1287 results about "Seismology" patented technology

The invention discloses a real-time surrounding rock deformation monitoring and data acquisition method and system, which is applied to long-distance weak surrounding rock tunnel construction, and comprises the following steps: determining the dynamic change trend of underground water seepage rate and ground stress distribution gradient by adopting a time sequence analysis method; based on the trend, carrying out risk partitioning on the tunnel construction section by adopting a K-means clustering algorithm, determining a deformation sensitive area, and optimizing the spatial distribution of the monitoring points according to the deformation sensitive area; monitoring data are acquired in real time, and when the data fluctuation period exceeds a threshold value, the data acquisition frequency of the corresponding monitoring point is automatically improved; processing high-frequency acquired data by adopting a long-short-term memory network to obtain a real-time surrounding rock deformation prediction result; the prediction result and the multi-source real-time geological parameters are fused, a Bayesian updating method is adopted for processing, a quantitative surrounding rock stability evaluation result is obtained, closed-loop self-adaptive optimization of a monitoring scheme and accurate risk prediction are achieved, and the safety early warning capacity of tunnel construction and the utilization efficiency of monitoring resources are remarkably improved.

The invention belongs to the technical field of mine safety monitoring and geotechnical engineering stability evaluation, and relates to a mine filling body performance detection and stability evaluation method and system.The method comprises the steps that S1, a first type of monitoring signals are collected in real time through a distributed optical fiber sensor array, and meanwhile vibration waveform signals serve as second type of monitoring signals; s2, inputting the first type of monitoring signals and the second type of monitoring signals into a multi-source data fusion model, and generating fusion state signals representing the three-dimensional damage degree and the real-time deformation state of the filling body; s3, inputting the fusion state signal into a parameter dynamic identification module to generate an updated numerical model parameter signal; and S4, based on the updated numerical model parameter signal, executing stability quantitative calculation to generate a risk level signal. According to the mine filling body performance detection and stability evaluation method and system, the problem of inaccurate psychological state recognition such as large individual difference, strong environmental interference and weak dynamic adaptability can be solved.

The invention discloses a geological disaster monitoring system based on multi-modal data, and relates to the technical field of geological disaster monitoring, and the system comprises a crack analysis module which carries out the time-space correlation mining of the crack propagation rate of a monitoring region, and analyzes the nonlinear evolution characteristics and spatial differentiation rules of rock mass fracture; and the critical identification module is used for performing wavelet packet energy spectrum analysis on the inclination angle change rate of the geologic body, identifying a critical turning point of rigidity attenuation of the geologic structure in combination with a preset algorithm, judging whether the overall stability enters an instability acceleration stage or not, and performing multi-parameter collaborative detection, crack evolution cross validation and dynamic trend stability verification to obtain the stability of the geologic structure. The accuracy and reliability of geological structure rigidity attenuation critical turning point recognition are remarkably improved, a more accurate rigidity attenuation stage judgment basis is provided for an early warning module, and the capturing capacity of a multi-modal data geological disaster monitoring system for structure instability precursor is enhanced.

The invention relates to the technical field of route planning, in particular to an underground cavern inspection method and system. The method comprises the following steps: acquiring geological survey data, micro-seismic monitoring data and historical deformation monitoring data; according to the geological survey data and the micro-seismic monitoring data, rock mass viscoelasticity local modeling is carried out, and a rock mass viscoelasticity model is obtained; performing deformation trend prediction according to the rock mass viscoelastic model and the historical deformation monitoring data to obtain deformation trend data; performing deformation disturbance resonance analysis according to the deformation trend data and the micro-seismic monitoring data to obtain deformation disturbance resonance data; performing risk area grading on the deformation disturbance resonance data to obtain risk area grading data; and generating a routing inspection graph according to the risk area grading data to obtain routing inspection graph data so as to carry out underground cavern routing inspection auxiliary operation. The method accurately identifies the potential risk area, dynamically plans the inspection task, and improves the inspection efficiency and decision timeliness.

The invention discloses a building structure anti-seismic toughness optimization method and system based on deep learning, and relates to the technical field of anti-seismic toughness optimizing.In operation of the system, a sensing network is deployed, multi-dimensional dynamic response parameters in the vibration process of a building structure are collected, preprocessing and sliding window characterization are carried out on a collected parameter set, and the anti-seismic toughness of the building structure is optimized; encoding the input tensor TXF, extracting a deep space-time coupling feature generation coefficient, performing multi-dimensional composite calculation on an anti-seismic stress energy coefficient SEC, a response co-motion coefficient RSC and a damage evolution index DEI to generate a system-level evaluation core index toughness spectrum evolution index REX, and comparing the toughness spectrum evolution index REX with a preset threshold value to obtain the evaluation core index. Grading and risk positioning are carried out on the current state of the building structure, a generated control strategy is sent to an execution layer, a damping adjusting device, a shock insulation layer controller or component repairing equipment is linked to complete structure regulation and control, and response is carried out after real-time acquisition and optimization.

The invention relates to a coal mining subsidence area plant foundation monitoring method and system based on multi-source data fusion, and the method comprises the steps: obtaining multi-source monitoring data of a target coal mining subsidence area plant area, the multi-source monitoring data comprising satellite remote sensing data, ground measurement data and interference radar data; performing time synchronization and space unification on the multi-source monitoring data to obtain monitoring data with unified space; performing fusion processing on the monitoring data with uniform space to obtain comprehensive deformation data; constructing a three-dimensional settlement deformation field according to the comprehensive deformation data, and extracting deformation rate and acceleration information of each monitoring point through a gradient calculation method; and according to the deformation rate and the acceleration information, obtaining risk assessment of the plant foundation in the target coal mining subsidence area. The technical problems of multi-source heterogeneous data fusion and real-time settlement monitoring and early warning are effectively solved, and the precision and timeliness of foundation settlement monitoring are improved.

The invention discloses a hydraulic fracture monitoring system and method based on an underground micro-seismic sensor network, and belongs to the field of coal mining, and the system comprises a plurality of micro-seismic sensors which are used for collecting micro-seismic signals generated by rock fracture; the plurality of data acquisition substations are used for receiving and processing the micro-seismic signals; the data transmission substation is used for converting the micro-seismic signal processed by the data acquisition substation into an optical signal; the micro-seismic server performs micro-seismic event analysis according to the optical signal; the mining network switch is connected with the data transmission substation and the micro-seismic server and is used for network data exchange; the in-mine ring main unit is connected with the mining network switch and the micro-seismic server to form a communication network of the system; in order to solve the problem that hydraulic fracture detection precision is low due to the fact that micro-seismic signals generated by hydraulic fracture and coal mining activities are difficult to accurately distinguish in a complex underground environment, the hydraulic fracture detection method and device distinguish the micro-seismic signals generated by hydraulic fracture and coal mining activities by dividing a fracture area and a mining area, and the real-time monitoring precision of hydraulic fracture expansion is improved.

The invention relates to the technical field of submarine engineering and information processing, in particular to a submarine cable positioning method and system, and the method comprises the steps: synchronously collecting magnetic field, structured light and underwater acoustic signal data, and fusing inertial navigation information to construct a unified space-time reference system; precise registration of heterogeneous data is realized by adopting sliding time window screening, multi-modal noise reduction and rigid body transformation; constructing a graph structure model for expressing cable space-signal features based on the fused data, and reasoning cable path topology by using a graph convolutional network; reconstructing a cable center line through three-dimensional visualization, embedding the cable center line into submarine topography, and realizing risk area dynamic clustering by combining signal integrity score and burial depth estimation; historical time sequence data are fused, a long short-term memory network is adopted to predict cable drift, detectable attenuation and burial depth change trends, risk levels are divided based on prediction results, and spatial distribution early warning and disposal suggestions are output. According to the invention, the cable positioning precision and the risk assessment intelligent level in a complex seabed environment are improved.

The invention provides a deep-buried tunnel TBM vibration and excavation unloading fracture micro-seismic discrimination method and system, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining micro-seismic event data in a target area; spatial classification is carried out according to the micro-seismic event data, and the influence range of the excavation unloading effect is obtained; performing region division according to the influence range to obtain a rock fracture signal after noise reduction; performing multi-layer decomposition on the rock fracture signal by using wavelet transform to obtain a characteristic parameter set; performing energy grading according to the characteristic parameter set to obtain a grading result; constructing a discrimination standard according to the grading result to obtain the discrimination standard; and performing discrimination based on the discrimination standard to obtain a discrimination result. According to the method, the dynamic model is constructed based on the multi-energy-level discrimination standard, main control factor classification is performed on the real-time micro-seismic event, real-time dynamic discrimination of TBM vibration and excavation unloading fracture in the construction process is achieved, and the early warning timeliness of time-delay rock burst is remarkably improved.

The invention relates to the technical field of hydraulic ring geological survey, in particular to a hydraulic ring geological survey system and method based on GPS positioning, and the method comprises the following steps: obtaining the three-dimensional coordinate data of a survey area through a GPS positioning device, generating an initial geological positioning point set, inputting the initial geological positioning point set into a three-dimensional modeling system, generating an initial three-dimensional geological model, and carrying out the three-dimensional modeling of the initial three-dimensional geological model. Dynamically correcting the initial three-dimensional geologic model through an environment compensation algorithm to generate a corrected geologic model, dividing a geologic abnormal region through a spatial clustering algorithm based on a rock stratum permeability coefficient abnormal gradient value in the corrected geologic model, comparing the geologic abnormal region with an engineering parameter threshold library to generate a visual investigation report, and performing the visual investigation on the geologic abnormal region. An internet of things monitoring node network is established, displacement monitoring is carried out on a geological abnormal area, if a monitoring value exceeds a preset threshold value, an early warning signal is triggered, and the method can be widely applied to the fields of hydraulic ring geological survey, engineering construction and disaster early warning and has remarkable technical advantages.

The invention relates to the technical field of mining, in particular to a mine disaster prediction method and system. The method comprises the following steps: acquiring geologic structure data corresponding to a mine, wherein the geologic structure data comprises rock stratum distribution, fault occurrence and underground water occurrence state; constructing a coupling model corresponding to the mine based on the rock stratum distribution, the fault occurrence and the underground water occurrence state, and determining a rock mass constitutive relation and coupling parameters in the coupling model; determining a prediction boundary condition of the coupling model based on the rock constitutive relation and the coupling parameters; and acquiring field monitoring data of the mine, correcting the coupling model based on the field monitoring data and the prediction boundary condition so as to determine a potential disaster point based on the corrected coupling model, and generating a risk early warning strategy and a risk recovery strategy corresponding to the potential disaster point. According to the embodiment of the invention, a full-chain prevention and control system from disaster prediction to emergency disposal is constructed, and the safety management level and disaster prevention and control capability of mine engineering are remarkably improved.

The invention discloses an underground space surrounding rock excavation damage area testing method, which belongs to the technical field of rock mass damage testing, and comprises the following steps: sampling rocks with different depths, carrying out ultrasonic testing, and determining rock sample attenuation characteristics and wave velocity; determining a comprehensive damage index range of the rock samples with different damage grades; then, sensors are arranged in the excavation process to collect ultrasonic and acoustic emission data; then, an initial calculation model is constructed, and a surrounding rock three-dimensional wave velocity model is obtained through inversion of collected data; and finally, determining a damage grade according to the comprehensive damage index, and marking in the model to realize damage area testing. According to the method, the mapping relation between the damage degree and the wave velocity change is established, the comprehensive damage index range threshold value of different subareas is provided, quantitative and gradable classification is achieved, the three-dimensional wave velocity model is inversed based on test and monitoring data, and three-dimensional presentation and grading labeling of the damage area are achieved.

The invention discloses a foundation settlement early warning method and system based on an intelligent algorithm, and relates to the technical field of settlement early warning. During operation of the system, through combined use of various sensors, foundation settlement related data from multiple sources are collected and processed and fused in real time, foundation settlement is predicted through the intelligent algorithm, and the early warning effect is achieved. The settlement risk of different areas is evaluated by combining a space-time big data analysis technology, a settlement risk index GSI is calculated, an early warning grade is output, early warning decision making is carried out according to a prediction result and a set dynamic threshold value, the severity of the settlement risk is judged and classified, a system automatically generates an early warning report, and once early warning is triggered, the early warning is automatically carried out. The system automatically starts an emergency response scheme, dynamically adjusts reinforcement and intervention strategies according to the actual change of foundation settlement, provides a real-time visual interface of settlement data and early warning information, and displays the settlement state, the early warning level and historical data.

The invention relates to the technical field of mine safety monitoring and geophysical exploration, in particular to a mine water disaster intelligent alarm system responding to multi-dimensional physical field parameter abnormity, which comprises a full-space data acquisition module for acquiring monitoring area observation data and calling a background geological physical model; the ideal reference reconstruction module is used for constructing an ideal physical field reference in a non-abnormal state; the double difference extraction module is used for calculating an observation residual error of observation data relative to a reference and a theoretical residual error of simulation response relative to the reference; the inversion coupling verification module is used for driving the theoretical residual error to approach to the observation residual error and extracting parameter sensitivity characteristics; the intelligent alarm judgment module is used for analyzing sensitivity characteristics and convergence states; generating an alarm instruction if the feature is determined to be the entity fluid abnormal feature; if it is determined that the target abnormal interference feature is not the target abnormal interference feature, signal suppression processing is executed; according to the invention, the false alarm rate of mine water disaster detection is greatly reduced.

The invention discloses a deep well rock mass instability failure time accurate forecasting and early warning system, and relates to the technical field of deep well rock mass failure early warning, and the deep well rock mass instability failure time accurate forecasting and early warning system comprises the following steps: collecting a plurality of deep well rock samples to carry out a uniaxial compression experiment, and collecting acoustic emission signals in real time based on an acoustic emission sensor to obtain original acoustic emission signal data; carrying out self-adaptive preprocessing, and carrying out data classification and integration to obtain rock sample acoustic emission parameter data; constructing an original damage forecasting model, and performing model training and verification optimization based on the rock sample acoustic emission parameter data to obtain a rock damage forecasting model; collecting an acoustic emission signal of the deep well rock mass, and predicting the instability failure time of the deep well rock mass in real time based on the rock failure forecasting model; the method is used for solving the problems that when an existing deep well rock mass damage early warning technology carries out early warning on rock mass instability damage through acoustic emission monitoring, single or few characteristic parameters are manually processed and analyzed, and the early warning reliability and timeliness are not high.

The invention relates to the technical field of reflected waves, in particular to a coal mine disaster comprehensive evaluation system based on three-component micro-motion detection, which comprises a frequency direction extraction module, a propagation included angle calculation module, a sensitive path identification module, a critical turning identification module and a structural disturbance mapping module. According to the method, the continuous sequence is formed in the space through the direction vector constructed through the main frequency value of the measuring point, an included angle screening strategy is used for eliminating incoherent direction sections, it is ensured that the path has trend consistency, the frequency amplitude difference value and symbol inversion are used for detecting a mutation point, and the response recognition capacity for directional changes is enhanced; the normalized frequency ratio is combined with a standard deviation and a difference value to carry out joint calculation so as to quantify a structure jump degree and improve the accuracy of reflex position expression, disaster boundary points form an image set, and a structure identification channel is established by taking a frequency direction as a principal line; and the spatial expression definition and precision are improved in the aspects of path aggregation, boundary precision, disaster recognition and partition and continuous structure drawing.

The invention discloses a shield tunnel ground subsidence real-time prediction method considering spatio-temporal information, and the method comprises the steps: constructing an input feature system, including classifying model input into geometric information, multi-ring geological condition information, multi-time step shield parameter information and historical subsidence information; a multi-source information fusion model architecture is designed, geometric information, geological condition information, shield operation parameter information and historical settlement information serve as input of the model, and feature extraction is conducted through multiple encoders. Then feature fusion is carried out, nonlinear mapping is carried out by using a residual network (ResNet), and finally a real-time settlement prediction value is output; according to the scheme, the space-time characteristics of ground subsidence induced by tunneling are deeply excavated, and real-time prediction of subsidence of any position in a disturbance range is realized through deep fusion of multi-source characteristics.

The invention discloses an open-pit mine slope risk prediction method and system based on multi-source data, and relates to the technical field of intelligent geological engineering, and the method comprises the steps: collecting geological data, displacement monitoring data and environmental activity data of an open-pit mine slope, carrying out the preprocessing, and constructing a three-dimensional digital twinborn body; carrying out fusion calculation on the preprocessed displacement monitoring data and environmental activity data through a physical information neural network, predicting a displacement value and a stress value of the strip mine slope, and obtaining full-field physical quantity prediction data; according to the strip mine slope slip crack surface damage evolution path, the stability risk levels of the whole strip mine slope and different areas in the strip mine slope are judged, high-risk areas are positioned, and a strip mine slope risk prediction report is generated. According to the method, the damage evolution path of the slip crack surface of the strip mine slope is dynamically deduced, so that space-time continuous modeling of the damage evolution process, early recognition of the slope instability precursor and accurate insight of the risk evolution trend are realized.

The invention relates to the technical field of electric digital data processing, in particular to a dangerous rock instability monitoring and early warning method. The method comprises the following steps: collecting multi-modal original data related to the dangerous rock in real time, and preprocessing the multi-modal original data to obtain multi-dimensional core parameters; lithologic partitioning is carried out on the monitoring area based on the borehole acoustic logging data, and a lithologic partitioning result is obtained; carrying out data association and fusion by adopting a D-S evidence theory based on the lithologic partitioning result and the multi-dimensional core parameters, and generating a comprehensive support degree of each catastrophe state; multi-modal characteristic spectrum mathematical characterization is carried out based on the multi-dimensional core parameters, a precursor mode corresponding to the multi-modal characteristic spectrum subjected to mathematical characterization is identified, and space-time correlation verification is carried out on an identification result; and according to the comprehensive support degree, the identification result and the time-space correlation verification result, outputting a graded early warning instruction of dangerous rock instability. According to the invention, the problems of one-sided consideration, low precision and poor real-time performance of dangerous rock instability early warning in the prior art can be solved.

The invention discloses a mine roadway deformation data real-time analysis system based on edge calculation, which relates to the technical field of intelligent sensing and data analysis fusion, and comprises a data acquisition module for acquiring roadway deformation initial data through a sensor, simulating a deformation field evolution process by adopting a finite element analysis method, and analyzing the deformation field evolution process; the feature extraction module is used for extracting key stress distribution parameters from the non-uniform field distribution features, if the parameters exceed a preset threshold value, the key stress distribution parameters are marked as a risk concentration area, a monitoring position candidate set is determined, and the signal quality evaluation module is used for evaluating the monitoring position candidate set according to the monitoring position candidate set. Acquiring a signal-to-noise ratio and an integrity data quality index of a sensor signal, and performing priority grouping on the monitoring points through a clustering algorithm to obtain a dynamic priority evaluation result; according to the mine roadway deformation data real-time analysis system based on edge calculation, intelligence, real-time performance and high reliability of mine roadway deformation monitoring are achieved.

The invention provides a hydraulic fracturing fracture net distribution form near-field monitoring method, which comprises the following steps of: arranging a plurality of roof monitoring holes in roadways on two sides of an underground coal mine working surface, installing electromagnetic signal sensors in each roof monitoring hole and on anchor rods of roadway stoping sides, and constructing a full-space monitoring network covering a hydraulic fracturing layer; a data interpretation processing module of the electromagnetic signal time-shifting imaging analysis system comprises a multi-field coupling mathematical model for analyzing and deducing real-time imaging of electromagnetic signals; in the hydraulic fracturing process, an electromagnetic signal sensor is used for monitoring a current field, an electromagnetic field and an ion diffusion field in the rock mass fracture development and expansion process, a monitoring result is substituted into an electromagnetic signal time-shifting imaging analysis system for calculation and feature value extraction, and the fracture net distribution form is deduced through the feature value. According to the invention, the expansion path of the fracturing fluid in the rock stratum can be monitored in real time, the three-dimensional distribution form of the hydraulic fracturing fracture network can be inverted, and a new technical support is provided for the inspection of the hydraulic fracturing pressure relief effect of the underground coal mine.

The invention relates to the technical field of track bridge engineering, in particular to a high-speed railway track-bridge system earthquake-caused residual irregularity prediction method, which comprises the following steps of: determining a target object, and constructing an earthquake-caused irregularity database based on historical earthquake oscillation data of the target object; based on the earthquake-induced irregularity damage database, constructing an earthquake-induced peak irregularity deep learning prediction model fused with the vibration mode superposition method, and performing prediction by adopting the earthquake-induced peak irregularity deep learning prediction model to obtain an earthquake-induced peak irregularity prediction value; calculating an earthquake-caused residual irregularity equivalent coefficient based on earthquake-caused peak irregularity and earthquake-caused residual irregularity in the earthquake-caused irregularity damage database; and earthquake-caused residual irregularity is predicted based on the predicted earthquake-caused peak irregularity predicted value and the earthquake-caused residual irregularity equivalent coefficient. According to the method, the engineering safety and reliability of the result are ensured based on the mapping relation of the probability assurance rate, and the method has high efficiency and safety and is suitable for practical engineering application.

The invention relates to a geological forecasting technology, discloses a tunnel advanced geological forecasting method based on multi-geophysical prospecting joint inversion and multi-source data fusion, and solves the problems of poor efficiency and reliability caused by lack of effective constraints of an initial inversion model during joint detection of existing multiple geophysical prospecting methods. And the judgment accuracy is influenced by the failure of deep fusion of multi-source geological data. The method comprises the following steps of: detecting geological conditions in front of a tunnel face by adopting advanced horizontal drilling and advanced geophysical prospecting to obtain an advanced horizontal drilling detection result and original data of each geophysical prospecting method; the method comprises the following steps of: establishing an initial model by taking a prior mean constraint and a structure constraint as means and utilizing an advanced horizontal drilling detection result, and carrying out echelon joint inversion of a multi-geophysical prospecting method by taking high-precision information as a prior condition of low-precision information and taking structural plane information as a prior condition of resistivity information inversion; carrying out dimension reduction fusion and cross correction on the inversion result of each parameter; and finally, identifying geological information in front of the tunnel face by explaining the meaning of the parameter data and imaging.

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 discloses a highway tunnel surrounding rock deformation prediction method, and relates to the technical field of geotechnical engineering monitoring, and the method comprises the steps: collecting existing data, determining a monitoring arrangement scheme, and collecting on-site monitoring data through a monitoring data real-time feedback technology; processing multiple types of monitoring data and screening proper input features; clustering analysis is carried out on the monitoring data of the multiple sections, and classification of the excavated sections is completed; extracting the shape center of each section classification, and comparing the shape center with early-stage monitoring data of a predicted section to realize classification of the predicted section; and establishing a prediction model according to the classification of the prediction sections, and performing long-term prediction on the tunnel surrounding rock deformation response. According to the method, fine processing of the monitoring data is realized through feature screening and hierarchical clustering, and classification and classification of the known section and the predicted section are realized by fully utilizing multiple types of monitoring data, so that the long-term response of tunnel section surrounding rock deformation is predicted more accurately.

The invention discloses an intelligent geophysical exploration geological exploration analysis system, and relates to the field of geological exploration, and the system comprises a collection and preprocessing module which is used for carrying out the adaptive collection of multiple types of geophysical field signals of a target exploration region, and synchronously carrying out the preliminary noise reduction and quality optimization of original signals through signal purification processing; the fusion module is used for receiving the preprocessed multi-type geophysical field signals and carrying out normalization and feature depth correlation fusion on coordinates of data of different dimensions through spatial-temporal feature correlation processing; the method can dynamically adapt to environmental noise optimization signal acquisition, extracts stratum recessive characteristics through multi-dimensional data fusion and intelligent mining, constructs an accurate three-dimensional geologic model, positions stratum attribute deviation and grades through multi-scale analysis, presents exploration results in combination with three-dimensional visualization, and generates target region optimization suggestions.

The invention discloses a coal mine subsidence monitoring method based on multi-source data fusion, and belongs to the technical field of coal mine subsidence monitoring and safety management. Inputting the fused data into the model to automatically identify boundaries and features of the subsidence area; constructing a relationship between surface deformation and underground mining parameters, and obtaining optimal solutions of parameters such as mining depth and the like by adopting Bayesian posterior inversion in combination with MCMC sampling; calculating the volume of the goaf according to the inversion parameters and estimating the mining quantity; and updating the data, and triggering multimode early warning based on the threshold value of the subsidence amount or the rate. The technical problem that the monitoring precision is low due to the fact that a traditional coal mine subsidence monitoring technology depends on a single data source is solved.

The invention belongs to the technical field of road detection, and aims to provide a roadbed disease detection method and system, electronic equipment and a product. The method comprises the following steps: collecting multi-source detection data of a specified roadbed in real time and carrying out data fusion processing on the multi-source detection data to obtain fused detection data; performing crack detection processing on the roadbed surface image to obtain crack characteristic data of the specified roadbed, performing surface settlement analysis processing on the laser point cloud data to obtain settlement characteristic data of the specified roadbed, and performing deep disease analysis processing on the ultrasonic detection data and the electromagnetic wave reflection information to obtain the subgrade settlement characteristic data of the specified roadbed. Obtaining deep disease characteristic data of the specified roadbed; according to the crack characteristic data, the settlement characteristic data and the deep-layer disease characteristic data, constructing a disease three-dimensional model of the specified roadbed; and carrying out visualization processing on the disease three-dimensional model. According to the invention, omnibearing disease detection on the surface and the deep layer of the roadbed can be realized, and the intelligent degree is high.

The invention discloses a multi-source information real-time identification and positioning method for a coal mine rock burst risk area. The method comprises the following steps: integrating a micro-seismic system, a rock noise system, a stress system and a geological system to construct an acquisition network; extracting multi-source characteristic indexes and carrying out space-time unification; establishing a dynamic weighted risk fusion model to calculate a comprehensive risk index RI of a three-dimensional grid unit, wherein the weight of the comprehensive risk index RI is dynamically and adaptively adjusted along with mining; based on the RI value, a high-risk area is identified and positioned through three-dimensional visualization, and graded early warning information is generated; according to the invention, a time-space synchronous multi-source information acquisition network is constructed, and uniform preprocessing and feature extraction are carried out on four types of data of microseism, rock noise, stress and geologic structure, so that deep fusion and collaborative analysis of multi-source information are realized, and an information island phenomenon existing in traditional monitoring is overcome; and the judgment on the rock burst risk is more comprehensive.

The invention discloses a data detection method and system based on gravitational-magnetic-electric-seismic signal joint gridding, and relates to the technical field of geological prospecting, and the method comprises the steps: determining a research region according to an exploration target and geological conditions, and dividing the research region into grids with the same size; on each grid node, gravity, magnetic, electric and seismic signals are collected; the collected gravity, magnetic and electric shock signals are preprocessed; performing feature extraction on the preprocessed gravity-magnetic-electric-seismic signals by using an independent component analysis algorithm, and performing data fusion by using a convolutional neural network in deep learning; establishing an initial value estimation incidence relation model, and obtaining an initial value prediction result based on the fused data; according to the initial value prediction result, geological parameters are obtained through solving by using a joint inversion algorithm. According to the invention, efficient combined acquisition of gravity, magnetic, electric and seismic signals is realized, the precision and quality of acquired data are improved, and unified management and fusion analysis of the data are realized.