CN114170761A - Slope state monitoring system and method based on big data - Google Patents

Slope state monitoring system and method based on big data Download PDF

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CN114170761A
CN114170761A CN202111400375.3A CN202111400375A CN114170761A CN 114170761 A CN114170761 A CN 114170761A CN 202111400375 A CN202111400375 A CN 202111400375A CN 114170761 A CN114170761 A CN 114170761A
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vegetation
slope
unit
module
weather
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张海东
赵广臣
顾军
孟胜国
杨海庆
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Shanxi Institute of Technology
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
    • G08B21/02—Alarms for ensuring the safety of persons
    • G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
    • 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
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data

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Abstract

The invention discloses a slope state monitoring system based on big data, and belongs to the technical field of slope state monitoring. The system comprises a slope information acquisition module, a greening distribution module, a weather module, a prediction module, a scanning module, a comparison module, a monitoring module and an early warning module; the output end of the slope information acquisition module, the greening distribution module and the weather module are sequentially connected; the output end of the weather module is connected with the input end of the prediction module; the output end of the prediction module is connected with the input end of the scanning module; the output end of the scanning module is connected with the input end of the comparison module; the output end of the comparison module is connected with the input end of the monitoring module; the output end of the monitoring module is connected with the input end of the early warning module; meanwhile, a slope state monitoring method based on big data is provided, early warning is carried out when slope vegetation is lost, and landslide events are prevented.

Description

Slope state monitoring system and method based on big data
Technical Field
The invention relates to the technical field of slope state monitoring, in particular to a slope state monitoring system and method based on big data.
Background
The side slope refers to a slope surface with a certain slope and formed on two sides of the roadbed to ensure the stability of the roadbed, and the greening of the side slope mainly combines engineering measures and biological measures, builds a base plate suitable for plant growth on the side slope, and achieves the anti-washing capacity of reinforcing the side slope and the surface of the slope by means of the adhesive force between plant roots and mutual coiling between the roots and roots.
The slope greening ecological protection can not only repair water sources and reduce water and soil loss, but also purify air, protect ecology, beautify environment, intercept rainwater, slow down runoff and consolidate soil, be favorable for water and soil conservation, enhance the stability of the slope, ensure human living safety, and have good economic benefits, social benefits and ecological benefits, and is also positive, therefore, attention should be paid to monitoring of vegetation states on the slope for monitoring of the slope states, further control is advanced for vegetation landscape and vegetation efficiency, the slope vegetation can be ensured to show bright landscape, meanwhile, the water sources can be effectively maintained, the risk of landslide does not appear on the slope is ensured, on monitoring of the slope, effective prediction is also needed to be carried out according to the basic information of the slope, the probability of landslide occurrence on the slope is obtained in advance, and road driving safety is ensured as much as possible.
In the current technology, the prediction effect is not accurate, the vegetation condition is not monitored, and the problem to be solved urgently is solved, meanwhile, after the vegetation is dead, the vegetation easily slips to the road to influence driving, serious people can cause traffic accidents, and therefore people need a system capable of monitoring the slope state to solve the existing problem.
Disclosure of Invention
The invention aims to provide a slope state monitoring system and method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
a slope state monitoring system based on big data comprises a slope information acquisition module, a greening distribution module, a weather module, a prediction module, a scanning module, a comparison module, a monitoring module and an early warning module;
the side slope information acquisition module is used for acquiring and recording basic information of a side slope to be monitored; the greening distribution module is used for designing a vegetation greening distribution scheme on the side slope; the weather module is used for acquiring weather forecast; the prediction module is used for predicting the survival state of vegetation under various weather conditions; the scanning module is used for scanning vegetation states on a slope; the comparison module is used for comparing the vegetation on the side slope with the vegetation growing normally and judging the survival state of the vegetation on the side slope; the monitoring module is used for monitoring the area where the vegetation or the slope soil and stone slide down; the early warning module is used for sending out early warning to remind workers to overhaul;
the output end of the side slope information acquisition module is connected with the input end of the greening distribution module; the output end of the greening distribution module is connected with the input end of the weather module; the output end of the weather module is connected with the input end of the prediction module; the output end of the prediction module is connected with the input end of the scanning module; the output end of the scanning module is connected with the input end of the comparison module; the output end of the comparison module is connected with the input end of the monitoring module; the output end of the monitoring module is connected with the input end of the early warning module.
According to the technical scheme, the side slope information acquisition module comprises an area acquisition unit, an age acquisition unit, a stratum lithology acquisition unit and an underground water condition acquisition unit;
the area acquisition unit is used for acquiring the area condition of the side slope; the age limit acquisition unit is used for acquiring the service life of the side slope; the stratum lithology acquisition unit is used for acquiring a rock stratum structure and stratum lithology of the side slope; the underground water condition acquisition unit is used for acquiring the influence degree of underground water on the side slope;
the output ends of the area acquisition unit, the age limit acquisition unit, the stratum lithology acquisition unit and the underground water condition acquisition unit are all connected to the input end of the greening distribution module;
the greening distribution module comprises a greening unit and a distribution unit;
the greening unit is used for selecting proper greening vegetation to plant according to the lithology of the stratum; the distribution unit is used for calculating the ground grabbing area of the vegetation roots and the vegetation greening coverage area to distribute the vegetation, so that the appearance is ensured, and the soil maintenance is promoted;
the output end of the greening unit is connected with the input end of the distribution unit; the output end of the distribution unit is connected with the input end of the weather module.
According to the technical scheme, the weather module comprises a weather receiving unit and an output unit;
the weather receiving unit is used for receiving and recording weather conditions; the output unit is used for outputting the weather condition to the prediction module according to the time period;
the output end of the weather receiving unit is connected with the input end of the output unit; the output end of the output unit is connected with the input end of the prediction module;
the prediction module comprises a historical database, a weight adjusting unit and a probability influence unit;
the historical database is used for collecting the historical data condition of the slope landslide; the weight adjusting unit is used for adjusting the weight of each influence factor in the slope landslide process; the probability influence unit is used for establishing initial influence probability of each influence factor according to historical data;
the output end of the historical database is connected with the input ends of the weight adjusting unit and the probability influence unit; the output ends of the weight adjusting unit and the probability influencing unit are connected with the input end of the scanning module.
According to the technical scheme, the scanning module comprises a control unit, a three-dimensional scanning unit and an analysis unit;
the control unit is used for controlling the opening and closing of the three-dimensional scanning unit according to the weather condition; the three-dimensional scanning unit is used for scanning the vegetation state in bad weather and generating a scanning image to the analysis unit; the analysis unit is used for analyzing the states of the leaves and the roots of the vegetation of the scanogram;
the output end of the control unit is connected with the input end of the three-dimensional scanning unit; the output end of the three-dimensional scanning unit is connected with the input end of the analysis unit; the output end of the analysis unit is connected with the input end of the comparison module;
the comparison module comprises a data unit and a comparison unit;
the data unit is used for acquiring growth data of normal vegetation; the comparison unit is used for comparing the states of the normal vegetation and the vegetation which has undergone poor weather and judging the survival rate;
the output end of the data unit is connected with the input end of the comparison unit; and the output end of the comparison unit is connected with the input end of the monitoring module.
According to the technical scheme, the monitoring module comprises a vegetation sliding region unit and a greening area monitoring unit;
the vegetation slip region unit is used for monitoring a vegetation slip region and whether the driving of road vehicles is influenced; the greening area monitoring unit is used for monitoring the change of the vegetation greening coverage area and the soil grabbing area of the vegetation roots and sending a message to the early warning module when the change is lower than a threshold value;
the output ends of the vegetation sliding region unit and the greening area monitoring unit are connected with the input end of the early warning module.
According to the technical scheme, the early warning module comprises an early warning unit and a management unit;
the early warning unit is used for sending out early warning information; the management unit is used for receiving the early warning information and informing a manager to perform management operation;
the output end of the early warning unit is connected with the input end of the management unit.
A slope state monitoring method based on big data comprises the following steps:
s1, acquiring basic information of the side slope and establishing a vegetation greening distribution scheme;
s2, acquiring weather influence factors, predicting the risk of landslide of the side slope, and establishing a prediction model;
s3, obtaining a three-dimensional scanning image of vegetation greening on the slope, and establishing a similarity model to obtain the survival condition of vegetation;
s4, calculating a vegetation sliding region, reacquiring the soil-grabbing area of the vegetation root, correcting the prediction model, and establishing danger level early warning.
According to the technical scheme, in step S1, the basic information of the side slope comprises side slope area, age, stratum lithology and underground water influence factors;
the vegetation greening distribution scheme comprises the following steps:
obtaining vegetation cover area, marked as S1；
Obtaining the area of the side slope, marked as S0；
Then the vegetation greening coverage rate Q exists1＝S1/S0；
Setting vegetation greening coverage rate threshold value to be Qmax；
In the vegetation greening distribution scheme, Q is present1≤Qmax；
Obtaining the soil-grabbing area of the vegetation root and recording as S2；
Then the vegetation soil-protecting coverage rate Q exists2＝S2/S0；
Setting the vegetation soil-protecting coverage rate threshold value to be Qmin；
In the vegetation greening distribution scheme, Q is present2≥Qmin。
In the above step, the vegetation greening coverage area is the area of the green part of the vegetation displayed on the side slope under the condition of the top view; the vegetation root soil grabbing area is the area of the vegetation root soil grabbing on the side slope, namely the area actually occupied by the vegetation; because the vegetation can have luxuriant branches and leaves in the growing process, the occupied area of the roots and the occupied area of the leaves are different;
in the vegetation treatment of the side slope, the landscape is seriously influenced due to the extreme luxuriance, and the maintenance cost is increased; and the vegetation cover rate threshold and the vegetation soil protection cover rate threshold are set for adjustment, so that a vegetation cover distribution scheme is generated.
According to the above technical solution, in the steps S2-S3, the predicting and early warning further includes:
the prediction process comprises the following steps:
s2-1, acquiring weather conditions in time T, wherein T is a positive integer and is a unit of day, and the weather conditions include good weather, severe weather and extreme weather;
such as sunny, cloudy, etc.; such as light rain, medium rain, gusts of rain, below-six winds, etc.; extreme weather such as heavy rain, hail, wind above six levels, etc.;
s2-2, acquiring basic information of the side slope, including the angle of the side slope, and recording the angle as theta; the soil-grabbing area of the vegetation root on the side slope is marked as S2(ii) a The slope age limit is marked as N years; the lithology of the slope stratum; the stratum lithology comprises a soil slope and a rock slope; groundwater impact factor of the slope; the angle of the side slope is the included angle between the side slope and the horizontal plane;
the groundwater influence factor of the slope comprises x1、x2(ii) a Said x1The influence of underground water is large, and the side slope can be softened or corroded by rocks; said x2The influence of underground water is small, and the weight of the rock mass of the side slope can be increased;
the groundwater influencing power is related to weather; if there is not less than T in T time1In case of heavy or heavy weather, the groundwater influence factor will vary from x2Upgrade to x1(ii) a Wherein T is1Is a threshold number of days of influence;
s2-3, acquiring historical data of slope landslide and establishing a prediction model;
P＝w1*k1+w2*k2+w3*k3+w4*k4+w5*k5+w6*k6
wherein P is the relative landslide prediction probability of each side slope; w is a1、w2、w3、w4、w5、w6Respectively the angle theta of the side slope and the soil-holding area S of the vegetation root on the side slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence weight; and w1+w2+w3+w4+w5+w6＝1；k1、k2、k3、k4、k5、k6Respectively is the angle theta of the slope under the historical data and the soil-holding area S of the vegetation roots on the slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence probability;
the relative landslide prediction probability refers to the relative landslide prediction probability among the slopes, namely the higher the P value is, the higher the landslide probability of all the slopes is, and the important protection is needed;
be provided with standard side slope, include:
the gradient ratio is 1: 1; the gradient ratio is the ratio of the vertical height and the horizontal width of the slope; namely the angle theta of the side slope is 45 degrees; the soil grabbing area of the vegetation roots on the side slope is S; the slope age is 5 years;
then there is
Figure BDA0003371369840000071
Figure BDA0003371369840000072
Figure BDA0003371369840000073
Figure BDA0003371369840000074
Wherein, the delta theta is the number of the slopes with the angle exceeding 45 degrees of the slope where the landslide occurs in the historical data; the delta S is the number of the side slopes with the soil-holding area of the vegetation roots on the side slopes with landslides smaller than S in the historical data; the delta N is the number of the slopes of which the slope age exceeds N when landslide occurs in the historical data; m is the number of slopes with the ratio of non-good weather to good weather exceeding 1 in the time period T of the slopes with landslides in the historical data; the non-good weather comprises extreme weather and severe weather; u is the number of slopes with landslides in the historical data;
at the same time:
when the lithology of the side slope stratum is a soil side slope, k4＝v1(ii) a When it is a rocky slope, k4＝v2；
The influence factor of underground water on the side slope is x1When k is5＝v3(ii) a The groundwater influence factor of the side slope is x2When k is5＝v4；
Wherein v is1、v2、v3、v4The values are constant values, and the system performs self-setting according to historical rules;
s2-4, establishing a weight mechanism, comprising:
according to the initial k1、k2、k3、k4、k5、k6Size of (a) to w1、w2、w3、w4、w5、w6Change ordering of, initial k1、k2、k3、k4、k5、k6The larger the weight is, the more forward the weight is;
set the rank weight, denoted as a1The grade weight is that the corresponding weight changes once when each influence factor in the prediction model changes once relatively, and the influence weights of other influence factors are reduced on average;
wherein the relative change times of the angle of the slope is y1(ii) a Then
Figure BDA0003371369840000075
Wherein i1The angle can be changed according to the standardThe precision required by the system is set to be 1 degree, 5 degrees, 10 degrees and the like, and the smaller the numerical value is, the more accurate the numerical value is;
wherein the vegetation root on the side slope grabs the soil area S2Relative number of changes of y2(ii) a Then
Figure BDA0003371369840000076
Wherein i2The area is changed for the standard, the area can be automatically set according to the accuracy required by the system, and the smaller the numerical value is, the more accurate the numerical value is;
wherein the relative change number of the age N of the side slope is y3(ii) a Then
Figure BDA0003371369840000081
Wherein i3The standard change period can be set to be 1 year, 5 years, 10 years and the like according to the required accuracy of the system, and the smaller the numerical value is, the more accurate the numerical value is;
wherein the lithology of the slope stratum does not change relatively;
wherein the groundwater influence factor of the slope is from x2To x1Is recorded as a relative change;
wherein the relative change number of weather is y4(ii) a Then
Figure BDA0003371369840000082
Wherein i4For standard change of weather days, M1Days of non-good weather; m2Good weather days; i.e. i4The accuracy required by the system can be automatically set to be 0.5 day, 1 day and the like, and the smaller the numerical value is, the more accurate the numerical value is;
for example the angle of the slope is 60 deg., setting i1The angle is 5 degrees, namely, the angle of the side slope is relatively changed every 5 degrees, and the predicted influence rate of the side slope angle on the side slope landslide is increased; then in this application, y13; then there is a new w1＝w1+3a1(ii) a And new w2＝w2-3a1(iii)/5; performing sequence calculation according to the ranking sequence; obtaining a weight mechanism of each influence factor of the slope, which is beneficial to more accurate prediction;
acquiring a three-dimensional scanning image of vegetation greening on the side slope after the non-favorable weather is finished; obtaining a scanning map of normal growth of vegetation;
setting the height of the vegetation from the ground, the size of the vegetation and the decay degree of the roots as similarity contrast factors; establishing a similarity model, and measuring and calculating the survival probability of vegetation;
Figure BDA0003371369840000083
wherein d represents the similarity of vegetation which has undergone poor weather to normally produced vegetation;
Figure BDA0003371369840000084
a similarity contrast factor b representing vegetation that has experienced poor weather;
Figure BDA0003371369840000085
a similarity contrast factor b representing a normally produced vegetation; n represents the number of similarity contrast factors;
setting the threshold value to dmin(ii) a If d is less than dminThen the vegetation is judged to be non-viable.
According to the above technical solution, in step S4, the method further includes:
acquiring the height of the vegetation which cannot survive, and recording the height as h;
then there is j1H/sin θ; wherein j1The distance of vegetation on the side slope;
then there is
Figure BDA0003371369840000091
Wherein a isAcceleration 1Is the glide acceleration; the speed when the slope reaches the bottom of the slope can be obtained;
then there is
Figure BDA0003371369840000092
Wherein a isAcceleration 2Deceleration acceleration for road slip; the farthest distance j of vegetation sliding can be obtained2；
The above conditions are all established in an ideal situation, i.e. without friction and hindrance, so that j can be put into practice2As the maximum sliding area distance;
setting an area of influence threshold to j3(ii) a If j is present2Exceeds j3If yes, sending out early warning information;
obtaining the soil-grabbing area of the vegetation roots which cannot survive and recording as S4；
Obtaining the vegetation greening coverage area which can not survive and is marked as S5；
Recalculating vegetation coverage area, denoted as S1-S5；
Then the vegetation greening coverage rate Q exists1＝S1-S5/S0；
Obtaining the soil-grabbing area of the vegetation root and recording as S2-S4；
Then the vegetation soil-protecting coverage rate Q exists2＝S2-S4/S0；
If Q is present1、Q2And when the threshold value is not met, sending out early warning information.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention can monitor the vegetation greening coverage area and the vegetation root soil grabbing area on the side slope, thereby carrying out landscape monitoring of the side slope state and vegetation sliding area monitoring, ensuring the side slope landscape and road driving safety;
2. the method can predict the landslide probability of the side slope according to the influence factors of the side slope, continuously adjust the weight, enhance the prediction precision, ensure that the special prediction effect is realized on each different side slope, can adjust in real time according to the weather, can establish the relative landslide probability of the side slope, realize key protection on the heavy side slope, save the human resources and ensure the road driving safety.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic flow chart of a slope condition monitoring system and method based on big data according to the present invention;
fig. 2 is a schematic step diagram of a slope state monitoring method based on big data according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. 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.
Referring to fig. 1-2, the present invention provides the following technical solutions:
a slope state monitoring system based on big data comprises a slope information acquisition module, a greening distribution module, a weather module, a prediction module, a scanning module, a comparison module, a monitoring module and an early warning module;
the side slope information acquisition module is used for acquiring and recording basic information of a side slope to be monitored; the greening distribution module is used for designing a vegetation greening distribution scheme on the side slope; the weather module is used for acquiring weather forecast; the prediction module is used for predicting the survival state of vegetation under various weather conditions; the scanning module is used for scanning vegetation states on a slope; the comparison module is used for comparing the vegetation on the side slope with the vegetation growing normally and judging the survival state of the vegetation on the side slope; the monitoring module is used for monitoring the area where the vegetation or the slope soil and stone slide down; the early warning module is used for sending out early warning to remind workers to overhaul;
the output end of the side slope information acquisition module is connected with the input end of the greening distribution module; the output end of the greening distribution module is connected with the input end of the weather module; the output end of the weather module is connected with the input end of the prediction module; the output end of the prediction module is connected with the input end of the scanning module; the output end of the scanning module is connected with the input end of the comparison module; the output end of the comparison module is connected with the input end of the monitoring module; the output end of the monitoring module is connected with the input end of the early warning module.
The side slope information acquisition module comprises an area acquisition unit, an age acquisition unit, a stratum lithology acquisition unit and an underground water condition acquisition unit;
the area acquisition unit is used for acquiring the area condition of the side slope; the age limit acquisition unit is used for acquiring the service life of the side slope; the stratum lithology acquisition unit is used for acquiring a rock stratum structure and stratum lithology of the side slope; the underground water condition acquisition unit is used for acquiring the influence degree of underground water on the side slope;
the output ends of the area acquisition unit, the age limit acquisition unit, the stratum lithology acquisition unit and the underground water condition acquisition unit are all connected to the input end of the greening distribution module;
the greening distribution module comprises a greening unit and a distribution unit;
the greening unit is used for selecting proper greening vegetation to plant according to the lithology of the stratum; the distribution unit is used for calculating the vegetation root gripping area and the vegetation greening coverage area to distribute the vegetation;
the output end of the greening unit is connected with the input end of the distribution unit; the output end of the distribution unit is connected with the input end of the weather module.
The weather module comprises a weather receiving unit and an output unit;
the weather receiving unit is used for receiving and recording weather conditions; the output unit is used for outputting the weather condition to the prediction module according to the time period;
the output end of the weather receiving unit is connected with the input end of the output unit; the output end of the output unit is connected with the input end of the prediction module;
the prediction module comprises a historical database, a weight adjusting unit and a probability influence unit;
the historical database is used for collecting the historical data condition of the slope landslide; the weight adjusting unit is used for adjusting the weight of each influence factor in the slope landslide process; the probability influence unit is used for establishing initial influence probability of each influence factor according to historical data;
the output end of the historical database is connected with the input ends of the weight adjusting unit and the probability influence unit; the output ends of the weight adjusting unit and the probability influencing unit are connected with the input end of the scanning module.
The scanning module comprises a control unit, a three-dimensional scanning unit and an analysis unit;
the control unit is used for controlling the opening and closing of the three-dimensional scanning unit according to the weather condition; the three-dimensional scanning unit is used for scanning the vegetation state in bad weather and generating a scanning image to the analysis unit; the analysis unit is used for analyzing the states of the leaves and the roots of the vegetation of the scanogram;
the output end of the control unit is connected with the input end of the three-dimensional scanning unit; the output end of the three-dimensional scanning unit is connected with the input end of the analysis unit; the output end of the analysis unit is connected with the input end of the comparison module;
the comparison module comprises a data unit and a comparison unit;
the data unit is used for acquiring growth data of normal vegetation; the comparison unit is used for comparing the states of the normal vegetation and the vegetation which has undergone poor weather and judging the survival rate;
the output end of the data unit is connected with the input end of the comparison unit; and the output end of the comparison unit is connected with the input end of the monitoring module.
The monitoring module comprises a vegetation sliding region unit and a greening area monitoring unit;
the vegetation slip region unit is used for monitoring a vegetation slip region and whether the driving of road vehicles is influenced; the greening area monitoring unit is used for monitoring the change of the vegetation greening coverage area and the soil grabbing area of the vegetation roots and sending a message to the early warning module when the change is lower than a threshold value;
the output ends of the vegetation sliding region unit and the greening area monitoring unit are connected with the input end of the early warning module.
The early warning module comprises an early warning unit and a management unit;
the early warning unit is used for sending out early warning information; the management unit is used for receiving the early warning information and informing a manager to perform management operation;
the output end of the early warning unit is connected with the input end of the management unit.
A slope state monitoring method based on big data comprises the following steps:
s1, acquiring basic information of the side slope and establishing a vegetation greening distribution scheme;
s2, acquiring weather influence factors, predicting the risk of landslide of the side slope, and establishing a prediction model;
s3, obtaining a three-dimensional scanning image of vegetation greening on the slope, and establishing a similarity model to obtain the survival condition of vegetation;
s4, calculating a vegetation sliding region, reacquiring the soil-grabbing area of the vegetation root, correcting the prediction model, and establishing danger level early warning.
In step S1, the basic information of the slope includes slope area, age, stratigraphic lithology, and groundwater influence factor;
the vegetation greening distribution scheme comprises the following steps:
obtaining vegetation cover area, marked as S1；
Obtaining the area of the side slope, marked as S0；
Then the vegetation greening coverage rate Q exists1＝S1/S0；
Setting vegetation greening coverage rate threshold value to be Qmax；
In the vegetation greening distribution scheme, Q is present1≤Qmax；
Obtaining vegetation rootsArea of partial soil pick-up, marked as S2；
Then the vegetation soil-protecting coverage rate Q exists2＝S2/S0；
Setting the vegetation soil-protecting coverage rate threshold value to be Qmin；
In the vegetation greening distribution scheme, Q is present2≥Qmin。
In steps S2-S3, the predicting and warning further includes:
the prediction process comprises the following steps:
s2-1, acquiring weather conditions in time T, wherein T is a positive integer and is a unit of day, and the weather conditions include good weather, severe weather and extreme weather;
s2-2, acquiring basic information of the side slope, including the angle of the side slope, and recording the angle as theta; the soil-grabbing area of the vegetation root on the side slope is marked as S2(ii) a The slope age limit is marked as N years; the lithology of the slope stratum; the stratum lithology comprises a soil slope and a rock slope; groundwater impact factor of the slope; the angle of the side slope is the included angle between the side slope and the horizontal plane;
the groundwater influence factor of the slope comprises x1、x2(ii) a Said x1The influence of underground water is large, and the side slope can be softened or corroded by rocks; said x2The influence of underground water is small, and the weight of the rock mass of the side slope can be increased;
the groundwater influencing power is related to weather; if there is not less than T in T time1In case of heavy or heavy weather, the groundwater influence factor will vary from x2Upgrade to x1(ii) a Wherein T is1Is a threshold number of days of influence;
s2-3, acquiring historical data of slope landslide and establishing a prediction model;
P＝w1*k1+w2*k2+w3*k3+w4*k4+w5*k5+w6*k6
wherein P is the relative landslide prediction probability of each side slope; w is a1、w2、w3、w4、w5、w6Respectively the angle theta of the side slope and the soil-holding area S of the vegetation root on the side slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence weight; and w1+w2+w3+w4+w5+w6＝1；k1、k2、k3、k4、k5、k6Respectively is the angle theta of the slope under the historical data and the soil-holding area S of the vegetation roots on the slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence probability;
be provided with standard side slope, include:
the gradient ratio is 1: 1; the gradient ratio is the ratio of the vertical height and the horizontal width of the slope; namely the angle theta of the side slope is 45 degrees; the soil grabbing area of the vegetation roots on the side slope is S; the slope age is 5 years;
then there is
Figure BDA0003371369840000151
Figure BDA0003371369840000152
Figure BDA0003371369840000153
Figure BDA0003371369840000154
Wherein, the delta theta is the number of the slopes with the angle exceeding 45 degrees of the slope where the landslide occurs in the historical data; the delta S is the number of the side slopes with the soil-holding area of the vegetation roots on the side slopes with landslides smaller than S in the historical data; the delta N is the number of the slopes of which the slope age exceeds N when landslide occurs in the historical data; m is the number of slopes with the ratio of non-good weather to good weather exceeding 1 in the time period T of the slopes with landslides in the historical data; the non-good weather comprises extreme weather and severe weather; u is the number of slopes with landslides in the historical data;
at the same time:
when the lithology of the side slope stratum is a soil side slope, k4＝v1(ii) a When it is a rocky slope, k4＝v2；
The influence factor of underground water on the side slope is x1When k is5＝v3(ii) a The groundwater influence factor of the side slope is x2When k is5＝v4；
Wherein v is1、v2、v3、v4The values are constant values, and the system performs self-setting according to historical rules;
s2-4, establishing a weight mechanism, comprising:
according to the initial k1、k2、k3、k4、k5、k6Size of (a) to w1、w2、w3、w4、w5、w6Change ordering of, initial k1、k2、k3、k4、k5、k6The larger the weight is, the more forward the weight is;
set the rank weight, denoted as a1The grade weight is that the corresponding weight changes once when each influence factor in the prediction model changes once relatively, and the influence weights of other influence factors are reduced on average;
wherein the relative change times of the angle of the slope is y1(ii) a Then
Figure BDA0003371369840000155
Wherein i1Is a standard variation angle;
wherein the vegetation root on the side slope grabs the soil area S2Relative number of changes of y2(ii) a Then
Figure BDA0003371369840000156
Wherein i2Is a standard change area;
wherein the relative change number of the age N of the side slope is y3(ii) a Then
Figure BDA0003371369840000161
Wherein i3Standard change years;
wherein the lithology of the slope stratum does not change relatively;
wherein the groundwater influence factor of the slope is from x2To x1Is recorded as a relative change;
wherein the relative change number of weather is y4(ii) a Then
Figure BDA0003371369840000162
Wherein i4For standard change of weather days, M1Days of non-good weather; m2Good weather days;
acquiring a three-dimensional scanning image of vegetation greening on the side slope after the non-favorable weather is finished; obtaining a scanning map of normal growth of vegetation;
setting the height of the vegetation from the ground, the size of the vegetation and the decay degree of the roots as similarity contrast factors; establishing a similarity model, and measuring and calculating the survival probability of vegetation;
Figure BDA0003371369840000163
wherein d represents the similarity of vegetation which has undergone poor weather to normally produced vegetation;
Figure BDA0003371369840000164
a similarity contrast factor b representing vegetation that has experienced poor weather;
Figure BDA0003371369840000165
a similarity contrast factor b representing a normally produced vegetation; n represents the number of similarity contrast factors;
setting the threshold value to dmin(ii) a If d is less than dminThen the vegetation is judged to be non-viable.
In step S4, the method further includes:
acquiring the height of the vegetation which cannot survive, and recording the height as h;
then there is j1H/sin θ; wherein j1The distance of vegetation on the side slope;
then there is
Figure BDA0003371369840000166
Wherein a isAcceleration 1Is the glide acceleration; the speed when the slope reaches the bottom of the slope can be obtained;
then there is
Figure BDA0003371369840000171
Wherein a isAcceleration 2Deceleration acceleration for road slip; the farthest distance j of vegetation sliding can be obtained2；
The above conditions are all established in an ideal situation, i.e. without friction and hindrance, so that j can be put into practice2As the maximum sliding area distance;
setting an area of influence threshold to j3(ii) a If j is present2Exceeds j3If yes, sending out early warning information;
obtaining the soil-grabbing area of the vegetation roots which cannot survive and recording as S4；
Obtaining the vegetation greening coverage area which can not survive and is marked as S5；
Recalculating vegetation coverage area, denoted as S1-S5；
Then the vegetation greening coverage rate Q exists1＝S1-S5/S0；
Obtaining the soil-grabbing area of the vegetation root and recording as S2-S4；
Then the vegetation soil-protecting coverage rate Q exists2＝S2-S4/S0；
If Q is present1、Q2And when the threshold value is not met, sending out early warning information.
In this embodiment:
the slope A is a rock slope; the age limit is 5 years; the angle of the side slope is marked as theta which is 50 degrees;
acquiring weather conditions within 15 days, wherein the good weather is 5 days, the bad weather is 5 days, and the extreme weather is 5 days;
acquiring the soil-holding area of the vegetation roots on the side slope, and recording the area as 10 square meters;
the groundwater influence factor of the side slope is x2；
Acquiring historical data of slope landslide and establishing a prediction model;
P＝w1*k1+w2*k2+w3*k3+w4*k4+w5*k5+w6*k6
wherein P is the relative landslide prediction probability of each side slope; w is a1、w2、w3、w4、w5、w6Respectively the angle theta of the side slope and the soil-holding area S of the vegetation root on the side slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence weight; and w1+w2+w3+w4+w5+w6＝1；k1、k2、k3、k4、k5、k6Respectively is the angle theta of the slope under the historical data and the soil-holding area S of the vegetation roots on the slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence probability;
be provided with standard side slope, include:
the gradient ratio is 1: 1; the gradient ratio is the ratio of the vertical height and the horizontal width of the slope; namely the angle theta of the side slope is 45 degrees; the soil grabbing area of the vegetation roots on the side slope is S, and is 8 square meters; the slope age is 5 years;
then there is
Figure BDA0003371369840000181
Figure BDA0003371369840000182
Figure BDA0003371369840000183
Figure BDA0003371369840000184
Wherein, the delta theta is the number of the slopes with the angle exceeding 45 degrees of the slope with landslide in the historical data and is 6; the delta S is the number of the side slopes with the vegetation root soil-holding area smaller than S on the side slope with landslide in the historical data and is 7; the delta N is the number of the slopes with the slope age exceeding N when landslide occurs in the historical data and is 8; m is the number of the slopes with the ratio of the non-good weather to the good weather of the slopes with landslides in the historical data exceeding 1 in the T time period, and is 9; the non-good weather comprises extreme weather and severe weather; u is the number of slopes with landslides in the historical data;
setting U as 10; then there is
Figure BDA0003371369840000185
Figure BDA0003371369840000186
Figure BDA0003371369840000187
Figure BDA0003371369840000188
At the same time:
when the lithology of the side slope stratum is a soil side slope, k4＝v1(ii) a When it is a rocky slope, k4＝v2；
Then k is4＝v2＝0.2
The influence factor of underground water on the side slope is x1When k is5＝v3(ii) a The groundwater influence factor of the side slope is x2When k is5＝v4；
Then k is5＝v4＝0.3；
A weight establishment mechanism comprising:
according to the initial k1、k2、k3、k4、k5、k6Size of (a) to w1、w2、w3、w4、w5、w6Change ordering of, initial k1、k2、k3、k4、k5、k6The larger the weight is, the more forward the weight is;
then the order is k6、k3、k2、k1、k5、k4；
Set the rank weight, denoted as a1The grade weight is that the corresponding weight changes once when each influence factor in the prediction model changes once relatively, and the influence weights of other influence factors are reduced on average;
a1＝0.05；
initial weight w1、w2、w3、w4、w5、w60.2, 0.1, 0.2, respectively;
wherein the relative change number of weather is y4(ii) a Then
Figure BDA0003371369840000191
Wherein i4Standard change weather days, 1 day; m1Days of non-good weather; m2Good weather days; then
Figure BDA0003371369840000192
The weights are changed for the first time to 0.15, 0.05, 0.45;
wherein the relative change number of the age N of the side slope is y3(ii) a Then
Figure BDA0003371369840000193
Wherein i3Standard change years of 2 years, then
Figure BDA0003371369840000194
The second change weights are 0.15, 0.05, 0.45;
wherein the vegetation root on the side slope grabs the soil area S2Relative number of changes of y2(ii) a Then
Figure BDA0003371369840000195
Wherein i2Standard change area, 1 square meter; then there is
Figure BDA0003371369840000196
The third change weights are 0.13, 0.25, 0.13, 0.03, 0.43;
wherein the relative change times of the angle of the slope is y1(ii) a Then
Figure BDA0003371369840000197
Wherein i1Standard variation angle, 5 °; then
Figure BDA0003371369840000201
The fourth modification weight is 0.18, 0.24, 0.12, 0.02, 0.42;
wherein the lithology of the slope stratum does not change relatively;
setting T15 days, 5+5 > 5 exists, so the groundwater influence factor of the side slope should be converted into x1；
The fifth alteration weight is 0.17, 0.23, 0.11, 0.01, 0.07, 0.41;
the final weights are 0.17, 0.23, 0.11, 0.01, 0.07, 0.41;
and k is5＝v3＝0.4；
Then finally predict
P＝w1*k1+w2*k2+w3*k3+w4*k4+w5*k5+w6*k6
＝0.17*0.6+0.23*0.7+0.11*0.8+0.01*0.2+0.07*0.4+0.41*0.9
＝0.75
The final relative landslide prediction probability for that side slope a is 75%.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. 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.

Claims (10)

1. The utility model provides a side slope state monitoring system based on big data which characterized in that: the system comprises a slope information acquisition module, a greening distribution module, a weather module, a prediction module, a scanning module, a comparison module, a monitoring module and an early warning module;
the side slope information acquisition module is used for acquiring and recording basic information of a side slope to be monitored; the greening distribution module is used for designing a vegetation greening distribution scheme on the side slope; the weather module is used for acquiring weather forecast; the prediction module is used for predicting the survival state of vegetation under various weather conditions; the scanning module is used for scanning vegetation states on a slope; the comparison module is used for comparing the vegetation on the side slope with the vegetation growing normally and judging the survival state of the vegetation on the side slope; the monitoring module is used for monitoring the area where the vegetation or the slope soil and stone slide down; the early warning module is used for sending out early warning to remind workers to overhaul;
the output end of the side slope information acquisition module is connected with the input end of the greening distribution module; the output end of the greening distribution module is connected with the input end of the weather module; the output end of the weather module is connected with the input end of the prediction module; the output end of the prediction module is connected with the input end of the scanning module; the output end of the scanning module is connected with the input end of the comparison module; the output end of the comparison module is connected with the input end of the monitoring module; the output end of the monitoring module is connected with the input end of the early warning module.
2. The big data based slope state monitoring system according to claim 1, characterized in that: the side slope information acquisition module comprises an area acquisition unit, an age acquisition unit, a stratum lithology acquisition unit and an underground water condition acquisition unit;
the area acquisition unit is used for acquiring the area condition of the side slope; the age limit acquisition unit is used for acquiring the service life of the side slope; the stratum lithology acquisition unit is used for acquiring a rock stratum structure and stratum lithology of the side slope; the underground water condition acquisition unit is used for acquiring the influence degree of underground water on the side slope;
the output ends of the area acquisition unit, the age limit acquisition unit, the stratum lithology acquisition unit and the underground water condition acquisition unit are all connected to the input end of the greening distribution module;
the greening distribution module comprises a greening unit and a distribution unit;
the greening unit is used for selecting proper greening vegetation to plant according to the lithology of the stratum; the distribution unit is used for calculating the vegetation root gripping area and the vegetation greening coverage area to distribute the vegetation;
the output end of the greening unit is connected with the input end of the distribution unit; the output end of the distribution unit is connected with the input end of the weather module.
3. The big data based slope state monitoring system according to claim 1, characterized in that: the weather module comprises a weather receiving unit and an output unit;
the weather receiving unit is used for receiving and recording weather conditions; the output unit is used for outputting the weather condition to the prediction module according to the time period;
the output end of the weather receiving unit is connected with the input end of the output unit; the output end of the output unit is connected with the input end of the prediction module;
the prediction module comprises a historical database, a weight adjusting unit and a probability influence unit;
the historical database is used for collecting the historical data condition of the slope landslide; the weight adjusting unit is used for adjusting the weight of each influence factor in the slope landslide process; the probability influence unit is used for establishing initial influence probability of each influence factor according to historical data;
the output end of the historical database is connected with the input ends of the weight adjusting unit and the probability influence unit; the output ends of the weight adjusting unit and the probability influencing unit are connected with the input end of the scanning module.
4. The big data based slope state monitoring system according to claim 1, characterized in that: the scanning module comprises a control unit, a three-dimensional scanning unit and an analysis unit;
the control unit is used for controlling the opening and closing of the three-dimensional scanning unit according to the weather condition; the three-dimensional scanning unit is used for scanning the vegetation state in bad weather and generating a scanning image to the analysis unit; the analysis unit is used for analyzing the states of the leaves and the roots of the vegetation in the scanning map;
the output end of the control unit is connected with the input end of the three-dimensional scanning unit; the output end of the three-dimensional scanning unit is connected with the input end of the analysis unit; the output end of the analysis unit is connected with the input end of the comparison module;
the comparison module comprises a data unit and a comparison unit;
the data unit is used for acquiring growth data of normal vegetation; the comparison unit is used for comparing the states of the normal vegetation and the vegetation which has undergone poor weather and judging the survival condition;
the output end of the data unit is connected with the input end of the comparison unit; and the output end of the comparison unit is connected with the input end of the monitoring module.
5. The big data based slope state monitoring system according to claim 1, characterized in that: the monitoring module comprises a vegetation sliding region unit and a greening area monitoring unit;
the vegetation slip region unit is used for monitoring a vegetation slip region and whether the driving of road vehicles is influenced; the greening area monitoring unit is used for monitoring the change of the vegetation greening coverage area and the soil grabbing area of the vegetation roots and sending a message to the early warning module when the change is lower than a threshold value;
the output ends of the vegetation sliding region unit and the greening area monitoring unit are connected with the input end of the early warning module.
6. The big data based slope state monitoring system according to claim 1, characterized in that: the early warning module comprises an early warning unit and a management unit;
the early warning unit is used for sending out early warning information; the management unit is used for receiving the early warning information and informing a manager to perform management operation;
the output end of the early warning unit is connected with the input end of the management unit.
7. A slope state monitoring method based on big data is characterized in that: the method comprises the following steps:
s1, acquiring basic information of the side slope and establishing a vegetation greening distribution scheme;
s2, acquiring weather influence factors, predicting the risk of landslide of the side slope, and establishing a prediction model;
s3, obtaining a three-dimensional scanning image of vegetation greening on the slope, and establishing a similarity model to obtain the survival condition of vegetation;
s4, calculating a vegetation sliding region, reacquiring the soil-grabbing area of the vegetation root, correcting the prediction model, and establishing danger level early warning.
8. The slope state monitoring method based on big data according to claim 7, characterized in that: in step S1, the basic information of the slope includes slope area, age, stratigraphic lithology, and groundwater influence factor;
the vegetation greening distribution scheme comprises the following steps:
obtaining vegetation cover area, marked as S1；
Obtaining the area of the side slope, marked as S0；
Then the vegetation greening coverage rate Q exists1＝S1/S0；
Setting vegetation greening coverage rate threshold value to be Qmax；
In the vegetation greening distribution scheme, Q is present1≤Qmax；
Obtaining the soil-grabbing area of the vegetation root and recording as S2；
Then the vegetation soil-protecting coverage rate Q exists2＝S2/S0；
Setting the vegetation soil-protecting coverage rate threshold value to be Qmin；
In the vegetation greening distribution scheme, Q is present2≥Qmin。
9. The big-data-based slope state monitoring method according to claim 8, characterized in that: in steps S2-S3, the predicting and warning further includes:
the prediction process comprises the following steps:
s2-1, acquiring weather conditions in time T, wherein T is a positive integer and is a unit of day, and the weather conditions include good weather, severe weather and extreme weather;
s2-2, acquiring basic information of the side slope, including the angle of the side slope, and recording the angle as theta; the soil-grabbing area of the vegetation root on the side slope is marked as S2(ii) a The slope age limit is marked as N years; the lithology of the slope stratum; the stratum lithology comprises a soil slope and a rock slope; groundwater impact factor of the slope; the angle of the side slope is the included angle between the side slope and the horizontal plane;
the groundwater influence factor of the slope comprises x1、x2(ii) a Said x1The influence of underground water is large, and the side slope can be softened or corroded by rocks; said x2The influence of underground water is small, and the weight of the rock mass of the side slope can be increased;
the groundwater influencing power is related to weather; if there is not less than T in T time1In case of heavy or heavy weather, the groundwater influence factor will vary from x2Upgrade to x1(ii) a Wherein T is1Is a threshold number of days of influence;
s2-3, acquiring historical data of slope landslide and establishing a prediction model;
P＝w1*k1+w2*k2+w3*k3+w4*k4+w5*k5+w6*k6
wherein P is the relative landslide prediction probability of each side slope; w is a1、w2、w3、w4、w5、w6Respectively the angle theta of the side slope and the soil-holding area S of the vegetation root on the side slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence weight; and w1+w2+w3+w4+w5+w6＝1；k1、k2、k3、k4、k5、k6Respectively is the angle theta of the slope under the historical data and the soil-holding area S of the vegetation roots on the slope2The slope age N, the slope stratum lithology, the slope underground water influence factor and the weather influence probability;
be provided with standard side slope, include:
the gradient ratio is 1: 1; the gradient ratio is the ratio of the vertical height and the horizontal width of the slope; namely the angle theta of the side slope is 45 degrees; the soil grabbing area of the vegetation roots on the side slope is S; the slope age is 5 years;
then there is
Figure FDA0003371369830000051
Wherein, the delta theta is the number of the slopes with the angle exceeding 45 degrees of the slope where the landslide occurs in the historical data; the delta S is the number of the side slopes with the soil-holding area of the vegetation roots on the side slopes with landslides smaller than S in the historical data; the delta N is the number of the slopes of which the slope age exceeds N when landslide occurs in the historical data; m is the number of slopes with the ratio of non-good weather to good weather exceeding 1 in the time period T of the slopes with landslides in the historical data; the non-good weather comprises extreme weather and severe weather; u is the number of slopes with landslides in the historical data;
at the same time:
when the lithology of the side slope stratum is a soil side slope, k4＝v1(ii) a When it is a rocky slope, k4＝v2；
The influence factor of underground water on the side slope is x1When k is5＝v3(ii) a The groundwater influence factor of the side slope is x2When k is5＝v4；
Wherein v is1、v2、v3、v4The values are constant values, and the system performs self-setting according to historical rules;
s2-4, establishing a weight mechanism, comprising:
according to the initial k1、k2、k3、k4、k5、k6Size of (a) to w1、w2、w3、w4、w5、w6Change ordering of, initial k1、k2、k3、k4、k5、k6The larger the weight is, the more forward the weight is;
set the rank weight, denoted as a1Said grade beingThe weight is that each influence factor in the prediction model relatively changes once and the corresponding weight also changes once, and the influence weights of other influence factors are averagely reduced;
wherein the relative change times of the angle of the slope is y1(ii) a Then
Figure FDA0003371369830000061
Wherein i1Is a standard variation angle;
wherein the vegetation root on the side slope grabs the soil area S2Relative number of changes of y2(ii) a Then
Figure FDA0003371369830000062
Wherein i2Is a standard change area;
wherein the relative change number of the age N of the side slope is y3(ii) a Then
Figure FDA0003371369830000063
Wherein i3Standard change years;
wherein the lithology of the slope stratum does not change relatively;
wherein the groundwater influence factor of the slope is from x2To x1Is recorded as a relative change;
wherein the relative change number of weather is y4(ii) a Then
Figure FDA0003371369830000071
Wherein i4For standard change of weather days, M1Days of non-good weather; m2Good weather days;
acquiring a three-dimensional scanning image of vegetation greening on the side slope after the non-favorable weather is finished;
obtaining a scanning map of normal growth of vegetation;
setting the height of the vegetation from the ground, the size of the vegetation and the decay degree of the roots as similarity contrast factors;
establishing a similarity model, and measuring and calculating the survival probability of vegetation;
Figure FDA0003371369830000072
wherein d represents the similarity of vegetation which has undergone poor weather to normally produced vegetation;
Figure FDA0003371369830000073
a similarity contrast factor b representing vegetation that has experienced poor weather;
Figure FDA0003371369830000074
a similarity contrast factor b representing a normally produced vegetation; n represents the number of similarity contrast factors;
setting a similarity threshold as dmin(ii) a If d is less than dminThen the vegetation is judged to be non-viable.
10. The big data based slope state monitoring method according to claim 9, characterized in that: in step S4, the method further includes:
acquiring the height of the vegetation which cannot survive, and recording the height as h;
then there is j1H/sin θ; wherein j1The distance of vegetation on the side slope;
then there is
Figure FDA0003371369830000075
Wherein a isAcceleration 1Is the glide acceleration; the speed when the slope reaches the bottom of the slope can be obtained;
then there is
Figure FDA0003371369830000081
Wherein a isAcceleration 2Deceleration acceleration for road slip; the farthest distance j of vegetation sliding can be obtained2；
The above conditions are all established in ideal conditions, i.e. without friction and hindrance, and therefore in realistic conditionsCan be substituted by2As the maximum sliding area distance;
setting an area of influence threshold to j3(ii) a If j is present2Exceeds j3If yes, sending out early warning information;
obtaining the soil-grabbing area of the vegetation roots which cannot survive and recording as S4；
Obtaining the vegetation greening coverage area which can not survive and is marked as S5；
Recalculating vegetation coverage area, denoted as S1-S5；
Then the vegetation greening coverage rate Q exists1＝S1-S5/S0；
Obtaining the soil-grabbing area of the vegetation root and recording as S2-S4；
Then the vegetation soil-protecting coverage rate Q exists2＝S2-S4/S0；
If Q is present1、Q2And when the threshold value is not met, sending out early warning information.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114912181A (en) * 2022-05-21 2022-08-16 武汉泰佰腾建筑劳务有限公司 Road surface slope safety monitoring and analyzing system based on artificial intelligence
CN117392811A (en) * 2023-10-27 2024-01-12 浙江水文新技术开发经营有限公司 Hilly rainfall monitoring and early warning system

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114912181A (en) * 2022-05-21 2022-08-16 武汉泰佰腾建筑劳务有限公司 Road surface slope safety monitoring and analyzing system based on artificial intelligence
CN114912181B (en) * 2022-05-21 2023-10-13 武汉泰佰腾建筑劳务有限公司 Pavement slope safety monitoring analysis system based on artificial intelligence
CN117392811A (en) * 2023-10-27 2024-01-12 浙江水文新技术开发经营有限公司 Hilly rainfall monitoring and early warning system
CN117392811B (en) * 2023-10-27 2024-05-07 浙江水文新技术开发经营有限公司 Hilly rainfall monitoring and early warning system

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Application publication date: 20220311