CN111539568B - Safety monitoring system and method based on unmanned aerial vehicle and three-dimensional modeling technology - Google Patents
Safety monitoring system and method based on unmanned aerial vehicle and three-dimensional modeling technology Download PDFInfo
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Abstract
The invention discloses a safety monitoring system based on an unmanned aerial vehicle and a three-dimensional modeling technology, belongs to the technical field of geology, and aims to provide a safety monitoring system based on the unmanned aerial vehicle and the three-dimensional modeling technology for accurate early warning of slope landslide; the safety prediction system comprises a three-dimensional modeling module, a danger prediction module and an early warning module, wherein the three-dimensional modeling module establishes a slope three-dimensional model according to the acquired information. The invention discloses a safety monitoring method based on an unmanned aerial vehicle and a three-dimensional modeling technology, belongs to the technical field of geology, and aims to provide a safety monitoring technology based on the unmanned aerial vehicle and the three-dimensional modeling technology, which is used for accurately early warning slope landslide.
Description
Technical Field
The invention relates to the technical field of geology, in particular to a safety monitoring system and a safety monitoring method based on an unmanned aerial vehicle and a three-dimensional modeling technology.
Background
The high and steep rock slope is a common type in rock slope stability analysis, is relatively widely distributed in mines, tunnels and the like, and under the action of dead weight stress, along with the proceeding of open-pit mining activities, the side slope free surface gradually increases, and the probability of landslide generation gradually increases. The rear edge of the side slope opens the crack to enable rainfall to continuously infiltrate, the danger caused by landslide is aggravated by hydrostatic pressure and hydrodynamic pressure on the structural surface, and the surface displacement is continuously increased.
After the open-pit gold mine mining task is finished, the mine pit is used as a tailing pond adjacent to a concentrating mill, the tailing pond continuously stores water due to the discharge of industrial water and rainfall in rainy season, and the influence of the pond water on the side slope mainly comprises two aspects: firstly, influence of water on mechanical properties of slope rocks; secondly, the influence of water level lifting circulation on slope rocks. The stability analysis of the slope of the tailings pond of the open pit is obviously influenced by the water level of the pond, and has outburst property, so that large-scale landslide disasters are often caused.
At present, the common landslide early warning and forecasting method has the advantages that the established model is simple, the landslide evaluation index is single, and a landslide early warning and forecasting system is incomplete; the established landslide prediction model is an apparent mathematical theory model, and does not consider main influence factors for generating landslide, such as damage to mechanical properties of side slope rocks caused by water level lifting circulation of an open pit and increase of side slope displacement caused by water; the common mechanical theories of strength reduction, limit balance and the like for analyzing the stability of the side slope have the following defects that the evaluation index is the safety coefficient of the side slope: firstly, corresponding parameters can be obtained only through mechanical experiments of rocks, and the safety evaluation of the side slope can be only qualitatively analyzed, and accurate timing and quantitative analysis cannot be realized; secondly, the change of the slope condition caused by the water level lifting circulation of the slope of the tailing pond and the weakening effect of rainwater on slope rocks cannot be considered; thirdly, the influence of time factors on the slope stability cannot be considered, and the evaluation index is single.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a safety monitoring system based on an unmanned aerial vehicle and a three-dimensional modeling technology for accurate early warning of slope landslide.
The above object of the present invention is achieved by the following technical solutions:
a safety monitoring system based on unmanned aerial vehicle and three-dimensional modeling technology comprises an information acquisition system and a safety prediction system,
the information acquisition system comprises an unmanned aerial vehicle, an information acquisition module and a wireless transmission module, wherein the information acquisition module and the wireless transmission module are arranged on the unmanned aerial vehicle, and the information acquisition module divides the side slope into a plurality of monitoring areas and acquires the gradient, the height, the area and the pictures of the plurality of monitoring areas;
the safety prediction system comprises a three-dimensional modeling module, a danger prediction module and an early warning module,
the information acquired by the information acquisition module is transmitted to the three-dimensional modeling module and the danger prediction module through the wireless transmission module, and the three-dimensional modeling module establishes a three-dimensional slope model according to the acquired information;
the risk prediction module establishes a risk prediction model according to the acquired information, calculates the probability value of collapse of the monitored area through the risk prediction model, and then calculates the risk value of the monitored side slope through the risk judgment model according to the probability values of collapse of the monitored areas;
and the early warning module executes early warning when the value of the danger value reaches a specified early warning reference value.
Through adopting above-mentioned technical scheme, the information acquisition system passes through unmanned aerial vehicle and acquires each item data of monitoring side slope, including the slope of side slope, height, area and photo, and send to the safety prediction system in through wireless transmission module, the three-dimensional modeling module is according to the information establishment side slope three-dimensional model of acquireing and predict the landslide condition of side slope, the early warning module carries out the early warning when it reaches the early warning benchmark value of regulation according to the numerical value of danger value, thereby carry out the multidimension degree evaluation to the side slope, improve the precision of the accurate early warning of side slope landslide.
The present invention in a preferred example may be further configured to: the information acquisition module comprises a microprocessor, a memory, a light sensing module and a light beam emission module, wherein the light sensing module is used for shooting and storing a slope image in the memory and sending the slope image to the safety prediction system through the wireless transmission module;
the light beam emitting module is used for emitting light beams and is captured by the light sensing module, and after the light sensing module senses the first light beam, the distance between the unmanned aerial vehicle and the measuring point is calculated through the microprocessor.
Through adopting above-mentioned technical scheme, adopt light sense module and light beam emission module cooperation to measure data such as the area, the slope, the length of monitored area, provide accurate data support for the safety prediction system.
The present invention in a preferred example may be further configured to: the information acquisition module comprises an angle sensor, the angle sensor is used for sensing the deflection angle of the light sensing module, and the microprocessor corrects the distance between the unmanned aerial vehicle and the measuring point through the deflection angle.
By adopting the technical scheme, the light sensing module and the light beam emitting module can deflect when in use, and the angle sensor corrects the deflection angle, so that the inaccuracy of the measurement result caused by the angle deflection is reduced.
The present invention in a preferred example may be further configured to: the three-dimensional modeling module comprises a three-dimensional modeling unit and a parameter setting unit,
the three-dimensional modeling unit establishes a three-dimensional model through the slope, the height, the area and the picture of the side slope acquired by the information acquisition system;
the parameter setting unit inputs a geological factor and a hydrological factor in the three-dimensional model.
By adopting the technical scheme, the geological factor and the hydrological factor are input into the three-dimensional model of the side slope, so that the three-dimensional model has a multi-dimensional evaluation standard, and the prediction result is more accurate.
The present invention in a preferred example may be further configured to: the safety prediction system further comprises a rainfall intensity acquisition unit, and the rainfall intensity acquisition unit is used for acquiring the rainfall intensity of the monitoring area in real time.
By adopting the technical scheme, the rainfall intensity acquisition unit is used for acquiring the rainfall intensity of the monitoring area in real time and guiding the rainfall intensity into the three-dimensional model, so that the three-dimensional model has real-time performance and the prediction result is more accurate.
The present invention in a preferred example may be further configured to: the risk prediction model E = F (h, s, t, x, l),
wherein h represents the height of the monitoring area; s represents the area of the monitoring area; t represents a geological factor of the monitored area; x represents a hydrological factor of the monitored area; l represents the gradient of the monitored area.
Through adopting above-mentioned technical scheme, including monitored zone's height, area, geological factor, hydrological factor and slope in the danger prediction model, the three-dimensional model of establishing like this is more accurate, and is more accurate to danger prediction's result.
The present invention in a preferred example may be further configured to: and the risk prediction model establishes a feedback dynamic neural network, and each parameter is put into a machine learning model for learning by adopting an artificial intelligence fuzzy control method, so that a corresponding risk prediction model is obtained.
By adopting the technical scheme, the risk prediction model at the training position of the machine learning model is more accurate, and the prediction of the result is more accurate.
The present invention in a preferred example may be further configured to: the danger judging model Y = n1*E1+ n2*E2+ n3*E3+ n4*E4… …, wherein n is1+n2+n3+n4+……=1。
By adopting the technical scheme, because each monitored area has different influence on the whole slope landslide according to different positions, different weights are distributed according to different positions, and the possible prediction precision of the slope possible landslide is higher.
In summary, the invention includes at least one of the following beneficial technical effects:
the second aim of the invention is realized by the following technical scheme:
9. a safety monitoring system using method based on an unmanned aerial vehicle and a three-dimensional modeling technology comprises the following specific steps:
s1: the unmanned aerial vehicle carries an information acquisition module and a wireless transmission module, and the information acquisition module divides the side slope into a plurality of monitoring areas;
s2: selecting two points on the same projection line in a monitored area, enabling the light beam emitting module to emit light beams to the two points in the monitored area, enabling the light sensing module to sense the reflected light beams, calculating the linear distance between the unmanned aerial vehicle and the two points in the monitored area, obtaining the vertical distance between the two points through the unmanned aerial vehicle, and obtaining the gradient of the monitored area through calculation;
s3: after the gradient of the monitored area is obtained, the boundary length and the width of the monitored area are measured through the unmanned aerial vehicle, so that the area of the monitored area is measured, and the measured area, gradient and height of the monitored area are sent to a safety prediction system through the wireless transmission module;
s4: the three-dimensional modeling unit establishes a three-dimensional model through the slope, the height, the area and the picture of the slope acquired by the information acquisition system, and inputs a geological factor and a hydrological factor into the three-dimensional model;
s5: the danger prediction module establishes a danger prediction model according to the acquired information, calculates the probability value of collapse of the monitored area through the danger prediction model, and then calculates the danger value of the monitored side slope through the danger judgment model according to the probability values of collapse of the monitored areas;
s6: and the early warning module executes early warning when the value of the danger value reaches a specified early warning reference value.
By adopting the technical scheme, the possibility of side slope landslide is predicted in advance, particularly in the rainwater season, accidents caused by the side slope landslide can be prevented, and the prevention is made in advance.
The present invention in a preferred example may be further configured to: in S5, the rainfall intensity obtaining unit is configured to obtain the rainfall intensity of the monitored area in real time, and implement adjustment of the hydrological factor according to the rainfall intensity.
By adopting the technical scheme, the rainfall intensity is input into the risk prediction model in real time, and the prediction result can be more accurate.
In summary, the invention includes at least one of the following beneficial technical effects:
1. the information acquisition system acquires various data of the monitored side slope through the unmanned aerial vehicle, wherein the data comprises the slope, the height, the area and the picture of the side slope, and the data is transmitted to the safety prediction system through the wireless transmission module;
2. because the light sensing module and the light beam emitting module can deflect when in use, the angle sensor corrects the deflection angle, and the inaccuracy of the measurement result caused by the angle deflection is reduced;
3. because each monitored area has different influence on the whole slope landslide according to different positions, different weights are distributed according to different positions, and the possible prediction precision of the slope landslide is higher.
Drawings
Fig. 1 is a system block diagram of a safety monitoring system based on unmanned aerial vehicles and three-dimensional modeling technology.
Fig. 2 is a system block diagram of an information acquisition module.
FIG. 3 is a system block diagram of a three-dimensional modeling module.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Example (b): referring to fig. 1, the safety monitoring system based on the unmanned aerial vehicle and the three-dimensional modeling technology disclosed by the invention comprises an information acquisition system and a safety prediction system.
Referring to fig. 1, the information acquisition system includes unmanned aerial vehicle, sets up information acquisition module and the wireless transmission module on unmanned aerial vehicle, and information acquisition module divides into polylith monitoring area with the side slope, acquires polylith monitoring area's slope, height, area and photo.
The wireless transmission module can adopt wireless transmission modules such as Bluetooth, WIFI, 4G, ZIGBE, GPRS and the like.
The unmanned aerial vehicle is also provided with a digital barometer, an electronic gyroscope, a GPS positioning module, an ultra-wave speed measurement or airspeed tube or a micro-differential pressure wind speed sensor which are respectively used for measuring the height, posture, speed and position of the unmanned aerial vehicle.
Referring to fig. 2, the information acquisition module includes a microprocessor, a memory, a light sensing module and a light beam emitting module, wherein the light sensing module is used for capturing and storing the slope image in the memory and sending the slope image to the safety prediction system through the wireless transmission module.
The light beam emission module is used for emitting a light beam and is captured by the light sensing module, and after the light sensing module senses the first light beam, the distance between the unmanned aerial vehicle and the measuring point is calculated through the microprocessor according to a speed time formula.
The information acquisition module comprises an angle sensor, the angle sensor is used for sensing the deflection angle of the light sensing module, and the microprocessor corrects the distance between the unmanned aerial vehicle and the measuring point through the deflection angle.
The information acquisition system is mainly used for acquiring the gradient, the height, the area and the picture of the monitored area and sending the gradient, the height, the area and the picture to the safety prediction system through the wireless transmission module.
Referring to fig. 1 and 3, the safety prediction system includes a three-dimensional modeling module, a risk prediction module, and an early warning module.
And the three-dimensional modeling module establishes a side slope three-dimensional model according to the acquired information and inputs various parameters.
The risk prediction model E = F (h, s, t, x, l), where h represents the height of the monitored area; s represents the area of the monitoring area; t represents a geological factor of the monitored area; x represents a hydrological factor of the monitored area; l represents the gradient of the monitored area.
The three-dimensional modeling module comprises a three-dimensional modeling unit and a parameter setting unit, wherein the three-dimensional modeling unit establishes a three-dimensional model through the slope, the height, the area and the picture of the slope acquired by the information acquisition system. The parameter setting unit inputs a geological factor and a hydrological factor in the three-dimensional model.
The safety prediction system further comprises a rainfall intensity acquisition unit, and the rainfall intensity acquisition unit is used for acquiring the rainfall intensity of the monitoring area in real time.
The danger prediction module establishes a danger prediction model according to the acquired information, calculates the probability value of collapse of the monitored area through the danger prediction model, and then calculates the danger value of the monitored slope through the danger judgment model according to the probability values of collapse of the monitored areas.
And establishing a feedback kinetic neural network by the risk prediction model, and putting each parameter into a machine learning model for learning by adopting an artificial intelligence fuzzy control method so as to obtain a corresponding risk prediction model.
Risk assessment model Y = n1*E1+ n2*E2+ n3*E3+ n4*E4… …, wherein n is1+n2+n3+n4+……=1。
And the early warning module executes early warning when the value of the danger value reaches a specified early warning reference value.
Example (b): the invention discloses a use method of a safety monitoring system based on an unmanned aerial vehicle and a three-dimensional modeling technology, which comprises the following specific steps:
s1: the unmanned aerial vehicle carries an information acquisition module and a wireless transmission module, and the information acquisition module divides the side slope into a plurality of monitoring areas;
s2: selecting two points on the same projection line in a monitored area, enabling the light beam emitting module to emit light beams to the two points in the monitored area, enabling the light sensing module to sense the reflected light beams, calculating the linear distance between the unmanned aerial vehicle and the two points in the monitored area, obtaining the vertical distance between the two points through the unmanned aerial vehicle, and obtaining the gradient of the monitored area through calculation;
s3: after the gradient of the monitored area is obtained, the boundary length and the width of the monitored area are measured through the unmanned aerial vehicle, so that the area of the monitored area is measured, and the measured area, gradient and height of the monitored area are sent to a safety prediction system through the wireless transmission module;
s4: the three-dimensional modeling unit establishes a three-dimensional model through the slope, the height, the area and the picture of the slope acquired by the information acquisition system, and inputs a geological factor and a hydrological factor into the three-dimensional model;
s5: the danger prediction module establishes a danger prediction model according to the acquired information, calculates the probability value of collapse of the monitored area through the danger prediction model, and then calculates the danger value of the monitored side slope through the danger judgment model according to the probability values of collapse of the monitored areas;
s6: and the early warning module executes early warning when the value of the danger value reaches a specified early warning reference value.
In S5, the rainfall intensity obtaining unit is configured to obtain the rainfall intensity of the monitored area in real time, and implement adjustment of the hydrological factor according to the rainfall intensity.
The embodiments of the present invention are preferred embodiments of the present invention, and the scope of the present invention is not limited by these embodiments, so: all equivalent changes made according to the structure, shape and principle of the invention are covered by the protection scope of the invention.
Claims (5)
1. A safety monitoring system using method based on an unmanned aerial vehicle and a three-dimensional modeling technology is characterized by comprising the following specific steps:
s1: the unmanned aerial vehicle carries an information acquisition module and a wireless transmission module, wherein the information acquisition module divides the side slope into a plurality of monitoring areas; the information acquisition module comprises a microprocessor, a memory, a light sensing module and a light beam emitting module; the light sensing module is used for shooting and storing the slope image in the memory and sending the slope image to the safety prediction system through the wireless transmission module; the light beam emitting module is used for emitting light beams and is captured by the light sensing module, and after the light sensing module senses the first light beam, the distance between the unmanned aerial vehicle and the measuring point is calculated through the microprocessor according to a speed time formula; the information acquisition module further comprises an angle sensor, the angle sensor is used for sensing the deflection angle of the light sensing module, and the microprocessor corrects the distance between the unmanned aerial vehicle and the measuring point through the deflection angle; the information acquisition system is mainly used for acquiring the gradient, the height, the area and the picture of the monitored area and sending the gradient, the height, the area and the picture to the safety prediction system through the wireless transmission module; the safety prediction system comprises a three-dimensional modeling module, a danger prediction module and an early warning module;
s2: selecting two points on the same projection line in the monitoring area, wherein the light beam emitting module emits light beams to the two points in the monitoring area, the light sensing module senses the reflected light beams, calculates the linear distance between the unmanned aerial vehicle and the two points in the monitoring area, obtains the vertical distance between the two points in the monitoring area through the unmanned aerial vehicle, and obtains the gradient of the monitoring area through calculation;
s3: after the gradient of the monitoring area is obtained, the boundary length and the width of the monitoring area are measured through an unmanned aerial vehicle, so that the area of the monitoring area is measured, and the measured area, gradient and height of the monitoring area are sent to the safety prediction system through the wireless transmission module;
s4: the three-dimensional modeling module establishes a three-dimensional model through the slope, the height, the area and the picture of the slope acquired by the information acquisition system, and inputs a geological factor and a hydrological factor into the three-dimensional model;
s5: the danger prediction module establishes a danger prediction model according to the acquired information, calculates the probability value of collapse in the monitoring area through the danger prediction model, and then calculates the danger value of the monitored side slope through a danger judgment model according to the probability values of collapse in the monitoring area; the safety prediction system also comprises a rainfall intensity acquisition unit which is used for acquiring the rainfall intensity of the monitoring area in real time; in S5, the rainfall intensity obtaining unit is configured to obtain the rainfall intensity of the monitoring area in real time, and implement adjustment of the hydrological factor according to the rainfall intensity;
s6: and the early warning module executes early warning when the value of the danger value reaches a specified early warning reference value.
2. The safety monitoring system using method based on the unmanned aerial vehicle and the three-dimensional modeling technology is characterized in that the three-dimensional modeling module comprises a three-dimensional modeling unit and a parameter setting unit;
the three-dimensional modeling unit is used for establishing the three-dimensional model through the slope, the height, the area and the picture of the slope acquired by the information acquisition system;
the parameter setting unit is used for inputting the geological factor and the hydrological factor in the three-dimensional model.
3. The method for using the safety monitoring system based on the unmanned aerial vehicle and the three-dimensional modeling technology according to claim 1, wherein the danger prediction model is as follows: e = F (h, s, t, x, l);
wherein h represents the height of the monitored area, s represents the area of the monitored area, t represents the geological factor of the monitored area, x represents the hydrological factor of the monitored area, and l represents the gradient of the monitored area.
4. The method for using the safety monitoring system based on the unmanned aerial vehicle and the three-dimensional modeling technology as claimed in claim 3, wherein the risk prediction model is built with a feedback dynamical neural network, and each parameter is put into a machine learning model for learning by adopting an artificial intelligence fuzzy control method, so that a corresponding risk prediction model is obtained.
5. The method for using the safety monitoring system based on the unmanned aerial vehicle and the three-dimensional modeling technology according to claim 3, wherein the danger judgment model is as follows:
wherein
，
The different weights assigned to different positions of each monitoring area are shown, i represents the sequence of the individual positions of the monitoring areas, and N represents the total value of the monitoring areas.
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| CN112633256A (en) * | 2021-01-27 | 2021-04-09 | 河南浩宇空间数据科技有限责任公司 | Hydrology test system based on laser radar point cloud technology |
| CN113409549A (en) * | 2021-06-11 | 2021-09-17 | 中铁西南科学研究院有限公司 | Landslide monitoring and early warning system in mountain canyon region |
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| CN111504268B (en) * | 2020-04-22 | 2020-11-24 | 深圳市地质局 | Intelligent early warning and forecasting method for dangerous case of soil slope |
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