CN114896548A - Slope stability judging method, device and equipment and readable storage medium - Google Patents
Slope stability judging method, device and equipment and readable storage medium Download PDFInfo
- Publication number
- CN114896548A CN114896548A CN202210555277.5A CN202210555277A CN114896548A CN 114896548 A CN114896548 A CN 114896548A CN 202210555277 A CN202210555277 A CN 202210555277A CN 114896548 A CN114896548 A CN 114896548A
- Authority
- CN
- China
- Prior art keywords
- information
- monitoring point
- curve
- slope
- calculation
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000012544 monitoring process Methods 0.000 claims abstract description 89
- 238000012545 processing Methods 0.000 claims abstract description 62
- 230000001133 acceleration Effects 0.000 claims abstract description 57
- 229910000831 Steel Inorganic materials 0.000 claims abstract description 15
- 239000002689 soil Substances 0.000 claims abstract description 15
- 239000010959 steel Substances 0.000 claims abstract description 15
- 238000013528 artificial neural network Methods 0.000 claims abstract description 9
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 9
- 238000007781 pre-processing Methods 0.000 claims abstract description 9
- 238000004364 calculation method Methods 0.000 claims description 86
- 230000004044 response Effects 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 9
- 230000008034 disappearance Effects 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 5
- 230000002265 prevention Effects 0.000 abstract description 2
- 238000003062 neural network model Methods 0.000 description 15
- 238000001228 spectrum Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 8
- 230000008859 change Effects 0.000 description 6
- 230000006870 function Effects 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000005070 sampling Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 230000004913 activation Effects 0.000 description 2
- 238000011478 gradient descent method Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003595 spectral effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 239000002253 acid Substances 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/14—Fourier, Walsh or analogous domain transformations, e.g. Laplace, Hilbert, Karhunen-Loeve, transforms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
- Y02A10/23—Dune restoration or creation; Cliff stabilisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Mathematical Optimization (AREA)
- Biomedical Technology (AREA)
- Databases & Information Systems (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Algebra (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention relates to the field of disaster prevention and control, in particular to a method, a device, equipment and a readable storage medium for judging slope stability, wherein the method comprises the steps of obtaining first information, wherein the first information comprises data information monitored by a seismic data monitor pre-embedded at least one monitoring point; calculating a stability coefficient according to the first information to obtain second information, wherein the second information comprises stability coefficient data of each monitoring point; acquiring third information, wherein the third information comprises soil pressure parameter information of each monitoring point, anchor cable tension parameter information of each monitoring point and steel strand tension parameter information of each monitoring point; and preprocessing the preset acceleration in the horizontal direction and the third information, and processing and judging the preprocessed data based on a BP neural network algorithm to obtain a judgment result of the slope stability after the earthquake. The method can judge the stability of the slope after the earthquake in real time, and is more accurate and efficient.
Description
Technical Field
The invention relates to the field of disaster prevention and control, in particular to a slope stability judging method, a slope stability judging device, slope stability judging equipment and a readable storage medium.
Background
Various forms and types of high and steep slopes are widely distributed along the railway in China, particularly in the southwest region, and earthquakes frequently occur in the southwest region, so that great potential harm is caused to the safe operation of the railway in China. To complicated various high steep side slopes, the shaking table test can not be carried out to each side slope along the line certainly, the inherent dynamic response relation of different high steep side slopes can not be obtained, the stability of a retaining structure can not be evaluated, and then the stability of the side slope after an earthquake can not be judged in time, so that some accidents are caused. There is therefore a need for a method and apparatus that can acquire different steep slope stabilities immediately after an earthquake.
Disclosure of Invention
The invention aims to provide a slope stability judging method, a slope stability judging device, slope stability judging equipment and a readable storage medium, so as to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in one aspect, the application provides a method for determining slope stability, the method including: acquiring first information, wherein the first information comprises data information monitored by a seismic data monitor pre-buried in at least one monitoring point;
calculating a stability coefficient according to the first information to obtain second information, wherein the second information comprises stability coefficient data of each monitoring point;
acquiring third information, wherein the third information comprises soil pressure parameter information of each monitoring point, anchor cable tension parameter information of each monitoring point and steel strand tension parameter information of each monitoring point;
and preprocessing the preset acceleration in the horizontal direction and the third information, and processing and judging the preprocessed data based on a BP neural network algorithm to obtain a judgment result of the slope stability after the earthquake.
In a second aspect, an embodiment of the present application provides a slope stability determination device, including:
the system comprises a first acquisition unit, a second acquisition unit and a monitoring unit, wherein the first acquisition unit is used for acquiring first information which comprises data information monitored by a seismic data monitor pre-embedded in at least one monitoring point;
the first calculation unit is used for calculating a stability coefficient according to the first information to obtain second information, and the second information comprises stability coefficient data of each monitoring point;
the second acquisition unit is used for acquiring third information, wherein the third information comprises soil pressure parameter information of each monitoring point, anchor cable tension parameter information of each monitoring point and steel strand tension parameter information of each monitoring point;
and the first processing unit is used for preprocessing the preset acceleration in the horizontal direction and the third information, and processing and judging the preprocessed data based on a BP neural network algorithm to obtain a judgment result of the slope stability after the earthquake.
In a third aspect, an embodiment of the present application provides a slope stability determination device, where the device includes a memory and a processor. The memory is used for storing a computer program; the processor is used for realizing the steps of the slope stability judging method when executing the computer program.
In a fourth aspect, an embodiment of the present application provides a readable storage medium, where a computer program is stored on the readable storage medium, and the computer program, when executed by a processor, implements the steps of the slope stability determination method.
The invention has the beneficial effects that:
according to the method, the earthquake ending time is obtained through accurate calculation according to the earthquake dynamic acceleration curve, the stability coefficient data of each monitoring point in each time period is calculated on the basis, then the parameter data measured by the sensor of each monitoring point is obtained, and finally the processing and judgment are carried out through the optimized BP neural network model, so that the slope can be monitored and judged more comprehensively, manual processing is not needed, and the post-earthquake stability of each monitoring point can be accurately judged in real time.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a slope stability determination method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a slope stability determination apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a slope stability determination device according to an embodiment of the present invention.
The labels in the figure are: 701. a first acquisition unit; 702. a first calculation unit; 703. a second acquisition unit; 704. a first processing unit; 7021. a first calculation subunit; 7022. a first processing subunit; 7023. a second processing subunit; 7024. a second calculation subunit; 7025. a third computing subunit; 70211. a fourth calculation subunit; 70212. a fifth calculation subunit; 70213. a third processing subunit; 70221. a sixth calculation subunit; 70222. a seventh calculation subunit; 70223. an eighth calculation subunit; 7041. a fourth processing subunit; 7042. a fifth processing subunit; 7043. a sixth processing subunit; 7044. a seventh processing subunit; 7045. a ninth calculation subunit; 7046. an eighth processing subunit; 7047. a tenth calculation subunit; 7048. a ninth processing subunit; 7049. a tenth processing subunit; 800. slope stability judging equipment; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Real-time example 1
As shown in fig. 1, the present embodiment provides a slope stability determination method, which includes step S1, step S2, step S3, and step S4.
S1, acquiring first information, wherein the first information comprises data information monitored by a seismic data monitor pre-buried in at least one monitoring point;
it can be understood that the seismic monitor in the above steps is always kept working at a sampling frequency of 200Hz under non-seismic conditions and seismic conditions, so as to record background noise and monitor and identify seismic events; the earthquake monitor is composed of a broadband three-channel accelerometer, a power supply lightning arrester, an earthquake data collector, a power supply controller, a router, a GNSS time-giving device, a solar cell panel, small wind power generation equipment, a lead-acid storage battery and a lightning-protection grounding grid.
Step S2, calculating a stability coefficient according to the first information to obtain second information, wherein the second information comprises stability coefficient data of each monitoring point;
step S3, acquiring third information, wherein the third information comprises soil pressure parameter information of each monitoring point, anchor cable tension parameter information of each monitoring point and steel strand tension parameter information of each monitoring point;
it can be understood that the seismic monitoring device and the notebook computer in the above steps are connected in a limited gigabit network manner, wherein the port includes, but is not limited to, a TCP port, and when the seismic monitor does not detect a seismic event, the seismic monitor transmits a low voltage signal 0 to the laptop, after the earthquake event is identified and obtained, the earthquake monitor sends a high-voltage signal 1 to the notebook computer, the notebook computer triggers the earthquake event alarm by identifying the high-voltage signal 1, and sends out an instruction to a sensor collector for monitoring data of the high-steep slope, the sensor collector changes the sampling frequency of the sensor and collects the data according to 1000Hz, after the earthquake event is finished, recovering the sampling frequency of the sensor to 1Hz for acquisition, completing the variable frequency monitoring work of the high and steep slope and the retaining structure, therefore, data acquisition and judgment can be efficiently and quickly carried out, and data processing and data acquisition are reduced when an earthquake comes in the future.
And step S4, preprocessing the preset acceleration in the horizontal direction and the third information, and processing and judging the preprocessed data based on a BP neural network algorithm to obtain a judgment result of the slope stability after the earthquake.
The earthquake stability monitoring method based on the earth vibration acceleration curve can be used for accurately calculating according to the earth vibration acceleration curve to obtain the earthquake ending time, calculating the stability coefficient data of each monitoring point in each time period on the basis of the earthquake ending time, then obtaining the parameter data measured by the sensor of each monitoring point, and finally processing and judging through the optimized BP neural network model, so that the side slope can be monitored and judged more comprehensively, manual processing is not needed, and the after-earthquake stability of each monitoring point can be accurately judged in real time.
In a specific embodiment of the present disclosure, the step S2 includes steps S21, S22, S23, S24, and S25.
Step S21, carrying out curve conversion calculation according to the data information in the first information to obtain acceleration curve information in the vertical direction of the seismic waves corresponding to each monitoring point;
step S22, extracting first sub information based on the acceleration curve information of the seismic waves in the vertical direction corresponding to each monitoring point, wherein the first sub information comprises the time when the seismic waves reach each monitoring point and the disappearance time of the seismic waves at each monitoring point;
s23, obtaining the earthquake time section of each monitoring point based on the first sub information;
step S24, segmenting each seismic time period to obtain at least one segmented signal, and calculating based on the acceleration curve information of the seismic wave in the vertical direction corresponding to each segmented information and a preset signal response curve calculation formula to obtain at least one signal response curve;
it should be noted that, in the above steps, the acceleration time curve is first divided according to a preset number of segments, and then processed by using the following formula:
wherein G is a predetermined number of segments, and the addition indicates a complex conjugate of the data,
a horizontal response curve of a monitoring point of the ith row in the mth column in the T time period; n is a radical of FFT Is the length of the fourier spectrum; f [ X ] l (P m-1,i,f,T ),
For the ith row of the m column m,i,f,T And the ith row measuring point P of the m-1 column m-1,i,f,T Cross-power spectral density function of (a); g [ X ] l (P m-1,i,f,T ),
For m-1 th column and ith row monitoring point P m-1,i,f,T The self-power spectral density function of (a); i is the serial number of the line number; m is the column number.
And step S25, processing all the signal response curves based on a preset amplitude coefficient calculation formula, a fluctuation coefficient calculation formula and a stability coefficient calculation formula to obtain stability coefficient data of each monitoring point.
The formula for calculating the amplitude coefficient adopted in the step is as follows:
wherein,
in order to be the amplitude coefficient of the signal,
is composed of
The maximum value of (a) is,
is composed of
The minimum value of (a) is determined,
is composed of
The maximum value of (a) is,
is composed of
Is measured.
The fluctuation coefficient calculation formula adopted in the step is as follows:
wherein,
for the fluctuation coefficient, Cov is covariance, Var is variance, and the meanings of the other parameters are described in the step.
It should be noted that, the stability coefficient calculation formula adopted in this step is as follows:
wherein,
in order to be a state coefficient of the state,
for the fluctuation coefficient, the meaning of the rest parameters is described in the step.
Wherein, the meaning of the parameters is described in the step.
Wherein,
for the historical state coefficient, the meanings of the rest parameters are described in the step.
Wherein,
for the historical state coefficient, the meanings of the rest parameters are described in the step.
The method and the device can be used for judging whether the earthquake occurs in real time by converting the data in the first information into the acceleration curve and determining the time period of the earthquake waves based on the acceleration curve, and can also be used for determining the stability coefficient data of each monitoring point in real time after the earthquake comes, and further judging the stability of the slope after the earthquake through the stability coefficient data.
In a specific embodiment of the present disclosure, the step S21 includes a step S211, a step S212, and a step S213.
Step S211, calculating the first information according to a long time window average value calculation formula and a short time window calculation formula respectively to obtain a first average value and a second average value, wherein the first average value is the long time window average value of the first information, and the second information is the short time window average value of the first information;
wherein the formula adopted in step S211 is as follows:
wherein the STA i Is a short timeWindow mean, LTA i Is the long time window mean, i represents the time at i, x i For the acceleration data at time i, 50 indicates a time window length of 50,3000 and a time window length of 3000.
It can be understood that the arrival time of the seismic wave can be accurately calculated in the above manner.
Step S212, calculating a quotient of the first average value and the second average value to obtain a first calculation result, and comparing the first calculation result with a preset first threshold value respectively to obtain comparison result information;
and S213, sending the time information and the first information which are greater than the first threshold value in the comparison result information to an acceleration data curve generation module to obtain an acceleration curve of the seismic waves in the vertical direction.
The method comprises the following steps of calculating a comparison result of a long time window mean value and a short time window mean value based on a time window mean value calculation formula, determining an acceleration curve of seismic waves, and determining the accurate arrival time of the seismic waves by using the acceleration curve of the seismic waves.
In a specific embodiment of the present disclosure, the step S22 includes a step S221, a step S222, and a step S223.
Step S221, performing fractal dimension calculation on the acceleration curve in the vertical direction of the seismic waves, and generating a fractal dimension curve based on a value obtained by the fractal dimension calculation;
wherein the formula adopted in step S221 is as follows:
D i ＝1-F
wherein, moving different sizes to cover the whole acceleration curve to obtain the approximate length of the curve, and taking logarithm of different sizes and approximate lengths to perform one-time fitting to obtain slopes F, D i Is the fractal dimension at time i.
Wherein D is i Is the fractal dimension at time i. t is tAt the time of t, D i-t+1 Is the fractal dimension, K, at time i-t +1 i Is the fractal dimension slope value at the t-th time.
Step S222, inputting the fractal dimension curve to a seismic wave arrival time calculation module for calculation, wherein the slope of each moment of the seismic time period is calculated based on the fractal dimension curve, and the slope of each moment is compared to obtain the maximum value of each slope;
and S223, subtracting a preset time length from the time scale corresponding to the maximum slope value of the fractal dimension curve to serve as the time when the seismic waves reach each monitoring point, and subtracting the preset time length from the time scale corresponding to the point with the first slope being zero in the fractal dimension curve to obtain the disappearance time of the seismic waves at each monitoring point.
The method and the device can determine the disappearance moment of the seismic waves at each monitoring point through the fractal dimension curve, further determine the time period of monitoring the earthquake by each monitoring point, monitor the stability of the slope after the earthquake in real time, reduce the monitoring cost and improve the monitoring efficiency.
In a specific embodiment of the present disclosure, the step S4 includes a step S41, a step S42, a step S43, and a step S44.
Step S41, respectively carrying out curve transformation on the soil pressure parameter, the anchor cable tension parameter and the steel strand tension parameter in the third information, and acquiring a maximum value of a parameter of a soil pressure curve, a maximum value of a parameter of an anchor cable tension curve, a maximum value of a parameter of a steel strand tension curve, a change value of soil pressure after an earthquake, a change value of anchor cable tension after the earthquake and a change value of steel strand tension after the earthquake based on an image recognition algorithm;
step S42, calculating and curve transforming the acceleration of the seismic waves in the vertical direction and the preset acceleration in the horizontal direction based on a synthetic acceleration calculation formula to obtain a synthetic acceleration curve of the seismic waves and the maximum value and the minimum value of the synthetic acceleration of the seismic waves;
wherein the formula employed in step S42 is as follows:
X i ＝sqrt(x NSi 2 +x EWi 2 +x UDi 2 )
wherein x is NSi 、x EWi 、x UDi Respectively representing the acceleration data of the time i in the north-south direction, the east-west direction and the vertical direction, wherein sqrt is the open square root and X i Representing the resultant acceleration of the seismic waves.
Step S43, Fourier spectrum calculation is carried out on the soil pressure curve, the anchor cable tension curve, the steel strand tension curve and the synthetic acceleration curve of the seismic waves, and Fourier spectrum of at least one curve is obtained;
step S44, determining a frequency domain peak for each fourier spectrum based on each fourier spectrum.
It can be understood that the above steps are to collect post-earthquake data and then to perform preprocessing to provide indexes for the next stability judgment, and the above steps are to evaluate the stability of the post-earthquake slope and the anti-skid structure from multiple aspects, so as to ensure the validity of the evaluation.
In a specific embodiment of the present disclosure, the step S4 further includes a step S45, a step S46, a step S47, a step S48, and a step S49.
Step S45, carrying out weight calculation on preset data information subjected to history preprocessing according to a preset weight proportion to obtain history input unit data;
step S46, processing the historical input unit data based on an activation function formula and an output calculation formula to obtain first output index data of the BP neural network, wherein the first output index data is stability index data output during BP neural network model training;
step S47, error calculation is carried out on the first output index data and preset real stability index data based on a least square method, and error data of the first output index data and the preset real stability index data are obtained;
s48, optimizing a preset weight proportion based on the error data and a gradient descent method to obtain an optimized BP neural network model;
and step S49, sending the preprocessed data to the optimized BP neural network model for processing, and judging the stability of the slope after the earthquake based on the second output index data obtained by processing to obtain a judgment result of the stability of the slope after the earthquake, wherein the second output index data is the stability index data output by the optimized BP neural network model.
It can be understood that the historical data are trained through the neural network model in the above steps, error calculation is performed based on the stability of index data and real data generated by training, then parameters in the BP neural network model are adjusted based on the error data to obtain an optimized BP neural network model, and the preprocessed data are sent to the optimized BP neural network model for processing, so that the stability data of each monitoring point can be intelligently output, the labor cost is reduced, the judgment efficiency is improved, and the judgment accuracy is greatly improved after the BP neural network model is optimized.
Example 2
As shown in fig. 2, the present embodiment provides a slope stability determination apparatus, which includes a first acquisition unit 701, a first calculation unit 702, a second acquisition unit 703 and a first processing unit 704.
The first acquiring unit 701 is used for acquiring first information, wherein the first information comprises data information monitored by a seismic data monitor pre-buried in at least one monitoring point;
a first calculating unit 702, configured to perform stability coefficient calculation according to the first information to obtain second information, where the second information includes stability coefficient data of each monitoring point;
a second obtaining unit 703, configured to obtain third information, where the third information includes soil pressure parameter information of each monitoring point, anchor cable tension parameter information of each monitoring point, and steel strand tension parameter information of each monitoring point;
the first processing unit 704 is configured to preprocess the preset acceleration in the horizontal direction and the third information, and process and judge the preprocessed data based on a BP neural network algorithm to obtain a judgment result of the slope stability after the earthquake.
In a specific embodiment of the present disclosure, the first calculating unit 702 includes a first calculating subunit 7021, a first processing subunit 7022, a second processing subunit 7023, a second calculating subunit 7024, and a third calculating subunit 7025.
The first calculating subunit 7021 is configured to perform curve conversion calculation according to the data information in the first information to obtain acceleration curve information in the seismic wave vertical direction corresponding to each monitoring point;
the first processing subunit 7022 is configured to extract first sub information based on acceleration curve information in the vertical direction of seismic waves corresponding to each monitoring point, where the first sub information includes a time when the seismic waves reach each monitoring point and a disappearance time of the seismic waves at each monitoring point;
the second processing subunit 7023 is configured to obtain, based on the first sub-information, an earthquake time period of each monitoring point;
a second calculating subunit 7024, configured to segment each seismic time period to obtain at least one segmented signal, and calculate based on acceleration curve information of the seismic wave in the vertical direction corresponding to each segmented information and a preset signal response curve calculation formula to obtain at least one signal response curve;
and a third computing subunit 7025, configured to process all the signal response curves based on a preset amplitude coefficient calculation formula, a fluctuation coefficient calculation formula, and a stability coefficient calculation formula, to obtain stability coefficient data of each monitoring point.
In one embodiment of the present disclosure, the first calculating sub-unit 7021 includes a fourth calculating sub-unit 70211, a fifth calculating sub-unit 70212, and a third processing sub-unit 70213.
A fourth calculating subunit 70211, configured to calculate the first information according to a long time window average value calculation formula and a short time window calculation formula, respectively, to obtain a first average value and a second average value, where the first average value is a long time window average value of the first information, and the second information is a short time window average value of the first information;
a fifth calculating subunit 70212, configured to calculate a quotient of the first average value and the second average value to obtain a first calculation result, and compare the first calculation result with a preset first threshold respectively to obtain comparison result information;
and the third processing subunit 70213 is configured to send the time information that is greater than the first threshold in the comparison result information and the first information to the acceleration data curve generation module, so as to obtain an acceleration curve in the vertical direction of the seismic waves.
In a specific embodiment of the present disclosure, the first processing subunit 7022 includes a sixth calculation subunit 70221, a seventh calculation subunit 70222, and an eighth calculation subunit 70223.
A sixth calculating subunit 70221, configured to perform fractal dimension calculation on the acceleration curve in the seismic wave vertical direction, and generate a fractal dimension curve based on a value obtained by the fractal dimension calculation;
a seventh calculating subunit 70222, configured to input the fractal dimension curve to the seismic wave arrival time calculation module for calculation, where a slope at each time of the seismic time period is calculated based on the fractal dimension curve, and the slopes at each time are compared to obtain a maximum slope value of each time;
an eighth calculating subunit 70223, configured to subtract the preset time length from the time scale corresponding to the maximum slope value of the fractal dimension curve to obtain a time when the seismic wave reaches each monitoring point, and subtract the preset time length from the time scale corresponding to the point in the fractal dimension curve where the first slope is zero to obtain a disappearance time of the seismic wave at each monitoring point.
In a specific embodiment of the present disclosure, the first processing unit 704 includes a fourth processing subunit 7041, a fifth processing subunit 7042, a sixth processing subunit 7043, and a seventh processing subunit 7044.
A fourth processing subunit 7041, configured to perform curve transformation on the soil pressure parameter, the anchor cable tension parameter, and the steel strand tension parameter in the third information, and obtain a maximum value of a soil pressure curve, a maximum value of an anchor cable tension curve, a maximum value of a steel strand tension curve, a change value of soil pressure after an earthquake, a change value of anchor cable tension after an earthquake, and a change value of steel strand tension after an earthquake based on an image recognition algorithm;
a fifth processing subunit 7042, configured to calculate and perform curve transformation on the acceleration in the vertical direction of the seismic waves and the acceleration in the preset horizontal direction based on a synthetic acceleration calculation formula, so as to obtain a synthetic acceleration curve of the seismic waves, and a maximum value and a minimum value of the synthetic acceleration of the seismic waves;
a sixth processing subunit 7043, configured to perform fourier spectrum calculation on the soil pressure curve, the anchor cable tension curve, the steel strand tension curve, and the synthetic acceleration curve of the seismic wave to obtain a fourier spectrum of at least one curve;
a seventh processing subunit 7044, configured to determine a frequency domain peak of each fourier spectrum based on each fourier spectrum.
In a specific embodiment of the present disclosure, the first processing unit 704 includes a ninth calculating subunit 7045, an eighth processing subunit 7046, a tenth calculating subunit 7047, a ninth processing subunit 7048, and a tenth processing subunit 7049.
A ninth calculating subunit 7045, configured to perform weight calculation on the preset data information after history preprocessing according to a preset weight ratio to obtain history input unit data;
an eighth processing subunit 7046, configured to process the historical input unit data based on an activation function formula and an output calculation formula to obtain first output index data of the BP neural network, where the first output index data is stability index data output when the BP neural network model is trained;
a tenth calculating subunit 7047, configured to perform error calculation on the first output index data and preset true stability index data based on a least square method, to obtain error data of the first output index data and preset true stability index data;
a ninth processing subunit 7048, configured to optimize a preset weight ratio based on the error data and a gradient descent method, to obtain an optimized BP neural network model;
a tenth processing subunit 7049, configured to send the preprocessed data to the optimized BP neural network model for processing, and judge the stability of the slope after the earthquake based on the second output index data obtained through the processing, so as to obtain a result of judging the stability of the slope after the earthquake, where the second output index data is stability index data output by the optimized BP neural network model.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3
Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a slope stability determining device, and a slope stability determining device described below and a slope stability determining method described above may be referred to in a corresponding manner.
Fig. 3 is a block diagram illustrating a slope stability determining device 800 according to an exemplary embodiment. As shown in fig. 3, the slope stability determining apparatus 800 may include: a processor 801, a memory 802. The slope stability determination device 800 may further include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the slope stability determining apparatus 800 to complete all or part of the steps of the slope stability determining method. The memory 802 is used to store various types of data to support operation of the slope stability determination device 800, such data may include, for example, instructions for any application or method operating on the slope stability determination device 800, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and so forth. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the slope stability determining device 800 and other devices. Wireless communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding communication component 805 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the slope stability determining Device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, for performing one of the slope stability determining methods described above.
In another exemplary embodiment, a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the slope stability determination method described above is also provided. For example, the computer readable storage medium may be the memory 802 described above including program instructions executable by the processor 801 of the slope stability determination device 800 to perform the slope stability determination method described above.
Example 4
Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a readable storage medium, and a readable storage medium described below and a slope stability determination method described above may be referred to in correspondence.
A readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the slope stability determination method of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various other readable storage media capable of storing program codes.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A slope stability judging method is characterized by comprising the following steps:
acquiring first information, wherein the first information comprises data information monitored by a seismic data monitor pre-buried in at least one monitoring point;
calculating a stability coefficient according to the first information to obtain second information, wherein the second information comprises stability coefficient data of each monitoring point;
acquiring third information, wherein the third information comprises soil pressure parameter information of each monitoring point, anchor cable tension parameter information of each monitoring point and steel strand tension parameter information of each monitoring point;
and preprocessing the preset acceleration in the horizontal direction and the third information, and processing and judging the preprocessed data based on a BP neural network algorithm to obtain a judgment result of the slope stability after the earthquake.
2. The slope stability determination method according to claim 1, wherein performing stability factor calculation according to the first information to obtain second information comprises:
performing curve conversion calculation according to the data information in the first information to obtain acceleration curve information in the vertical direction of the seismic waves corresponding to each monitoring point;
extracting first sub-information based on acceleration curve information in the vertical direction of seismic waves corresponding to each monitoring point, wherein the first sub-information comprises the time when the seismic waves reach each monitoring point and the disappearance time of the seismic waves at each monitoring point;
acquiring the earthquake time period of each monitoring point based on the first sub-information;
segmenting each seismic time period to obtain at least one segmented signal, and calculating based on acceleration curve information of the seismic wave in the vertical direction corresponding to each segmented information and a preset signal response curve calculation formula to obtain at least one signal response curve;
and processing all the signal response curves based on a preset amplitude coefficient calculation formula, a fluctuation coefficient calculation formula and a stability coefficient calculation formula to obtain stability coefficient data of each monitoring point.
3. The slope stability judging method according to claim 2, wherein curve conversion calculation is performed according to data information in the first information to obtain acceleration curve information in the seismic wave vertical direction corresponding to each monitoring point, and the method comprises the following steps:
calculating the first information according to a long time window average value calculation formula and a short time window calculation formula respectively to obtain a first average value and a second average value, wherein the first average value is the long time window average value of the first information, and the second information is the short time window average value of the first information;
calculating a quotient of the first average value and the second average value to obtain a first calculation result, and comparing the first calculation result with a preset first threshold value respectively to obtain comparison result information;
and sending the time information and the first information which are greater than the first threshold value in the comparison result information to an acceleration data curve generation module to obtain an acceleration curve of the seismic waves in the vertical direction.
4. The slope stability determination method according to claim 2, wherein the extracting of the first sub-information based on the acceleration curve information of the seismic wave in the vertical direction corresponding to each monitoring point includes:
performing fractal dimension calculation on the acceleration curve in the vertical direction of the seismic waves, and generating a fractal dimension curve based on a value obtained by the fractal dimension calculation;
inputting the fractal dimension curve into a seismic wave arrival time calculation module for calculation, wherein the slope of each moment of the seismic time period is calculated based on the fractal dimension curve, and the slope of each moment is compared to obtain the maximum value of each slope;
and subtracting a preset time length from the time scale corresponding to the maximum slope value of the fractal dimension curve to obtain the moment when the seismic waves reach each monitoring point, and subtracting the preset time length from the time scale corresponding to the point with the first slope being zero in the fractal dimension curve to obtain the disappearance moment of the seismic waves at each monitoring point.
5. A slope stability judging device, comprising:
the system comprises a first acquisition unit, a second acquisition unit and a monitoring unit, wherein the first acquisition unit is used for acquiring first information which comprises data information monitored by a seismic data monitor pre-buried in at least one monitoring point;
the first calculation unit is used for calculating a stability coefficient according to the first information to obtain second information, and the second information comprises stability coefficient data of each monitoring point;
the second acquisition unit is used for acquiring third information, wherein the third information comprises soil pressure parameter information of each monitoring point, anchor cable tension parameter information of each monitoring point and steel strand tension parameter information of each monitoring point;
and the first processing unit is used for preprocessing the preset acceleration in the horizontal direction and the third information, and processing and judging the preprocessed data based on a BP neural network algorithm to obtain a judgment result of the slope stability after the earthquake.
6. The slope stability determination device according to claim 5, wherein the device comprises:
the first calculating subunit is used for carrying out curve conversion calculation according to the data information in the first information to obtain acceleration curve information in the vertical direction of the seismic waves corresponding to each monitoring point;
the first processing subunit is used for extracting first sub information based on acceleration curve information in the vertical direction of seismic waves corresponding to each monitoring point, wherein the first sub information comprises the time when the seismic waves reach each monitoring point and the disappearance time of the seismic waves at each monitoring point;
the second processing subunit is used for obtaining the earthquake time period of each monitoring point based on the first sub information;
the second calculating subunit is used for segmenting each seismic time segment to obtain at least one segmented signal, and calculating based on acceleration curve information of the seismic wave in the vertical direction corresponding to each segmented information and a preset signal response curve calculation formula to obtain at least one signal response curve;
and the third calculation subunit is used for processing all the signal response curves based on a preset amplitude coefficient calculation formula, a fluctuation coefficient calculation formula and a stability coefficient calculation formula to obtain stability coefficient data of each monitoring point.
7. The slope stability determination device according to claim 6, wherein the device comprises:
a fourth calculating subunit, configured to calculate the first information according to a long time window average value calculation formula and a short time window calculation formula, respectively, to obtain a first average value and a second average value, where the first average value is the long time window average value of the first information, and the second information is the short time window average value of the first information;
the fifth calculating subunit is configured to calculate a quotient of the first average value and the second average value to obtain a first calculation result, and compare the first calculation result with a preset first threshold value respectively to obtain comparison result information;
and the third processing subunit is used for sending the time information and the first information which are greater than the first threshold value in the comparison result information to the acceleration data curve generation module to obtain an acceleration curve of the seismic waves in the vertical direction.
8. The slope stability determination device according to claim 6, wherein the device comprises:
the sixth calculating subunit is used for performing fractal dimension calculation on the acceleration curve in the vertical direction of the seismic waves and generating a fractal dimension curve based on a value obtained by the fractal dimension calculation;
the seventh calculating subunit is used for inputting the fractal dimension curve to the seismic wave arrival time calculating module for calculation, calculating the slope of each moment of the seismic time period based on the fractal dimension curve, and comparing the slopes of each moment to obtain the maximum slope of each moment;
and the eighth calculating subunit is used for subtracting the preset time length from the time scale corresponding to the maximum slope value of the fractal dimension curve to obtain the time when the seismic waves reach each monitoring point, and subtracting the preset time length from the time scale corresponding to the point with the first slope being zero in the fractal dimension curve to obtain the disappearance time of the seismic waves at each monitoring point.
9. A slope stability judging device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the slope stability determination method according to any one of claims 1 to 4 when executing the computer program.
10. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the slope stability determination method according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210555277.5A CN114896548B (en) | 2022-05-20 | 2022-05-20 | Slope stability judging method, device and equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210555277.5A CN114896548B (en) | 2022-05-20 | 2022-05-20 | Slope stability judging method, device and equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114896548A true CN114896548A (en) | 2022-08-12 |
| CN114896548B CN114896548B (en) | 2023-04-07 |
Family
ID=82724013
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210555277.5A Active CN114896548B (en) | 2022-05-20 | 2022-05-20 | Slope stability judging method, device and equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114896548B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115329249A (en) * | 2022-10-12 | 2022-11-11 | 西南交通大学 | Bedding slope stability coupling judgment method, device, equipment and medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544791A (en) * | 2012-07-10 | 2014-01-29 | 中国矿业大学(北京) | Underground invasion monitoring system on basis of seismic waves |
| US20170268874A1 (en) * | 2014-08-21 | 2017-09-21 | Nec Corporation | Slope monitoring system, device for slope stability analysis, method, and program |
| US20190250291A1 (en) * | 2018-02-09 | 2019-08-15 | China University Of Geosciences, Beijing | Method and system for acquiring probability of slope failure and destabilization caused by earthquake |
| CN111562611A (en) * | 2020-04-08 | 2020-08-21 | 山东大学 | Semi-supervised depth learning seismic data inversion method based on wave equation drive |
| CN112560587A (en) * | 2020-11-27 | 2021-03-26 | 贵州中建建筑科研设计院有限公司 | Dynamic early warning method and system for convolutional neural network slope crack change |
| CN112668244A (en) * | 2021-01-06 | 2021-04-16 | 西南交通大学 | Slope earthquake stability prediction method, device and equipment and readable storage medium |
| CN112966425A (en) * | 2021-04-06 | 2021-06-15 | 昆明理工大学 | Slope stability prediction and evaluation method |
| CN113642941A (en) * | 2021-10-14 | 2021-11-12 | 西南交通大学 | Slope safety state analysis method, device and equipment and readable storage medium |
| CN114037020A (en) * | 2021-12-01 | 2022-02-11 | 中国石油大学(北京) | Microseism event identification and classification method, device, equipment and storage medium |
| CN114330101A (en) * | 2021-12-01 | 2022-04-12 | 武汉大学 | Slope earthquake slip prediction method and system based on artificial neural network |
| CN114353876A (en) * | 2022-01-07 | 2022-04-15 | 兰州大学 | Loess highway slope health monitoring method |
| US20220129757A1 (en) * | 2020-10-26 | 2022-04-28 | Jiangxi University Of Science And Technology | Prediction method, device and system for rock mass instability stages |
-
2022
- 2022-05-20 CN CN202210555277.5A patent/CN114896548B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103544791A (en) * | 2012-07-10 | 2014-01-29 | 中国矿业大学(北京) | Underground invasion monitoring system on basis of seismic waves |
| US20170268874A1 (en) * | 2014-08-21 | 2017-09-21 | Nec Corporation | Slope monitoring system, device for slope stability analysis, method, and program |
| US20190250291A1 (en) * | 2018-02-09 | 2019-08-15 | China University Of Geosciences, Beijing | Method and system for acquiring probability of slope failure and destabilization caused by earthquake |
| CN111562611A (en) * | 2020-04-08 | 2020-08-21 | 山东大学 | Semi-supervised depth learning seismic data inversion method based on wave equation drive |
| US20220129757A1 (en) * | 2020-10-26 | 2022-04-28 | Jiangxi University Of Science And Technology | Prediction method, device and system for rock mass instability stages |
| CN112560587A (en) * | 2020-11-27 | 2021-03-26 | 贵州中建建筑科研设计院有限公司 | Dynamic early warning method and system for convolutional neural network slope crack change |
| CN112668244A (en) * | 2021-01-06 | 2021-04-16 | 西南交通大学 | Slope earthquake stability prediction method, device and equipment and readable storage medium |
| CN112966425A (en) * | 2021-04-06 | 2021-06-15 | 昆明理工大学 | Slope stability prediction and evaluation method |
| CN113642941A (en) * | 2021-10-14 | 2021-11-12 | 西南交通大学 | Slope safety state analysis method, device and equipment and readable storage medium |
| CN114037020A (en) * | 2021-12-01 | 2022-02-11 | 中国石油大学(北京) | Microseism event identification and classification method, device, equipment and storage medium |
| CN114330101A (en) * | 2021-12-01 | 2022-04-12 | 武汉大学 | Slope earthquake slip prediction method and system based on artificial neural network |
| CN114353876A (en) * | 2022-01-07 | 2022-04-15 | 兰州大学 | Loess highway slope health monitoring method |
Non-Patent Citations (4)
| Title |
|---|
| XINHUA XUE ET.AL: "PREDICTION OF SLOPE STABILITY BASED ON GA-BP HYBRID ALGORITHM", 《NEURAL NETWORK WORLD》 * |
| YANG CHANG-WEI ET.AL: "Improvement of pseudo-static method for slope stability analysis", 《JOURNAL OF MOUNTAIN SCIENCE》 * |
| 刘义建等: "边坡抗震稳定性分析的神经网络法", 《勘察科学技术》 * |
| 郭社锋等: "基于人工神经网络的公路黄土高边坡稳定性预测研究", 《公路交通科技》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115329249A (en) * | 2022-10-12 | 2022-11-11 | 西南交通大学 | Bedding slope stability coupling judgment method, device, equipment and medium |
| CN115329249B (en) * | 2022-10-12 | 2023-02-28 | 西南交通大学 | Bedding slope stability coupling judgment method, device, equipment and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114896548B (en) | 2023-04-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111442827B (en) | Optical fiber passive online monitoring system for transformer winding vibration | |
| CN107944118A (en) | A kind of effective building safety monitoring method | |
| CN114063153B (en) | Method and device for automatically inverting mechanism solution of seismic source | |
| CN105074503B (en) | Earthquake prediction apparatus | |
| CN114896548B (en) | Slope stability judging method, device and equipment and readable storage medium | |
| CN112308124B (en) | Intelligent electricity larceny prevention method for electricity consumption information acquisition system | |
| CN113670434B (en) | Method and device for identifying sound abnormality of substation equipment and computer equipment | |
| CN114723285A (en) | Power grid equipment safety evaluation prediction method | |
| CN104089699A (en) | Substation equipment sound reconstruction algorithm | |
| CN109342828A (en) | A kind of lightening pulse signal detecting method based on frequency domain constant false alarm | |
| CN106546918A (en) | A kind of method for diagnosing faults of Hydropower Unit | |
| CN118197019A (en) | Cable potential safety hazard monitoring and early warning method and system based on thermal degradation products | |
| CN113985479A (en) | Seismic information processing method and device based on edge calculation | |
| CN111027827B (en) | Method and device for analyzing operation risk of bottom-protecting communication network and computer equipment | |
| CN206235747U (en) | Natural disaster risk processing device based on Internet of things | |
| CN117108458A (en) | Marine wind power partition corrosion safety assessment method, system, equipment and storage medium | |
| CN114530880B (en) | Power grid regional load prediction system and method based on big data | |
| Ugwiri et al. | Edge sensor signal processing algorithms for earthquake early detection | |
| RU59842U1 (en) | DEVICE FOR SEISMOACOUSTIC DETECTION AND CLASSIFICATION OF MOVING OBJECTS (OPTIONS) | |
| Harrouni | Using fractal dimension to evaluate wind gusts long-term persistence | |
| CN117074840B (en) | DC charging pile on-line testing system | |
| CN117404257A (en) | Remote monitoring method and system for wind turbine generator | |
| CN115169500A (en) | Compressor equipment abnormal state detection method based on increment and GMM | |
| CN114460466B (en) | Virtual sensor equipment for transmission monitoring and monitoring method thereof | |
| CN114785414B (en) | Identification method and identification system for external acoustic interference of optical fiber composite submarine cable |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |