument on an excavated tunnel bottom plate. According to the method, the rock mass average wave velocity can be rapidly and accurately calculated by combining the pre-measured error time of the vibration measurement instrument, the limitation of the arrangement distance of the vibration measurement instrument is broken through, the measurement range is remarkably expanded, the cost and difficulty of rock mass wave velocity measurement are effectively reduced, wave velocity measurement is completed under the condition that the original property of the rock mass is kept, and the rock mass average wave velocity is calculated. The actual rock mass condition of the blasting construction area is fed back in time, high adaptability to different rock mass properties is achieved, a scientific basis is provided for engineering design and construction, and safety and efficiency of construction are guaranteed.

The invention relates to the technical field of monitoring and intelligent decision analysis, and discloses a monitoring and intelligent decision system for the influence of holographic blasting vibration on slope stability, and the system comprises a data collection module, a data processing module, an intelligent decision module, and a feedback control module. The method comprises the following steps: dynamically deploying a three-dimensional vibration sensor array based on topographic features; collecting and preprocessing blasting vibration signals and geological data; fusing the physical model and the data driving model to predict vibration propagation; calculating a dynamic safety coefficient and generating a multi-objective optimization scheme; and outputting an early warning instruction and feeding back and adjusting the sensor network configuration. Through a three-dimensional dynamic monitoring network, real-time data processing, a hybrid drive prediction model, multi-target quantitative evaluation and hierarchical early warning regulation and control, the problems of incomplete sensor coverage, early warning delay, prediction deviation, evaluation deficiency and poor scene adaptability are solved, and accurate monitoring and intelligent decision making of blasting vibration influence are realized.

The invention discloses a double-row medium-length hole blasting method for a filling stope. According to the method, a theoretical value range of each blasting parameter is deduced according to field engineering practice, an ore rock blasting crushing mechanism and a blasting parameter theoretical formula. The method comprises the following steps of: constructing a stope blasting model with different hole bottom distances, explosive unit consumption and resistance line combinations by using ANSYS numerical simulation software in combination with physical and mechanical properties of ore rocks and field explosive performance parameters, and preferably selecting double-row medium-length hole blasting parameters according to a chamber effective stress distribution rule and a roof area vibration velocity peak value of the ANSYS blasting model. According to the method, by selecting the double-row optimal medium-length hole blasting parameters, it is guaranteed that ore rocks in the chamber are fully crushed, the effective stress invading the roof and the blasting vibration velocity peak value are reduced, and the roof is prevented from being damaged. And meanwhile, the explosive loading amount is reduced, the economic cost is reduced, and the production efficiency of a mine is improved.

The invention provides a mine slope risk assessment method and system based on multi-source data space-time fusion, and relates to the technical field of mine monitoring, and the method comprises the steps: obtaining geological and engineering data, and constructing a basic model representing a slope three-dimensional space structure; collecting multi-source heterogeneous monitoring data on a mine slope field, and aligning and fusing the multi-source heterogeneous monitoring data with the basic model in a space-time dimension to generate a continuous dynamic parameter field; and setting a data fusion model, and taking the basic model and the dynamic parameter field as input to obtain a four-dimensional space-time fusion risk field. According to the method, the basic model representing the three-dimensional space structure of the slope is constructed, and a unified space-time bearing base is provided for all heterogeneous monitoring data, so that originally isolated data such as GNSS displacement, deep displacement, blasting vibration and underground water level can be fused in a unified coordinate system; the problem of data splitting in the prior art is fundamentally solved, and integral and integrated expression of slope engineering and geological conditions is formed.

The invention provides a method for analyzing the dynamic stability of a slope containing a mining cavern group under the action of dynamic and static loads, and belongs to the technical field of analysis of the dynamic stability of the slope containing the mining cavern group. In order to solve the technical problem that complex mechanical behaviors of a slope containing a mining cave group under the dynamic and static load coupling effect are difficult to accurately consider because most of existing slope stability analysis aims at single static load or dynamic load working conditions, the technical scheme adopted by the invention comprises the following steps: collecting end slope mining coal pillars and rock samples of rock stratums overlying the end slope mining coal pillars, and testing physical and mechanical parameters of the rock samples; the physical and mechanical parameters of all the rock stratums are finally selected in combination with related production materials of the mining area; calculating a shear strength parameter of the rock mass after the rock is damaged under the action of cyclic blasting vibration; calculating shear strength parameters of the end slope mining area-evolved weak layer under the action of blasting vibration; the stability of the slope containing the mining cavern group under the blasting vibration effect is calculated; the method is applied to the stability analysis of the power of the slope containing the mining cavern group.

The invention relates to a blasting vibration rule prediction method and device, electronic equipment and a storage medium. Comprising the following steps: determining main factors influencing the blasting vibration speed and dimensions thereof based on monitoring data of a target blasting site; based on the main factors and the dimensions of the main factors, analyzing a Sadgowski formula by using a dimension analysis principle to determine target factors influencing the blasting vibration speed in the Sadgowski formula; constructing a function form of a blasting vibration rule, and taking the target factors as basic dimensions to obtain a common form of a Sadgowski formula; and based on a common form of a Sadgowski formula, fitting and constructing a new-form blasting vibration law prediction model so as to predict a propagation law of blasting vibration speeds in different directions. Therefore, the method solves the problems that a traditional Sadgowski formula cannot be suitable for complex engineering projects with consideration of influence factors such as a charging structure and a free surface, and cannot guide blasting near-area construction, and improves the effectiveness of blasting vibration prediction.

The invention discloses a comprehensive blasting method for controlling muck pile lumpiness and vibration in surface mine blasting, which comprises the following steps: S1, early-stage preparation and parameter calculation: investigating the geological condition of a mine in detail, and obtaining such information as rock physical and mechanical properties and joint fissure distribution; according to rock properties and mining requirements, blast hole parameters are determined; calculating initial delay detonation time according to a delay blasting rock breaking principle; s2, optimizing a charging structure: selecting a water medium interval charging structure, and determining the optimal unit consumption through theoretical analysis and field test; and based on the optimal unit consumption, establishing three-dimensional models of different water medium interval charging structures by adopting numerical simulation software, and selecting the optimal water medium interval charging structure. According to the method, the muck pile lumpiness is effectively controlled; the blasting vibration intensity is reduced; the blasting quality and the economic benefit are improved; the adaptability is high.

The invention belongs to the technical field of blasting vibration prediction, and particularly discloses a blasting vibration speed attenuation law prediction method based on regression analysis, and the method comprises the steps: laying monitoring points in multiple directions around a blasting source, synchronously collecting the geological parameters and mass point peak vibration speed of each point, and constructing a segmented propagation path; a single dominant propagation section is identified based on adjacent monitoring point data, independent influence of specific geological conditions on vibration attenuation is quantified by combining single-factor control regression analysis, decoupling modeling of geological factors and attenuation behaviors is realized, and meanwhile, a double-factor dominant propagation section is identified on the basis of quantizing the independent influence of the single geological conditions. And deducting a known effect through residual analysis to separate out an independent contribution of another geological parameter, and finally integrating a multi-factor influence function to construct a comprehensive attenuation model. Progressive modeling from single-factor decoupling to multi-factor collaboration is achieved, and the precision and physical interpretability of blasting vibration prediction in a complex heterogeneous stratum are remarkably improved.

The invention discloses a mine blasting safety monitoring and early warning system, which comprises a multi-source sensing module, an edge computing node, a dynamic risk assessment module, an intelligent early warning decision module and an emergency linkage execution module, and is characterized in that the multi-source sensing module acquires multi-dimensional data such as blasting vibration, shock waves and toxic gas through various sensors; edge computing nodes filter noise and extract time-frequency features; the dynamic risk assessment module inverts blasting energy based on vibration and shock wave data, and predicts secondary disasters in combination with a geological model and gas concentration; the intelligent early warning decision module dynamically adjusts an early warning threshold by using a deep learning model and fusing multi-modal data; the emergency linkage execution module executes instructions such as power failure and evacuation according to devices such as early warning level, linkage ventilation and personnel positioning, overcomes the defects of traditional single monitoring and lack of correlation models, realizes full-process and all-directional monitoring of blasting operation, and effectively improves the safety of mine blasting operation and the efficiency of emergency management.

The invention provides a mine vibration safety detection method and system during mining blasting. The method comprises the following steps: acquiring three-component signals of explosion fluctuation at a plurality of sampling points in a warning range through an intelligent sensor network during mine blasting; decomposing each three-component signal into a plurality of attenuation waves, determining cross correlation coefficients of the attenuation waves in the same component direction among different sampling points, and performing reverse time back propagation on each sampling point according to all the cross correlation coefficients to obtain a reverse time wave field of blasting vibration at each sampling point; inputting all the reverse time wave fields into a pre-trained multi-seismic-source positioning model to obtain a plurality of explosion points; and determining the risk value of miss-fire of each blast hole in the detonation network according to the coordinate positions of all the explosion points, and performing explosion aftershock evaluation on the mine according to all the risk values. According to the scheme, the miss-fire risk of each blast hole can be evaluated after mine blasting.

The invention discloses a filling mining method based on stage reverse medium-length hole groove broaching, which relates to the technical field of mining, and comprises the following steps: stope structure parameter design, mining preparation engineering construction, cutting engineering, stoping process and filling process are carried out in sequence, the cutting project comprises the steps of cutting courtyard rapid forming, cutting groove broaching and cutting groove rapid penetrating; and the cutting courtyard is rapidly formed by adopting a laser pre-splitting auxiliary blasting process. According to the method, the cutting and groove broaching processes are deeply optimized, so that the overall efficiency of underground mining is remarkably improved. In the cutting courtyard forming stage, the laser presplitting auxiliary blasting technology is introduced, so that the rock integrity is reduced, the blasting energy is more concentrated, the effect is more accurate, the problems of perforation, rock slag squeezing and the like in the traditional process are effectively avoided, the explosive loading amount can be reduced, disturbance of blasting vibration to surrounding rock is reduced, and the blasting quality is improved. And a safer technical support is provided for high-stress ore body mining.

The utility model discloses a surface mine high and steep slope monitoring device and belongs to the technical field of slope monitoring. The surface mine high and steep slope monitoring device comprises a base, the upper surface of the base is fixedly connected with a fixing rod in a penetrating mode, and the top end of the fixing rod is electrically connected with front-end data monitoring equipment. The front-end data monitoring device comprises a surface deformation monitoring device, a deep deformation monitoring device, a blasting vibration monitoring device, an underground water level monitoring device and a settlement monitoring device. Front-end data monitoring equipment is used for monitoring the deformation condition of the whole surface of a side slope, the inclination angle acceleration multifunctional instrument is installed on the side slope boulder outburst dangerous rock mass to monitor the stability condition of the side slope boulder dangerous rock mass, and the inclination angle acceleration multifunctional instrument assists in monitoring the development condition of side slope cracks and is arranged on the penetrating edge at certain intervals through deep clinometers. Monitoring the slippage condition of the fault zone in the drill hole of the potential fault zone of the slope, aiming at the surface mine slope subjected to blasting mining, mounting and arranging along a slope top platform according to a certain interval, and monitoring the overall damage degree of blasting operation to the mine slope.

The invention provides a tunnel blasting vibration reduction method and system based on asymmetric slotting, relates to the technical field of geotechnical engineering numerical simulation, and provides an integrated intelligent blasting vibration reduction system of asymmetric slotting structure optimization-quantum genetic algorithm collaborative optimization-model dynamic regulation. Triple fusion of a physical mechanism, a mathematical algorithm and an information technology of proximity tunnel blasting vibration control is creatively achieved, the bottleneck that a traditional method depends on experience trial and error, model static lag and vibration control and construction efficiency are difficult to consider at the same time is broken through, vibration speed control precision is improved, tunneling efficiency is improved, and meanwhile the construction efficiency is improved. The dynamic adaptability to complex geological conditions is remarkably enhanced, and a new generation of integrated solution is provided for safe, efficient and intelligent construction of the adjacent tunnel.

The invention relates to the technical field of tunnel engineering, in particular to a method and a system for optimizing design parameters based on unsymmetrical pressure tunnel blasting excavation dynamic response. The method comprises the following steps of obtaining geological parameters and construction parameters of surrounding rock of the bias tunnel, constructing a discontinuous structure equivalent damage model, and obtaining initial stress field distribution of a rock mass in combination with the discontinuous structure equivalent damage model, the geological parameters and the construction parameters; analyzing the time-varying modified dynamic mechanical parameters, and calculating through a stress wave propagation path and an energy dissipation rule to obtain a time travel curve of dynamic stress and vibration speed of each point of the surrounding rock in the blasting process; correcting a dynamic response index by using a self-adaptive artificial boundary condition to obtain a dynamic response state characteristic; and on the basis of the initial stress field distribution of the rock mass, the time history curve and the dynamic response state characteristics, optimal design parameters are obtained according to optimal design indexes. According to the method, the balance between the blasting vibration strength and the construction efficiency is realized by optimizing and adjusting the design parameters.

The invention provides a blasting vibration monitoring and influence range prediction method for a long and large tunnel in an environment sensitive area, and relates to the technical field of blasting safety monitoring and vibration control in tunnel engineering, and the method comprises the steps: constructing a three-dimensional dynamic monitoring network, collecting multi-source data based on the three-dimensional dynamic monitoring network, carrying out the preprocessing of the multi-source data, obtaining a blasting vibration feature matrix, and carrying out the prediction of the influence range. Building a prediction model based on the improved LSTM model, training the prediction model based on a preset blasting vibration characteristic matrix and blasting parameters and geological conditions corresponding to the preset blasting vibration characteristic matrix to obtain a trained prediction model, and inputting a to-be-detected blasting vibration characteristic matrix and blasting parameters and geological conditions corresponding to the to-be-detected blasting vibration characteristic matrix into the trained prediction model. And obtaining a prediction result. According to the method, the precision and the real-time performance of blasting vibration monitoring and prediction are remarkably improved, the vibration control problem in blasting construction of the long and large tunnel in the environment sensitive area is effectively solved, the prediction precision is high, the real-time performance is high, the adaptability is high, and the data utilization efficiency is high.

The invention relates to the technical field of tunnel blasting vibration monitoring, in particular to a tunnel blasting vibration monitoring and early warning integrated system based on lining crack conditions. According to the method, blasting vibration characteristic data are extracted from triaxial vibration signals of different key points in the tunnel blasting process, correlation analysis is carried out on the blasting vibration characteristic data and vibration characteristic data of corresponding reference points to judge the relevance between blasting vibration and crack response, and accurate judgment on the relation between blasting vibration and lining cracks is achieved; when blasting vibration and crack response have relevance, a dynamic safety threshold value is determined according to the lining crack health degree before tunnel blasting, and the dynamic safety threshold value is compared with vibration characteristic data in real time to trigger graded early warning; when blasting vibration and crack response are not correlated, the change rate of crack characteristic data is analyzed through comparison of lining crack conditions in multiple periods, the autonomous extension risk of cracks is predicted, reinforcement early warning is triggered, timely and reliable guarantee is provided for tunnel construction and operation safety, and lining cracks are effectively prevented from deteriorating.

The invention discloses a tunnel blasting vibration prediction method suitable for a sand shale interbed area, and mainly relates to the technical field of tunnel blasting. Comprising the following steps: S1, determining key factors influencing the vibration velocity; s2, building a blasting vibration prediction model in the sand shale interbed area; s3, carrying out data normalization processing; s4, introducing a BP neural network to train the prediction model; s5, introducing a particle swarm optimization algorithm and initializing related parameters of a particle swarm; s6, improving a particle swarm optimization algorithm; s7, a sand shale interbed tunnel blasting vibration prediction model of the improved particle swarm optimization BP neural network is obtained, the model is combined with blasting characteristic parameters of specific sand shale interbed area tunnel engineering for prediction, and a blasting effect prediction result is obtained; according to the method, important theoretical guidance can be provided for blasting vibration prediction of the stratified structure with changeable rock strata, and a practical safety evaluation tool is provided for ground surface vibration velocity control of similar projects.

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

The invention discloses a blasting vibration monitoring method and system based on vision and vibration sensors, and relates to the technical field of blasting vibration monitoring. An aerial vision monitoring device is arranged at a set height right above a blasting point, and a vibration sensor array is radially arranged around the blasting point; and the visual monitoring device and the vibration sensor array are in wireless communication connection with the blasting vibration analysis module based on the computer. Omnibearing monitoring of the blasting process is achieved by combining the aerial vision monitoring device and the ground vibration sensor array, ground vibration data can be captured, and the scattering condition of fragments generated by explosion can be observed. An image feature recognition algorithm is used for analyzing an image captured by a high-speed camera, and visual evidence is provided for understanding the explosion effect. Complex vibration data and visual feature changes can be converted into charts and schematic diagrams which are easy to understand through image processing software and a data visualization tool, and non-professionals can conveniently and rapidly grasp key information.

The invention provides a blasting vibration waveform feature extraction and damage range intelligent calibration system. The method comprises the following steps: calculating a waveform irregularity sequence based on original vibration waveform data; then, a fiber grating sensor network is used for collecting strain vibration waves on the surface of the structure, and spatial distribution information of the strain change rate of the structure is mapped into a feature enhancement channel; learning a feature distribution mode based on the waveform data processed by the feature enhancement channel by using a coding and decoding structure, and performing reconstruction operation on the current blasting vibration waveform according to the feature distribution mode; identifying and amplifying abnormal feature differences by comparing an actual waveform irregularity sequence with a reconstruction sequence, and generating reconstruction error peak distribution; and finally, when the strain gradient abrupt change point position detected by the optical fiber sensor network is overlapped with the reconstruction error peak point space, generating a blasting damage area thermodynamic diagram based on the overlapped point space density. The precision and reliability of blasting damage range calibration are improved.

The invention discloses a non-linear static analysis method for influence of blasting vibration on surrounding buildings based on field measurement vibration velocity, and belongs to the technical field of engineering blasting. The safety evaluation method comprises the following steps: step 1, field detection and data acquisition; the vibration velocity condition of the target structure and a blasting seismic wave velocity vibration curve of the blasting at the target structure are obtained; 2, calculating a structure inelastic quasi-absolute acceleration-relative displacement SA, e-SD, e curve, namely a demand curve of a target structure; 3, establishing a multi-degree-of-freedom structure model, performing static pushing analysis on the multi-degree-of-freedom structure model to obtain a load and displacement response relation V-D curve of the structure, and performing deformation to obtain a capacity curve of the equivalent SDOF structure; and 4, moving the demand curve and the capability curve of the target structure into the same coordinate system, wherein the intersection point of the demand curve and the capability curve reflects the local damage condition of the structure. By adopting the method, whether the structure is integrally damaged or not can be judged, and the local damage condition and position can be analyzed.

The invention provides an intelligent decision-making method for tunnel micro-perturbation blasting, and relates to the technical field of tunnel and underground engineering blasting construction.The method comprises the steps that a Sadgowski empirical formula is improved by introducing geologic structure factors and ground stress factors, and a micro-perturbation response prediction model is obtained; the peak velocity of a mass point of a blasting vibration waveform is predicted through a micro-perturbation response prediction model, a three-objective optimization function is constructed, a blasting decision variable group is introduced, the three-objective optimization function is solved by applying an NSGA-III non-dominated sorting genetic algorithm, and a blasting result is obtained. The non-dominated solution set corresponding to the optimal blasting decision variable set and the peak velocity, the dominant frequency and the blasting damage area depth of mass points of the corresponding blasting vibration waveform are obtained, the problem that the coupling effect of a geological structure and ground stress is ignored in existing tunnel blasting is solved, and the method is suitable for intelligent decision making of deep tunnel blasting.

The invention discloses a blasting vibration and dust control-oriented multi-objective collaborative optimization parameter setting method, which comprises the following steps of: training a neural network model capable of predicting a complete vibration time travel curve and a three-dimensional dust concentration field on the basis of a training sample containing geological conditions, blasting parameters and an actual blasting effect; the model is used for carrying out multi-target optimization on a target function containing four indexes of vibration, dust, cost and regulation compliance through a preset evolutionary algorithm, a Pareto optimal solution set composed of a plurality of non-dominated parameter schemes is obtained, the schemes in the optimal solution set are analyzed through a preset explanation algorithm, and the optimal solution set is obtained. The influence and contribution degree of each blasting parameter on a prediction result are quantified, so that a black box model is converted into an interpretable white box decision; according to the method, the problems that a traditional method is difficult to balance multiple conflict targets and the decision-making process is opaque are solved, and an optimal blasting parameter scheme considering safety, environmental protection and cost effectiveness is provided for blasting operation.