CN113970782A - Big data processing high and steep rock mass mining slope stability sound wave detection evaluation system - Google Patents
Big data processing high and steep rock mass mining slope stability sound wave detection evaluation system Download PDFInfo
- Publication number
- CN113970782A CN113970782A CN202111276419.6A CN202111276419A CN113970782A CN 113970782 A CN113970782 A CN 113970782A CN 202111276419 A CN202111276419 A CN 202111276419A CN 113970782 A CN113970782 A CN 113970782A
- Authority
- CN
- China
- Prior art keywords
- data
- module
- rock mass
- data processing
- big data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000012545 processing Methods 0.000 title claims abstract description 82
- 239000011435 rock Substances 0.000 title claims abstract description 54
- 238000005065 mining Methods 0.000 title claims abstract description 39
- 238000001514 detection method Methods 0.000 title claims abstract description 31
- 238000011156 evaluation Methods 0.000 title claims abstract description 25
- 238000000034 method Methods 0.000 claims abstract description 30
- 230000008569 process Effects 0.000 claims abstract description 22
- 238000004891 communication Methods 0.000 claims abstract description 16
- 238000005516 engineering process Methods 0.000 claims abstract description 10
- 230000005540 biological transmission Effects 0.000 claims abstract description 4
- 230000010354 integration Effects 0.000 claims description 20
- 238000001914 filtration Methods 0.000 claims description 18
- 238000006243 chemical reaction Methods 0.000 claims description 12
- 238000004458 analytical method Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 claims description 10
- 238000007405 data analysis Methods 0.000 claims description 8
- 230000003321 amplification Effects 0.000 claims description 6
- 238000010586 diagram Methods 0.000 claims description 6
- 238000012905 input function Methods 0.000 claims description 6
- 238000003199 nucleic acid amplification method Methods 0.000 claims description 6
- 238000011157 data evaluation Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 4
- 238000004088 simulation Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000004140 cleaning Methods 0.000 claims description 3
- 230000006870 function Effects 0.000 claims description 3
- 230000004927 fusion Effects 0.000 claims description 3
- 230000003993 interaction Effects 0.000 claims description 3
- 238000013507 mapping Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 239000013598 vector Substances 0.000 claims description 3
- 238000004590 computer program Methods 0.000 claims description 2
- 239000002689 soil Substances 0.000 description 3
- 238000012854 evaluation process Methods 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000009412 basement excavation Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
- G01V1/00—Seismology; Seismic or acoustic prospecting or detecting
- G01V1/28—Processing seismic data, e.g. analysis, for interpretation, for correction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24553—Query execution of query operations
- G06F16/24554—Unary operations; Data partitioning operations
- G06F16/24556—Aggregation; Duplicate elimination
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
- G06F18/24137—Distances to cluster centroïds
Abstract
The invention belongs to the technical field of slope stability detection, and discloses a big data processing high and steep rock mass mining slope stability acoustic wave detection evaluation system, which comprises: the sound wave transmitting module is connected with the central processing module, is placed in the drill hole by utilizing the vibration exciter and transmits sound waves with different certain frequencies; the data acquisition module acquires acoustic wave data through an acoustic wave sensor arranged in the drill hole and processes an acoustic wave signal; the central processing module is respectively connected with the sound wave transmitting module, the data acquisition module and the communication module, respectively controls each module, and analyzes, judges and stores the acquired data; the communication module builds a data transmission bridge between the central processing module and the cloud server through the communication equipment; and the cloud server analyzes and evaluates the stability of the high and steep rock mining slope by utilizing a big data processing technology through the cloud server. The method has high data processing efficiency and accuracy, and can effectively ensure the safety of mining of high and steep rock masses.
Description
Technical Field
The invention belongs to the technical field of slope stability detection, and particularly relates to a large data processing high-steep rock mass mining slope stability acoustic wave detection evaluation system.
Background
At present, in engineering geology, a geologic body having certain rock components, structural characteristics and occurrence in a certain geological environment within the engineering action range is called a rock body. Rock mass is cut under the conditions of internal weak bonding, bedding, joints, faults and the like, and has obvious discontinuity. The method is an important characteristic of the rock mass, and the mechanical effect of the rock mass structure is weakened and eliminated. The strength of the rock mass is far lower than that of the rock mass, the deformation of the rock mass is far greater than that of the rock mass, and the permeability of the rock mass is far greater than that of the rock. The cause types of the rock mass structural plane are as follows: the structural surfaces with different causes have different engineering geological characteristics. The structural surfaces can be divided into three types, namely primary structural surfaces, structural surfaces and secondary structural surfaces according to the cause. In the process of rock mass mining, the stability of the high and steep rock mass mining slope needs to be detected. The slope stability refers to the stability degree of the rock and soil bodies of the slope under certain slope height and slope angle conditions. According to the cause, the side slopes are divided into natural slopes and artificial slopes, and the latter are divided into excavation side slopes, dam side slopes and the like. The side slopes are divided into 3 types of rock mass side slopes, soil mass side slopes and rock and soil mass composite side slopes according to material composition. According to the stability degree, the method is divided into a stable side slope, an unstable side slope and a limit balance state side slope. However, in the existing high and steep rock mining slope stability acoustic wave detection and evaluation technology, the data processing efficiency and accuracy are low in the evaluation process, and the safety of high and steep rock mining cannot be guaranteed.
Through the above analysis, the problems and defects of the prior art are as follows: the existing high and steep rock mining slope stability acoustic wave detection and evaluation technology has low data processing efficiency and accuracy in the evaluation process, and cannot ensure the safety of high and steep rock mining.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a big data processing high steep rock mass mining slope stability acoustic wave detection evaluation system.
The invention is realized in this way, a big data processing high steep rock mass mining slope stability acoustic wave detection evaluation system, which includes:
the sound wave transmitting module is connected with the central processing module, is placed in the drill hole by utilizing the vibration exciter and transmits sound waves with different certain frequencies;
the data acquisition module is connected with the central processing module, acquires acoustic wave data through an acoustic wave sensor arranged in the drill hole and processes acoustic wave signals;
the central processing module is respectively connected with the sound wave transmitting module, the data acquisition module and the communication module, respectively controls each module, and analyzes, judges and stores the acquired data;
the communication module is connected with the central processing module and used for building a data transmission bridge between the central processing module and the cloud server through the communication equipment;
and the cloud server is connected with the communication module and is used for analyzing and evaluating the stability of the high and steep rock mining slope by utilizing a big data processing technology.
Further, the central processing module is provided with:
and the data integration module is used for realizing integration processing of the data through a data integration algorithm.
And the data analysis and evaluation module analyzes and evaluates the data through a slope stability sound wave detection and evaluation program.
And the data classification storage module is used for clustering the data and storing the data.
Further, the specific process of classifying the data by the data classification storage module is as follows:
determining classes or groups to use and randomly initialize their respective center points based on data in the overall system;
classifying each data point in each class or group by calculating the distance between the point and the center of each group, and then classifying this point as the group closest to it;
recalculating the group center by taking the mean of all vectors in the group according to the determined classification points;
and repeating the processes until all data processing and classification are completed.
Further, the data integration module realizes the specific process of integrating and processing the data through a data integration algorithm, and the specific process comprises the following steps:
determining a neural network model and a learning rule according to data in the whole system, and establishing a total input function;
defining the overall input function as a mapping function of a relevant unit, and reflecting the statistical law of the environment into the structure of the network through the interaction of the neural network and the environment;
and learning and understanding the output information of the sensor, determining the distribution of weight values, and finishing the acquisition of data and information fusion.
Further, the specific process of analyzing and evaluating the data by the data analysis and evaluation module through the slope stability sound wave detection and evaluation program is as follows:
inputting the collected data into a computer, and establishing a corresponding simulation graph by using a big data graphic processing mode;
using a certain curve and the plan-plan graph to represent the quantitative relation between the slope-related parameters, thereby obtaining the slope stability coefficient and obtaining the stable slope angle or the limit slope height;
and solving boundary conditions of slope deformation and damage by using the diagram, analyzing the combination relation of the weak structural planes, analyzing the form and the sliding direction of the sliding body, and evaluating the stability degree of the slope.
Further, the cloud server utilizes a big data processing technology to analyze and evaluate the stability of the high and steep rock mining slope through the cloud server, and the concrete process of big data processing is as follows:
receiving data from the client using a plurality of databases and performing simple query and processing work through the databases;
importing data into a centralized large-scale distributed database or a distributed storage cluster, and cleaning and preprocessing the imported data on the basis of importing;
the statistics and analysis utilizes a distributed database or a distributed computing cluster to carry out common analysis and classification summarization on mass data stored in the distributed database or the distributed computing cluster so as to meet most common analysis requirements;
various algorithm-based calculations are performed on the existing data, thereby achieving the effect of prediction and realizing some requirements of high-level data analysis.
Further, the data acquisition module is provided with:
the energy conversion module converts sound waves into electric signals through an energy conversion circuit;
the signal amplification module is used for amplifying the signal through the signal amplification circuit;
the signal filtering module is used for filtering the signal through the signal filtering circuit;
and the signal conversion module is used for converting the signal into a signal identified by the system through the signal conversion circuit.
Further, the specific process of filtering the signal by the signal filtering module is as follows:
performing wavelet transformation on the signal containing the noise to obtain a group of wavelet coefficients;
performing threshold processing on the wavelet coefficient to obtain an estimated wavelet coefficient; and performing wavelet reconstruction by using the estimated wavelet coefficient to obtain a filtering signal.
It is another object of the present invention to provide a computer program product stored on a computer readable medium, comprising a computer readable program for providing a user input interface to implement the big data processing high steep rock mass mining slope stability acoustic detection evaluation system when executed on an electronic device.
Another object of the present invention is to provide a computer-readable storage medium storing instructions which, when executed on a computer, cause the computer to execute the big data processing high steep rock mass mining slope stability acoustic wave detection and evaluation system.
By combining all the technical schemes, the invention has the advantages and positive effects that: the sound wave transmitting module and the data acquisition module are arranged, so that the sound wave signals can be accurately acquired, and the sound wave signals which are interfered are avoided. According to the method, the cloud server is used for analyzing and evaluating the stability of the high and steep rock mining slope by using a big data processing technology, so that the accuracy and the efficiency of data processing are improved. According to the method, the corresponding simulation diagram is established by using a big data graphical processing mode, so that the stability of the high and steep rock mass mining slope is observed and analyzed conveniently, the evaluation accuracy is further improved, and the safety of the high and steep rock mass mining is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained from the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a large data processing high steep rock mass mining slope stability acoustic wave detection evaluation system provided by the embodiment of the invention.
In the figure: 1. a sound wave emitting module; 2. a data acquisition module; 3. a central processing module; 4. a communication module; 5. and (4) a cloud server.
Fig. 2 is a schematic structural diagram of a central processing module according to an embodiment of the present invention.
Fig. 3 is a flowchart of a method for classifying data by the data classification storage module according to an embodiment of the present invention.
Fig. 4 is a flowchart of a method for implementing data integration processing by a data integration module according to a data integration algorithm provided in the embodiment of the present invention.
Fig. 5 is a flowchart of a method for analyzing slope stability by the central processing module according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Aiming at the problems in the prior art, the invention provides a big data processing high steep rock mass mining slope stability acoustic wave detection evaluation system, and the invention is described in detail below by combining the attached drawings.
As shown in fig. 1, the acoustic wave detection evaluation system for large data processing high steep rock mass mining slope stability provided by the embodiment of the invention comprises:
and the sound wave transmitting module 1 is connected with the central processing module 3, and is placed inside the drill hole by using a vibration exciter to transmit sound waves with different certain frequencies.
And the data acquisition module 2 is connected with the central processing module 3, acquires sound wave data through a sound wave sensor arranged in the drill hole, and processes sound wave signals.
And the central processing module 3 is respectively connected with the sound wave transmitting module 1, the data acquisition module 2 and the communication module 4, respectively controls each module, and analyzes, judges and stores the acquired data.
And the communication module 4 is connected with the central processing module 3 and builds a data transmission bridge between the central processing module and the cloud server through communication equipment.
And the cloud server 5 is connected with the communication module 4, and analyzes and evaluates the stability of the high and steep rock mining slope by utilizing a big data processing technology through the cloud server.
As shown in fig. 2, the central processing module provided in the embodiment of the present invention includes:
and the data integration module is used for realizing integration processing of the data through a data integration algorithm.
And the data analysis and evaluation module analyzes and evaluates the data through a slope stability sound wave detection and evaluation program.
And the data classification storage module is used for clustering the data and storing the data.
As shown in fig. 3, the specific process of classifying data by the data classification storage module according to the embodiment of the present invention is as follows:
s101: determining classes or groups to use and randomly initialize their respective center points based on data in the overall system;
s102: classifying each data point in each class or group by calculating the distance between the point and the center of each group, and then classifying this point as the group closest to it;
s103: recalculating the group center by taking the mean value of all vectors in the group according to the classification points determined in S102;
s104: and repeating the steps S101-S102 until all data processing and classification are completed.
As shown in fig. 4, the specific process of the data integration module provided in the embodiment of the present invention for implementing data integration processing by a data integration algorithm is as follows:
s201: determining a neural network model and a learning rule according to data in the whole system, and establishing a total input function;
s202: defining the overall input function as a mapping function of a relevant unit, and reflecting the statistical law of the environment into the structure of the network through the interaction of the neural network and the environment;
s203: and learning and understanding the output information of the sensor, determining the distribution of weight values, and finishing the acquisition of data and information fusion.
As shown in fig. 5, the central processing module provided in the embodiment of the present invention analyzes the slope stability as follows:
s301: inputting the collected data into a computer, and establishing a corresponding simulation graph by using a big data graphic processing mode;
s302: using a certain curve and the plan-plan graph to represent the quantitative relation between the slope-related parameters, thereby obtaining the slope stability coefficient and obtaining the stable slope angle or the limit slope height;
s303: and solving boundary conditions of slope deformation and damage by using the diagram, analyzing the combination relation of the weak structural planes, analyzing the form and the sliding direction of the sliding body, and evaluating the stability degree of the slope.
The cloud server provided by the embodiment of the invention utilizes a big data processing technology to analyze and evaluate the stability of the mining slope of the steep rock mass through the cloud server, and the concrete process of big data processing is as follows:
receiving data from the client using a plurality of databases and performing simple query and processing work through the databases;
importing data into a centralized large-scale distributed database or a distributed storage cluster, and cleaning and preprocessing the imported data on the basis of importing;
the statistics and analysis utilizes a distributed database or a distributed computing cluster to carry out common analysis and classification summarization on mass data stored in the distributed database or the distributed computing cluster so as to meet most common analysis requirements;
various algorithm-based calculations are performed on the existing data, thereby achieving the effect of prediction and realizing some requirements of high-level data analysis.
The data acquisition module 2 provided by the embodiment of the invention is provided with:
the energy conversion module converts sound waves into electric signals through an energy conversion circuit;
the signal amplification module is used for amplifying the signal through the signal amplification circuit;
the signal filtering module is used for filtering the signal through the signal filtering circuit;
and the signal conversion module is used for converting the signal into a signal identified by the system through the signal conversion circuit.
The specific process of filtering the signal by the signal filtering module provided by the embodiment of the invention is as follows:
performing wavelet transformation on the signal containing the noise to obtain a group of wavelet coefficients;
performing threshold processing on the wavelet coefficient to obtain an estimated wavelet coefficient; and performing wavelet reconstruction by using the estimated wavelet coefficient to obtain a filtering signal.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any modification, equivalent replacement, and improvement made by those skilled in the art within the technical scope of the present invention disclosed herein, which is within the spirit and principle of the present invention, should be covered by the present invention.
Claims (10)
1. The utility model provides a big data processing high steep rock mass mining slope stability acoustic detection evaluation system which characterized in that, big data processing high steep rock mass mining slope stability acoustic detection evaluation system includes:
the sound wave transmitting module is connected with the central processing module, is placed in the drill hole by utilizing the vibration exciter and transmits sound waves with different certain frequencies;
the data acquisition module is connected with the central processing module, acquires acoustic wave data through an acoustic wave sensor arranged in the drill hole and processes acoustic wave signals;
the central processing module is respectively connected with the sound wave transmitting module, the data acquisition module and the communication module, respectively controls each module, and analyzes, judges and stores the acquired data;
the communication module is connected with the central processing module and used for building a data transmission bridge between the central processing module and the cloud server through the communication equipment;
and the cloud server is connected with the communication module and is used for analyzing and evaluating the stability of the high and steep rock mining slope by utilizing a big data processing technology.
2. The big data processing high steep rock mass mining slope stability acoustic detection evaluation system of claim 1, characterized in that, the central processing module is provided with:
the data integration module is used for realizing integration processing of data through a data integration algorithm;
the data analysis and evaluation module analyzes and evaluates the data through a slope stability sound wave detection and evaluation program;
and the data classification storage module is used for clustering the data and storing the data.
3. The big data processing high and steep rock mass mining slope stability acoustic wave detection and evaluation system according to claim 2, wherein the specific process of classifying the data by the data classification storage module is as follows:
determining classes or groups to use and randomly initialize their respective center points based on data in the overall system;
classifying each data point in each class or group by calculating the distance between the point and the center of each group, and then classifying this point as the group closest to it;
recalculating the group center by taking the mean of all vectors in the group according to the determined classification points;
and repeating the processes until all data processing and classification are completed.
4. The big data processing high and steep rock mass mining slope stability acoustic wave detection evaluation system according to claim 2, wherein the data integration module realizes the specific process of data integration processing through a data integration algorithm as follows:
determining a neural network model and a learning rule according to data in the whole system, and establishing a total input function;
defining the overall input function as a mapping function of a relevant unit, and reflecting the statistical law of the environment into the structure of the network through the interaction of the neural network and the environment;
and learning and understanding the output information of the sensor, determining the distribution of weight values, and finishing the acquisition of data and information fusion.
5. The big data processing high and steep rock mass mining slope stability acoustic wave detection evaluation system according to claim 2, wherein the specific process of analyzing and evaluating the data by the data analysis and evaluation module through the slope stability acoustic wave detection evaluation program is as follows:
inputting the collected data into a computer, and establishing a corresponding simulation graph by using a big data graphic processing mode;
using a certain curve and the plan-plan graph to represent the quantitative relation between the slope-related parameters, thereby obtaining the slope stability coefficient and obtaining the stable slope angle or the limit slope height;
and solving boundary conditions of slope deformation and damage by using the diagram, analyzing the combination relation of the weak structural planes, analyzing the form and the sliding direction of the sliding body, and evaluating the stability degree of the slope.
6. The big data processing high and steep rock mass mining slope stability acoustic wave detection and evaluation system according to claim 1, wherein the cloud server analyzes and evaluates the high and steep rock mass mining slope stability by using a big data processing technology through the cloud server, and the concrete big data processing process comprises the following steps:
receiving data from the client using a plurality of databases and performing simple query and processing work through the databases;
importing data into a centralized large-scale distributed database or a distributed storage cluster, and cleaning and preprocessing the imported data on the basis of importing;
the statistics and analysis utilizes a distributed database or a distributed computing cluster to carry out common analysis and classification summarization on mass data stored in the distributed database or the distributed computing cluster so as to meet most common analysis requirements;
various algorithm-based calculations are performed on the existing data, thereby achieving the effect of prediction and realizing some requirements of high-level data analysis.
7. The big data processing high and steep rock mass mining slope stability acoustic wave detection and evaluation system according to claim 1, wherein the data acquisition module is provided with:
the energy conversion module converts sound waves into electric signals through an energy conversion circuit;
the signal amplification module is used for amplifying the signal through the signal amplification circuit;
the signal filtering module is used for filtering the signal through the signal filtering circuit;
and the signal conversion module is used for converting the signal into a signal identified by the system through the signal conversion circuit.
8. The big data processing high and steep rock mass mining slope stability acoustic wave detection evaluation system according to claim 7, wherein the specific process of the signal filtering module for filtering the signal is as follows:
performing wavelet transformation on the signal containing the noise to obtain a group of wavelet coefficients;
performing threshold processing on the wavelet coefficient to obtain an estimated wavelet coefficient; and performing wavelet reconstruction by using the estimated wavelet coefficient to obtain a filtering signal.
9. A computer program product stored on a computer readable medium, comprising a computer readable program which, when executed on an electronic device, provides a user input interface to implement a big data processing high steep rock mass mining slope stability acoustic detection evaluation system according to any one of claims 1 to 8.
10. A computer readable storage medium storing instructions which, when executed on a computer, cause the computer to perform the big data processing high steep rock mass mining slope stability acoustic detection evaluation system of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111276419.6A CN113970782A (en) | 2021-10-29 | 2021-10-29 | Big data processing high and steep rock mass mining slope stability sound wave detection evaluation system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111276419.6A CN113970782A (en) | 2021-10-29 | 2021-10-29 | Big data processing high and steep rock mass mining slope stability sound wave detection evaluation system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113970782A true CN113970782A (en) | 2022-01-25 |
Family
ID=79589025
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111276419.6A Withdrawn CN113970782A (en) | 2021-10-29 | 2021-10-29 | Big data processing high and steep rock mass mining slope stability sound wave detection evaluation system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113970782A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117252111A (en) * | 2023-11-15 | 2023-12-19 | 中国电建集团贵阳勘测设计研究院有限公司 | Active monitoring method for hidden danger and dangerous case area of dyke |
-
2021
- 2021-10-29 CN CN202111276419.6A patent/CN113970782A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117252111A (en) * | 2023-11-15 | 2023-12-19 | 中国电建集团贵阳勘测设计研究院有限公司 | Active monitoring method for hidden danger and dangerous case area of dyke |
| CN117252111B (en) * | 2023-11-15 | 2024-02-23 | 中国电建集团贵阳勘测设计研究院有限公司 | Active monitoring method for hidden danger and dangerous case area of dyke |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105974469B (en) | Micro seismic monitoring prewarning analysis system and method under Excavation of Rock Slope perturbation action | |
| CN110910613B (en) | Rock micro-seismic wireless monitoring, receiving and early warning system | |
| CN109000876B (en) | SNS optical fiber impact identification method based on automatic encoder deep learning | |
| CN111914454A (en) | Performance-based slope earthquake vulnerability assessment method | |
| CN104950334A (en) | Method and device for predicting reservoir distribution | |
| CN110334458B (en) | Structural seismic capacity assessment method considering influence of initial damage state | |
| CN114972384A (en) | Tunnel rock intelligent rapid regional grading method based on deep learning | |
| CN108824409A (en) | Railway high precipitous rock slope micro seismic monitoring method and system | |
| CN107193001B (en) | Karst tunnel unfavorable geology forecasting expert system based on ground penetrating radar and implementation method | |
| CN104297788A (en) | Mine microseism and blasting signal identification method based on waveform oscillation starting trend line slope | |
| CN101901209A (en) | Structural response analysis method based on improved EMD (Empirical Mode Decomposition) and ARMA (Autoregressive Moving-Average) model | |
| Sokolov et al. | Probabilistic seismic hazard assessment for Saudi Arabia using spatially smoothed seismicity and analysis of hazard uncertainty | |
| CN113970782A (en) | Big data processing high and steep rock mass mining slope stability sound wave detection evaluation system | |
| CN113687433A (en) | Bi-LSTM-based magnetotelluric signal denoising method and system | |
| CN209619984U (en) | Railway high precipitous rock slope Microseismic monitoring system | |
| CN103898890A (en) | Soil layer quantization layering method based on double-bridge static sounding data of BP neural network | |
| CN113189644B (en) | Microseismic source positioning method and system | |
| CN103628866A (en) | Method for obtaining stratum pressure coefficients of stratum | |
| CN107561584A (en) | A kind of rock mass seismic wave, the method for sound wave velocity of longitudinal wave contrast conversion | |
| CN102507062B (en) | Neural network model BP (Belief Propagation) algorithm-based embedded type mine pressure detection method | |
| Yu et al. | Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning | |
| Liu et al. | Production and construction quality management system of prefabricated buildings based on BIM technology | |
| JP2022053155A (en) | Earthquake motion evaluation model generation method, earthquake motion evaluation model generation device, earthquake motion evaluation method, and earthquake motion evaluation device | |
| Zhang et al. | Radar health management system based on multi-agent | |
| Abdollahi Mansourkhani et al. | Assessment of Groundwater Quality Spatial Distribution and Appointment Optimize Network of Shahrkord Plain Aquifer Using Geostatistical Methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220125 |