594 results about "Stress wave" patented technology

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.

The invention discloses a bolt on-line monitoring method and system, and the method comprises the steps: generating a micro-motion feature matrix of a bolt according to a multi-dimensional vibration signal and a stress wave propagation characteristic of a bolt connection part; based on the micro-motion characteristic matrix, fusing local stress distribution data acquired by a bolt surface strain gauge, and outputting a health state quantitative index of the bolt; outputting a loosening risk level according to the health state quantitative index and historical loosening evolution data; and based on the looseness risk level and in combination with equipment operation state parameters, a self-adaptive decision tree model is adopted to dynamically adjust monitoring frequency and an alarm threshold value, a real-time monitoring strategy and a visual early warning report are generated, and a wireless transmission module is synchronously triggered to upload the real-time monitoring strategy and the visual early warning report to a cloud operation and maintenance platform. According to the embodiment of the invention, high-precision, self-adaptive and traceable bolt health state evaluation and early warning can be realized.

The invention, which relates to the industrial circuit board monitoring technology field, discloses an industrial circuit board operation abnormity monitoring system comprising a multi-sensor welding spot data acquisition module, a signal processing module, an element welding spot mechanical fatigue monitoring module, a welding spot fatigue fault grading early warning module, and an early warning and response module. Data fusion is carried out on the acoustic emission sensor, the strain gauge sensor and the ultrasonic sensor, the state of the welding spot is monitored synchronously from three dimensions of stress acoustic emission, mechanical deformation and internal defects, the strain gauge sensor captures tiny deformation around the welding spot, and the ultrasonic sensor detects internal potential defects. The acoustic emission sensor senses stress release signals, the three kinds of sensor data are subjected to deep fusion analysis through the D-S evidence theory, the problem of misjudgment caused by one-sided information is effectively avoided, stress waves during crack propagation are captured through the acoustic emission signals, and faults can be diagnosed in time in combination with deformation data of the strain gauge sensor.

The invention discloses a tunnel blasting simulation method and system for a karst area, and relates to the field of tunnel blasting simulation, and the method comprises the steps: building a three-dimensional geologic model of the karst area, and generating an initial ground stress distribution rule through numerical simulation; generating refined initial crustal stress field data; calculating stress wave intensity and time sequence data generated by explosion; acquiring stress wave propagation data; calculating the surrounding rock damage degree and the crack initial position and direction; simulating the surrounding rock crack propagation process, and calculating the penetration probability and the propagation speed of the crack near the karst cave in combination with the karst cave characteristic parameters; analyzing the surrounding rock stability and damage evolution trend of the karst cave area; and optimizing explosive source item parameters and stress wave propagation boundary conditions, and generating a karst area tunnel blasting simulation result. According to the method, the influence of the karst cave on stress wave propagation is accurately simulated, powerful support is provided for optimizing a blasting scheme and improving construction safety and efficiency, and the method has important guiding significance on tunnel engineering under complex geological conditions.

The invention discloses a system and a method for predicting the fatigue life of a flange by utilizing ultrasonic flaw detection, and belongs to the field of artificial intelligence. Comprising an aggressive frequency domain spot feature extraction module, a microdefect enhancement recognition module, an acoustic echo database training module, a damage accumulation-stress wave mapping model construction module, an active echo disturbance injection module, a fatigue life progressive prediction module, a real-time feedback closed-loop control module and a multi-scale self-checking redundancy mechanism module. Spot feature extraction and defect identification share high-frequency phase data to generate a focusing map, and registration with historical defect features is carried out in a database; the mapping model constructs a stress-damage relation according to the stress concentration coefficient and the frequency domain characteristics; the disturbance injection module applies directional excitation to potential fatigue points, and the prediction module constructs a life evolution curve based on response; the feedback control module fits and corrects the database and the disturbance strategy; the self-checking mechanism improves the prediction robustness and self-adaptability through waveform deviation and mismatch correction. The beneficial effects are that the structure safety and the service life management intelligence are improved.

The invention discloses a rock burst global monitoring and dynamic and static far and near field cooperative early warning method and system, and the early warning method comprises the steps: determining a stope space key monitoring region and a stress wave type based on a dynamic load stress wave far field generation and propagation mechanism, delineating a working face surrounding rock high-stress region, and analyzing the structural characteristics of a rock stratum. Arranging a micro-seismic sensor in a far-field key area, and obtaining parameters such as seismic source fracture size, energy and magnitude through waveform inversion; an acoustoelectric system is arranged in a near-field high-stress area, coal rock fracture characteristics are dynamically monitored, and the static load energy storage state of surrounding rock is quantitatively analyzed. And in combination with a stress wave propagation attenuation coefficient, a far and near field fusion early warning criterion under the action of dynamic and static energy superposition is established, and advanced accurate early warning of the rock burst is realized through collaborative analysis of multi-source monitoring data. The disaster-pregnant space global monitoring technology fusing earthquake, sound and electricity is provided, the chain evolution disaster-causing process of dynamic load generation, cross-space propagation and working face surrounding rock damage is considered, and the accuracy of rock burst early warning can be remarkably improved.

The invention discloses a method for grabbing an object by a robot, which comprises the following steps: acquiring surface vibration, transient deformation and three-dimensional deformation data of the object through multi-point pulse excitation, and generating stress wave characteristic mapping data; analyzing internal propagation characteristics of the object according to the stress wave characteristic mapping data, identifying a stress transmission channel, determining a material boundary position and a deformation sensitive area, and generating internal structure characteristic data; calculating a deformation risk coefficient and a stability contribution degree according to the internal structure characteristic data, selecting a grabbing position, and generating grabbing strategy data; and according to the stress wave characteristic mapping data and the grabbing strategy data, the inherent frequency and the local deformation characteristic are identified, a force loading sequence is designed, the vibration state and the deformation amount are monitored, and the force loading sequence is dynamically adjusted. According to the method, the deformation behavior of the object can be accurately predicted, the optimal grabbing strategy is determined by analyzing the internal structure characteristics of the object, dynamic force control is achieved based on frequency modulation, and the instability problem in the grabbing process of the deformed object is effectively solved.

The invention relates to the technical field of digital twinning, in particular to a digital twinning fused power grid fault prediction system and method, and the system comprises a state sensing module, a path deduction module, a twinning mapping module and a prediction execution module. According to the method, multi-source heterogeneous sensing data are fused, a space-time alignment algorithm is executed to construct a structural state tensor, Laplacian operation is combined with a stress wave propagation model to analyze non-rigid deformation characteristics, a load transfer path atlas is generated to reveal recessive mechanical association, Young modulus is dynamically corrected through a finite element grid to form a structural degradation coefficient matrix, and the structural degradation coefficient matrix is obtained. The time sequence difference and the long short-term memory network are combined to capture the equipment degradation trend, the Kolmogov test is used to quantify the fault space-time distribution probability, the power grid equipment jumps from single threshold early warning to multi-physics coupling prediction, the fault prediction space-time resolution and dynamic adaptability are improved, the artificial experience dependence is reduced, and the fault prediction efficiency is improved. And the robustness of the complex working condition system is enhanced.

The invention discloses a bridge engineering-oriented foundation pile quality detection system, which relates to the technical field of engineering quality detection, and comprises a data acquisition module configured to acquire detection data including foundation pile construction parameters, geological environment data, stress wave signals and geometric dimension parameters, the wave velocity correction module is configured to correct a standard wave velocity through a dynamic coupling algorithm, the defect depth calculation module is configured to calculate the defect depth of a foundation pile defect, and the credibility evaluation module is configured to output a credibility correction coefficient and feed back the credibility correction coefficient to the wave velocity correction module by quantifying errors of detection data; according to the method, through multi-physical field wave velocity correction, pile diameter-defect depth double correction and credibility quantitative iteration, a high-precision, objective and traceable integrated solution is systematically provided for bridge engineering foundation pile quality detection.

The invention provides a tunnel blasting scheme determining method and device, and relates to the technical field of tunnel engineering data processing.The method compensates stress wave attenuation caused by large-aperture blast hole air gaps through radial explosive density gradient design, so that the energy utilization rate is higher; the axial charge density is dynamically adjusted on the basis of a three-dimensional clamping coefficient, the problem that energy of a hole bottom high constraint area is insufficient is solved, the explosive density and the hole net topology are collaboratively optimized, the contour forming quality is greatly improved, back break is reduced, and therefore the cost is reduced, and the time is shortened. Due to the fact that explosive and hole net parameters are optimized, energy distribution is more reasonable, blasting disturbance is reduced, stability of surrounding rock is enhanced, safety in a hole is greatly improved, and casualties are reduced.

The invention discloses a tunnel monitoring method and system based on a vibrating wire sensor, and relates to the technical field of tunnel engineering construction safety monitoring, and the method comprises the steps: collecting the vibration frequency data and spatial position coordinates of the vibrating wire sensor in a monitoring region; the method comprises the following steps: calculating a time difference and an energy attenuation ratio between adjacent sensors by extracting spectrum energy density distribution, and constructing a stress wave propagation field; dividing grids in the propagation field to calculate node stress values, generating a stress field distribution cloud picture and extracting a stress concentration area; calculating a gravity center position coordinate change rate and a stress time sequence curve of the stress concentration area, and determining fracture extension parameters; and calculating a surrounding rock bearing capacity attenuation curve based on the parameters, and outputting an early warning signal when the slope exceeds a threshold value. According to the invention, real-time monitoring and early warning of the tunnel surrounding rock instability risk are realized.

The invention discloses an evaluation and improvement method and device for a tunnel blasting effect and a medium, and relates to the technical field of tunnel engineering.The evaluation and improvement method comprises the steps that a tunnel coupling data set is collected, space registration and three-dimensional shape reconstruction are conducted, fracture space distribution is formed, risk probability mapping is conducted on the fracture space distribution, and a fracture risk thermodynamic diagram is output; the fracture risk thermodynamic diagram and tunnel rock mass data are input into a digital twin blasting model, a load mapping layer carries out load gradient distribution, a rock mass response deduction layer carries out stress wave propagation simulation and damage accumulation quantification, an optimized blasting number instruction set is generated, and a drill jumbo is driven to execute blasting operation and vibration monitoring according to the optimized blasting instruction set. And actual blasting effect parameters are obtained. According to the method, accurate simulation of geological conditions and optimal adjustment of blasting parameters are realized through the digital twin blasting model, and meanwhile, refined and interpretable evaluation of the blasting effect is realized through vibration monitoring and error propagation modeling of the actual blasting effect.

The invention discloses a blast furnace cooling wall damage probability prediction method, which relates to the field of industrial equipment maintenance, and comprises the following steps: arranging a temperature sensor array, a vibration sensor and an acoustic emission sensor, and collecting temperature gradient data, mechanical vibration frequency and high-frequency stress wave signals in real time; calculating the slag crust thickness of the hot surface of the cooling wall based on an unsteady state heat transfer equation, introducing an in-furnace gas flow parameter correction model, and generating a dynamic slag crust thickness distribution cloud picture; establishing temperature and vibration reference threshold values under different working conditions according to historical operation data of the cooling wall, and detecting temperature over-limit accumulated duration and a vibration energy spectrum abnormal frequency band in real time; taking the temperature gradient range, the slag skin thickness variation coefficient and the vibration dominant frequency offset as input characteristics, training a multi-dimensional coupled damage probability prediction model through a random forest algorithm, and outputting a recent dynamic damage probability value of each section of cooling wall; and dividing risk grades according to the damage probability value, dynamically adjusting a threshold interval in combination with the real-time smelting strength of the blast furnace, and generating a differentiated maintenance suggestion set.

The invention relates to the technical field of vibration monitoring, in particular to a self-powered transmission line fitting aeolian vibration damage diagnosis system and method, and the system comprises a sensing collection module, a signal decoupling module, a damage identification module, a damage association module and a risk assessment module. According to the method, stress wave velocity and acceleration data are synchronously collected, time alignment is implemented, feature coupling precision is enhanced, wave crest offset and energy density are respectively extracted by using moving average filtering and wavelet transform, effective data segments are dynamically screened, and environmental noise interference is suppressed. A stress wave propagation change rate is quantified based on a path attenuation model, a continuous energy abnormal node is matched to realize damage positioning, a breeze response abnormal region is identified by combining vibration direction change and a signal envelope offset degree, multi-dimensional features are coded and subjected to risk judgment through a neural network, a damage positioning and risk assessment closed-loop framework is formed, and the risk assessment accuracy is improved. And the spatial resolution and evaluation precision of aeolian vibration damage identification under complex working conditions are significantly improved.

The invention discloses a drilling construction intelligent decision support and risk early warning method, device and equipment, and the method comprises the steps: collecting drilling sound wave response data, carrying out harmonic analysis to recognize a reservoir resonance node, and constructing a reservoir energy conduction network; a fluctuation propagation path is tracked based on an energy conduction network, and a construction risk area is identified through stress wave spectrum features; porosity and permeability data are extracted for phase difference analysis, a seepage-construction mismatch factor is generated, and a construction optimization window is actively generated; constructing a geological potential energy gradient field to determine an overflow influence range, and performing tensor coupling on multi-dimensional risk factors to generate a three-dimensional construction decision matrix; an optimal construction scheme is generated through energy minimization and risk-efficiency dynamic balance processing; construction parameter response changes are monitored in real time, abnormal parameter combination symptoms are recognized, a grading risk early warning instruction is generated, and intelligent decision support and risk early warning of drilling construction are achieved.

The invention relates to the technical field of intelligent operation and maintenance, in particular to a bridge maintenance system based on data analysis, which comprises a multi-source data acquisition module, a fatigue degradation monitoring module, a wave velocity anomaly positioning module, a risk collaborative judgment module and a maintenance task planning module. According to the method, structural fatigue characteristics are subjected to cross validation by integrating two-dimensional data of a strain sequence and a stress wave propagation parameter, and a dynamic window range deviation degree is adopted to replace a static threshold value to establish a datum reference linked with a bridge bearing period; a same-path propagation difference value is extracted by using a high-frequency and low-frequency stress wave frequency band energy separation technology to detect continuous overrun, and a degradation trend weight and a priority list of damage space distribution are generated in combination with multi-index coexistence verification and a hierarchical sorting strategy; resource allocation is optimized by integrating a multi-level response mechanism of emergency repair coordinate positioning, directional detection and regular inspection, accurate crack positioning is achieved, the early degradation trend capturing capacity is enhanced, the misjudgment risk is reduced, and an operation and maintenance response chain is shortened.

The invention discloses a natural gas pipeline leakage detection method based on edge calculation, relates to the technical field of natural gas pipeline leakage detection, and remarkably improves the stability of edge side leakage detection through double innovations of dynamic multi-scale perception and space-time cooperative verification: a dynamic window mechanism breaks through the limitation of a fixed analysis window; through dual triggering of the energy accumulation rate and the pulse density, feature adaptability extraction of leakage events with different intensities is realized, and the missing report rate of tiny leakage is reduced; spatio-temporal fingerprint verification is combined with matching of pipeline stress wave propagation characteristics and sensor types, a physical conduction chain credibility evaluation system is constructed, and false alarm diffusion caused by regional interference is effectively inhibited; the environment entropy dynamic correction and the sensitivity strategy pool form closed-loop optimization, so that the system keeps stable detection performance under complex working conditions such as construction vibration and meteorological sudden change.

The invention provides a coal mine roof dynamic monitoring and early warning method and system based on artificial intelligence, and relates to the field of coal mine safety, and the method comprises the steps: collecting a stress waveform and a displacement waveform of a coal mine roof, extracting stress loosening characteristics, and constructing a three-dimensional stress conduction strength field; identifying a stress sudden change transition point by using a recurrent neural network to form a stress conduction chain; analyzing a regional topology migration rule and extracting a stress field instability path; matching with historical data to determine a prediction window; and continuously verifying the instability path and generating a support reinforcement scheme. The coal mine roof instability risk can be predicted in advance, and the early warning accuracy and timeliness are improved.

The invention provides a real-time three-dimensional city modeling and dynamic updating method and system based on deep learning. According to the method, building structure static load parameters of a target building are obtained, a micro-seismic sensor array is deployed at a node of a bearing wall and a frame beam column, and stress wave propagation path data are collected at a preset sampling frequency. And synchronously receiving electrical load data uploaded by the intelligent electric meter, and extracting a current abrupt change characteristic section in a time window corresponding to the abnormal vibration event. Building structure static load parameters, stress wave frequency domain energy distribution and the phase angle offset of the current abrupt change characteristic section are input into the deep learning model, the association rule of structural mechanical response and electrical load disturbance is coupled, and a three-dimensional model updating instruction is generated. And dynamically adjusting the grid vertex coordinates of the target building in the initial three-dimensional city model based on the instruction, and overlapping and displaying the space influence range boundary of the current abrupt change feature segment. The technical scheme provided by the invention is based on real-time three-dimensional city modeling and dynamic updating of deep learning.

The invention discloses a secant pile nondestructive testing system and method based on multi-source sensing data fusion, and belongs to the technical field of civil engineering detection, the full pile long strain is axially monitored through an optical fiber, three-dimensional imaging and stress wave global scanning of a secant interface are realized through a sound wave transmitting-receiving probe pair, line-plane-body multi-level detection is formed, and the secant pile nondestructive testing system and the secant pile nondestructive testing method based on multi-source sensing data fusion are obtained. Through cooperative complementation of strain / temperature data, stress wave data and wave velocity three-mode data, defect type probabilistic recognition is achieved, the weight of each sensor is dynamically adjusted according to the real-time environment, and the defect comprehensive probability is calculated and output. And dynamic weight distribution is realized. Through multi-source sensing data fusion analysis, pile body integrity, occlusion interface compactness and temperature strain distribution characteristics can be accurately identified, and the method has the characteristics of multiple detection dimensions, high precision and strong interference resistance.

The invention relates to the technical field of mobile pump collaborative operation, and particularly discloses a tandem type multipurpose crawler mobile pump collaborative operation system which is characterized in that the system acquires pressure data through a pressure drop capturing module and analyzes a pressure-bearing compensation value, and whether a low-pressure trap appears or not is judged; a margin analysis module is combined with the compensation efficiency coefficient to dynamically calibrate the collaborative cavitation margin; the cavitation trend is recognized based on the cavitation proximity by means of a controllable analysis module, and when cavitation forms positive feedback, hierarchical collaborative optimization is conducted on the moving pump; and finally, a coherence model of pressure pulsation and stress waves is established based on cavitation collapse characteristics through a conduction verification module, and a low-pressure trap improvement degree model is established to evaluate the improvement degree. According to the method, the cavitation fault of the series pump set can be prevented, pressure loss under complex working conditions can be dynamically adapted, and collaborative design and operation of the series pump set are optimized.

The invention discloses a surrounding rock tunnel blasting dynamic simulation method, and belongs to the technical field of data simulation. The method comprises the following steps: acquiring three-dimensional geological data; constructing an initial mechanical parameter field of the surrounding rock based on the three-dimensional geological data; based on the initial mechanical parameter field, discretizing the surrounding rock area by adopting a finite element method, dividing grids and initializing mechanical attributes of each unit; based on the mechanical properties, blasting simulation is conducted, in the simulation process, the stress distribution state of the surrounding rock is monitored in real time, and whether the stress concentration phenomenon exists or not is judged; calculating a propagation path and an attenuation rate of a stress wave generated by explosive explosion in the surrounding rock through numerical simulation based on a judgment result; based on the propagation path and attenuation rate of the stress wave, the fracture mode and fragment distribution characteristics of the surrounding rock are analyzed; and obtaining a final blasting simulation result based on the stress distribution state, the fracture mode and the fragment distribution characteristics of the surrounding rock. The method can reflect the dynamic response of the surrounding rock and predict the blasting effect.

The invention belongs to the technical field of equipment monitoring, and discloses a stress wave sensing-based measurement method and system applied to a wind power blade. Comprising the following steps: acquiring stress wave signal data, preprocessing the stress wave signal data, and sequentially performing bipolar cooperative decomposition, error correction and environmental interference suppression on the stress wave signal data to obtain perfect stress wave signal data; performing feature extraction on the perfect stress wave signal data, generating an intermediate feature matrix, performing further feature screening on the intermediate feature matrix, and outputting stress wave signal feature data; constructing a damage assessment model based on the stress wave signal feature data, and performing damage assessment on the wind power blade by using the damage assessment model to obtain a damage assessment record; generating a three-dimensional damage map based on the damage evaluation record, and sending the three-dimensional damage map to a preset wind power equipment safety terminal; equipment monitoring on the wind power blade is realized by using the stress wave.

The invention relates to the technical field of bio-fertilizer crushing, and discloses a dynamic stress regulation and control method of a crusher in a bio-fertilizer production process, which comprises the following steps: arranging a non-uniform gradient microgroove array arranged according to a Fibonacci sequence on a working surface of a cutter, filling a beta-glucan-based viscoelastic composite material in the microgrooves, the grain orientation of a tool matrix is regulated and controlled through an electromagnetic field-assisted directional solidification process, and a dynamic stress wave self-interference regulation and control system is constructed. Through the synergistic effect of geometric arrangement of the microgroove array and the nonlinear damping characteristic of the viscous-elastic material, the destructive interference effect of stress waves is spontaneously formed in the crushing process, and space equalization and peak suppression of dynamic stress of the working face of the cutter are achieved; the problems of stress concentration and tool fatigue of a traditional crusher in high-fiber and high-viscosity bio-fertilizer raw material processing are fundamentally solved, the crushing efficiency is remarkably improved, and the service life of equipment is remarkably prolonged.

A distributed composite geotechnical sensor disclosed by the present invention comprises a multi-core cable, an optical fiber sensing member, at least two composite sensing members and a plurality of connectors, the multi-core cable is used for transmitting charge signals, and the optical fiber sensing member and the multi-core cable are arranged in parallel and at an interval and are used for monitoring temperature and stress. The at least two composite sensing pieces are sequentially arranged at intervals along the length direction of the optical fiber sensing piece, are arranged in the same core with the optical fiber sensing piece and are used for monitoring dynamic stress waves or vibration, and the plurality of connecting bodies are respectively arranged at two ends of the composite sensing pieces and are connected with the multi-core cable, the optical fiber sensing piece and the composite sensing pieces. The electric connector is used for electrically connecting the multi-core cable with the composite sensing piece. According to the invention, the problems of relatively single monitoring index and incapability of positioning due to the fact that a traditional piezoelectric geotechnical cable can only monitor external disturbance information and cannot realize distributed monitoring can be effectively solved.

The present application relates to the technical field of pyrotechnic impact equipment, and more particularly to a pyrotechnic impact isolation method using honeycomb porous materials. The present application uses the complexity of the transmission path of honeycomb porous materials to construct the satellite-rocket interface, increase the stress wave impedance of the satellite-rocket interface, reduce the stress wave transmissivity, and achieve the isolation of the pyrotechnic impact response. The present application uses the explicit finite element method to model and analyze the transient dynamics of the relevant structure, and studies the pyrotechnic impact isolation characteristics of honeycomb porous materials. The present application can effectively reflect the stress wave, dissipate the impact energy, achieve the effective isolation and suppression of the pyrotechnic impact, and improve the method of traditional satellite-rocket connection.

The invention provides an impact deformation feature extraction method based on high-speed camera shooting and image processing, and the method comprises the steps: collecting the transient deformation data of the edge package of a remote controller housing under impact through high-speed camera shooting, recording the change speed of a strain rate, obtaining the material characteristics, the size of a package structure, and the geometric discontinuous features of an opening and closing part, generating continuous frame sequence images; performing strain field data quantification processing on the third visualization result, extracting strain field characteristic data, and judging influence weights of stress wave reflection on strain fields of different functional areas of the shell in combination with a falling height and an impact angle of the remote controller; and improving edge packaging design parameters according to the deformation buffer effect and discontinuous quantitative indexes at the opening and closing positions, verifying the improvement amplitude of the deformation buffer effect, and obtaining strain field distribution data of the optimized shell.

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 discloses a tunnel precise drilling and blasting design method integrating multi-source intelligence and closed-loop optimization. The method comprises the steps that geological parameters are collected through geological detection equipment; based on fracture mechanics and rock mass damage theories, constructing a quantitative relation model reflecting joint network distribution, stress wave propagation characteristics and a rock mass crushing effect coupling mechanism; inputting the geological parameters into a pre-constructed multi-dimensional knowledge base, integrating historical engineering data association rules by the multi-dimensional knowledge base by using a graph database, and extracting geological features; based on a deep learning model, analyzing the mapping relation between the geological features and the blasting parameters, and generating a preliminary blasting scheme including the blast hole distance, the charging structure and the detonating mode; according to the method, a closed-loop optimization system of geologic features-blasting parameters-effect evaluation is constructed, so that dynamic intelligent decision-making and construction self-adaptive regulation and control of tunnel drilling and blasting design are realized, and the contour forming precision and the blasting efficiency are remarkably improved.

The invention provides a three-axis six-direction impact test simulation method and system based on an F-DEM, and the method comprises the steps: building a full-size model frame, coupling the F-DEM, carrying out the dynamic solving of a model, and carrying out the post-processing of automatic data. The invention further provides a triaxial six-direction impact test simulation system based on the F-DEM, a three-dimensional full-scale numerical model is established based on basic parameters of a waveguide rod and a rock sample in a TEHB test system, and the three-dimensional full-scale numerical model comprises a discrete element rock sample model and a finite element waveguide rod model. According to the method, efficient coupling modeling of the finite element waveguide rod and the discrete element rock sample is achieved, the method has good stress wave propagation precision, the dynamic response behavior of the rock sample under triaxial six-direction loading can be accurately simulated, and efficient and verifiable technical support is provided for impact response analysis and engineering application research of the rock mass in a complex stress environment.