CN117789064A - Highway side slope unmanned aerial vehicle patrols and examines early warning system based on multisource data - Google Patents
Highway side slope unmanned aerial vehicle patrols and examines early warning system based on multisource data Download PDFInfo
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Abstract
The invention relates to the technical field of highway side slope inspection, in particular to a highway side slope unmanned aerial vehicle inspection early warning system based on multi-source data.
Description
Technical Field
The invention relates to the technical field of highway side slope inspection, in particular to a highway side slope unmanned aerial vehicle inspection early warning system based on multi-source data.
Background
The highway side slope is an important component in highway engineering and bears the important tasks of supporting the stable running of the highway and guaranteeing the safety of the running of the vehicle, however, due to the influence of geological conditions, climate change, artificial activities and other factors, the highway side slope is easy to cause problems, such as side slope landslide, collapse, settlement and the like, and the safety of the running of the vehicle and pedestrians is threatened.
In order to discover and solve the side slope problems in time, regular inspection and monitoring work is needed, the traditional side slope inspection mainly depends on a manual mode, inspection is performed through personnel walking or driving vehicles, however, the mode is low in efficiency and long in time consumption, a certain personal safety risk exists, along with the continuous development of scientific technology, unmanned aerial vehicle technology is introduced into the road side slope inspection to become a new solution, the unmanned aerial vehicle has the characteristics of being rapid, flexible and efficient, capable of rapidly covering a large range of side slopes, performing functions of high-definition shooting, three-dimensional modeling and the like, a large amount of side slope data can be rapidly obtained through inspection of the unmanned aerial vehicle, and comprehensive monitoring and evaluation of the side slope are realized.
According to the technical scheme disclosed by China patent publication (patent number 202210018046.0), the disease type and the distribution characteristics of a highway slope are primarily identified through multi-period image data obtained through unmanned aerial vehicle aerial survey, meanwhile, a slope live-action three-dimensional model and a three-dimensional point cloud model are generated, the slope disease is identified through a comparison point cloud model, then the stability coefficient of the slope under different working conditions is calculated based on the live-action three-dimensional model, further, the potential important monitoring slope object along the highway is determined, the multi-dimensional omnibearing collaborative monitoring of dangerous slopes is achieved, and the defects still exist, namely the following aspects are realized: 1. according to the scheme, the stability coefficient of the side slope is calculated mainly based on the live-action three-dimensional model, the influence of other influencing factors on the stability of the side slope of the highway is not considered, such as the soil compactness of the side slope, the soil water content, weather factors and the like, the early warning of the potentially dangerous side slope is possibly caused to be untimely or inaccurate, the effect of an early warning system is reduced, and the time for taking effective measures is delayed.
2. According to the scheme, the stability degree of the side slope of the highway is analyzed through the multi-period image data, but prediction of the risk degree of the side slope is not mentioned, the stability of the side slope may change along with time, the risk prediction is lacking, the increment of the risk degree of the side slope per unit time cannot be evaluated, the judgment of the potential risk may be caused to deviate, and therefore necessary measures cannot be taken in advance to cope with possible unstable events.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a highway side slope unmanned aerial vehicle inspection early warning system based on multi-source data, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme: the invention provides a highway side slope unmanned aerial vehicle inspection early warning system based on multi-source data, which comprises: and the unmanned aerial vehicle inspection route planning module is used for planning an unmanned aerial vehicle inspection route, detecting the vertical height of the road side slope at each measuring point and setting the flight height for the unmanned aerial vehicle.
And the structural appearance parameter acquisition module is used for detecting the cracking degree, the deformation degree and the displacement of the road side slope of each monitoring period.
The side slope risk analysis module is used for analyzing the cracking degree, the deformation degree and the displacement of the side slope of each monitoring period to obtain a risk degree coefficient χ of the side slope of the road, and comparing the risk degree coefficient χ with a preset risk degree coefficient threshold value to obtain the risk condition of the side slope of the road.
And the risk change trend analysis module is used for analyzing and obtaining the risk prediction duration of the highway side slope according to the risk degree coefficient of the highway side slope.
And the soil parameter acquisition module is used for acquiring the soil compactness and the soil water content of the highway side slope.
The risk correction module is used for obtaining rainfall and wind power levels in each period in the highway side slope risk prediction duration, analyzing and obtaining highway side slope risk degree correction coefficients by combining the soil compactness and the soil water content of the highway side slope, comparing the highway side slope risk degree correction coefficients with a preset risk degree correction coefficient threshold value, correcting the highway side slope risk prediction duration and feeding back to the system.
And the management database is used for storing the original model of the road side slope.
Preferably, the specific analysis method of the unmanned aerial vehicle routing module comprises the following steps: extracting a highway slope original model from a management database, obtaining a highway slope outline shape according to the highway slope original model, simultaneously respectively selecting a plurality of measuring points on the slope surface of the highway slope according to a preset distance, detecting the vertical height of the highway slope at each measuring point, marking the vertical height as each measuring point of the highway slope, extracting a recommended inspection route set of each highway slope outline shape from the management database, marking the corresponding inspection route as an unmanned plane inspection route according to the matching of the highway slope outline shape, simultaneously screening out the maximum value from the vertical heights of each measuring point of the highway slope, marking the maximum vertical height as the highway slope, and setting the flight height as the unmanned plane according to the maximum vertical height of the highway slope.
Preferably, the specific analysis process of the cracking degree of each monitoring period highway side slope is as follows: dividing monitoring periods according to preset time length, marking the monitoring periods as each monitoring period, acquiring surface images of the highway side slope at the starting time point of each monitoring period through the unmanned aerial vehicle, and marking the surface images as highway side slope images of each monitoring period.
And secondly, reading each monitoring period highway side slope image, converting the monitoring period highway side slope image into a gray level image, recording the gray level image as each monitoring period highway side slope gray level image, detecting the gray level value of each pixel point of each monitoring period highway side slope gray level image, setting a high threshold value and a low threshold value according to the preset gray level value, comparing the gray level value of each pixel point of each monitoring period highway side slope gray level image with the preset high threshold value and the preset low threshold value, marking the pixel point with the gray level value larger than or equal to the high threshold value as a strong edge pixel point, marking the pixel point with the gray level value between the low threshold value and the high threshold value as a weak edge pixel point, discarding the rest pixel points, counting all the strong edge pixel points and the weak edge pixel points in each monitoring period highway side slope gray level image, forming a complete crack edge by connecting the strong edge pixel points, marking the weak edge pixel points connected with the strong edge pixel points as crack edges, discarding the rest weak edge pixel points, and recording the obtained crack edges as the edge contours of each crack edge of each monitoring period highway side slope image.
Thirdly, counting the number of pixel points in the edge outline of each crack of the highway side slope gray level image of each monitoring period, and marking as rho mi Where m denotes the number of the mth monitoring period, m=1, 2,.. i=1, 2..q, simultaneously extracting the total number of pixels of the road side slope gray level image of each monitoring period, and recording as ρ m total By the formulaObtaining the cracking degree alpha of the road side slope of each monitoring period m 。
Preferably, the specific analysis process of the deformation degree of each monitoring period highway side slope is as follows: the method comprises the steps of firstly, reading each monitoring period highway side slope image, carrying out edge contour detection on each monitoring period highway side slope image, extracting contour lines of each monitoring period highway side slope, and respectively carrying out vertical height from top to bottom on the contour lines of each monitoring period highway side slopeThe degree and the slope length are measured and recorded as h' m 、l m Substituting it into formulaObtaining the gradient theta of the road side slope of each monitoring period m 。
Step two, reading the original model of the highway slope, obtaining the original slope of the highway slope from the original model, and recording the original slope as theta 0 Substituting it into formulaObtaining the deformation degree beta of the road side slope of each monitoring period m Wherein h and l respectively represent the original vertical height, slope length and phi of the original model of the highway slope 1 、φ 2 、φ 3 The weight factors respectively represent the vertical height of the road side slope, the length of the slope surface and the gradient.
Preferably, the specific analysis method for the displacement of the road side slope of each monitoring period comprises the following steps: the contour line of each monitoring period highway side slope is read, a plurality of measurement characteristic points are arbitrarily taken on the contour line of each monitoring period highway side slope according to a set interval and marked as each characteristic point, the contour line of each monitoring period highway side slope is respectively overlapped with the contour line of the monitoring period highway side slope on the contour line, each characteristic point corresponds to each other one by one, the displacement of each characteristic point is measured, and the displacement d of each characteristic point of each monitoring period highway side slope is marked as each characteristic point displacement d ma Where a denotes the number of the a-th feature point, a=1, 2,..and b, which is substituted into the formula to obtainDisplacement gamma of road side slope in each monitoring period m B represents the number of feature points.
Preferably, the analysis process of the slope risk analysis module is as follows: the first step, the cracking degree alpha of the road side slope of each monitoring period is respectively read m Degree of deformation beta m The displacement gammam is substituted into the formulaObtaining a risk degree coefficient χ of the road side slope, wherein α 0 、β 0 、γ 0 Respectively represents the set cracking degree, deformation degree and displacement reference value, w 1 、w 2 、w 3 Weight factors respectively representing the set cracking degree, deformation degree and displacement amount, and w 1 +w 2 +w 3 =1, n represents the number of monitoring cycles.
And secondly, comparing the risk degree coefficient of the highway side slope with a preset risk degree coefficient threshold value, if the risk degree coefficient of the highway side slope is larger than or equal to the preset risk degree coefficient threshold value, indicating that the risk degree coefficient of the highway side slope is unqualified, pre-warning the system, and if the risk degree coefficient of the highway side slope is smaller than the preset risk degree coefficient threshold value, analyzing the risk prediction duration of the highway side slope.
Preferably, the specific analysis method for analyzing the predicted highway side slope risk duration comprises the following steps: reading a risk degree coefficient χ of a highway side slope, extracting the duration t' of a monitoring period, and obtaining a formulaObtaining the unit time increment delta 'χ of the highway side slope risk degree coefficient, wherein n is the number of monitoring periods, and substituting the unit time increment delta' χ into a formula +.>Obtaining the time t required for the risk degree coefficient of the highway side slope to reach a preset risk degree coefficient threshold value, and recording the time t as the predicted time of the highway side slope risk, wherein χ is 0 Representing a preset risk level coefficient threshold.
Preferably, the specific analysis process of the soil parameter obtaining module is as follows: firstly, drilling a plurality of holes with equal volume from the earth surface to the underground on a highway slope according to a set distance, scanning the inside of the holes by using a scanning instrument to obtain three-dimensional data of holes of all holes, extracting the number of holes of all holes, and obtaining the volume of the holes of all holes by volume calculationDenoted as v jk Where j represents the number of the j-th borehole, j=1, 2, g, k represents the number of the k-th hole wall gap, k=1, 2, f, which is substituted into the formulaObtaining the soil compactness W of the road side slope, wherein v 0 And (5) representing a preset hole wall gap reference volume, wherein g is the number of drilled holes.
Taking a set amount of soil in each drilling hole as a sample to be measured, marking the sample as each soil sample, weighing each soil sample, and marking the weight as the initial wet weight M of each soil sample j wet Drying each soil sample to constant weight at a certain temperature, weighing the dried soil sample, and recording as dry weight M of each soil sample j trunk By the formulaAnd obtaining the soil water content H of the road side slope, wherein g represents the number of drilling holes.
Preferably, the specific analysis process of the risk correction module is as follows: the first step, networking with a local weather monitoring system, and reading rainfall and wind power level in each period of the road side slope risk prediction duration from the network according to the road side slope risk prediction duration, wherein the rainfall and the wind power level are respectively recorded as R x 、F x Where x represents the number of the x-th period, x=1, 2,..and y, and simultaneously reading the soil compactness W of the road side slope and the soil water content H of the road side slope, substituting them into the formulaObtaining the correction coefficient of the road side slope danger degree>Wherein H is 0 、R 0 、F 0 Respectively represents the standard soil water content, rainfall, wind power grade, eta of the highway side slope under the safety condition 1 、η 2 、η 3 、η 4 Respectively represent the set soil compactionThe weight factors of the degree, the soil water content, the rainfall and the wind power level are calculated, and y is the number of time periods.
Step two, reading the road side slope dangerous degree correction coefficientComparing the road side slope risk degree correction coefficient with a preset risk degree correction coefficient threshold value, and substituting the road side slope risk degree correction coefficient into the formula +_, if the road side slope risk degree correction coefficient is greater than or equal to the preset risk degree correction coefficient threshold value>Obtaining the corrected highway slope risk prediction time period t 'mu' 1 And e is a natural constant and is fed back to the system.
Compared with the prior art, the invention has the following beneficial effects: 1. according to the invention, the height of the highway side slope is detected, and the inspection route and the flying height are planned for the unmanned aerial vehicle, so that the flying accident risk caused by the change of the terrain is avoided, the flying safety is improved, and the high-efficiency and accurate inspection of the highway side slope is realized.
2. According to the method, the risk degree coefficient of the highway side slope is obtained through analysis according to the cracking degree, the deformation degree and the displacement of the highway side slope in each monitoring period, and is compared with the preset risk degree coefficient threshold value, so that the risk condition of the highway side slope is obtained, the risk of the side slope is facilitated to be treated in time, the safety and the reliability of the highway are improved, the possibility of accidents is reduced, and the smoothness of traffic and transportation is ensured.
3. According to the method, the risk prediction duration of the highway side slope is obtained according to the risk degree coefficient analysis of the highway side slope, potential side slope problems can be early warned in advance, corresponding measures can be conveniently taken, and the safety and reliability of the highway side slope are further improved.
4. According to the method, the rainfall and wind power levels in each period of the highway side slope risk prediction duration are obtained, the highway side slope risk degree correction coefficient is obtained by combining the soil compactness and the soil water content analysis of the highway side slope, the highway side slope risk prediction duration is corrected, the highway side slope risk condition can be more accurately pre-warned, the possibly existing problem side slope can be found in advance, and corresponding preventive and protective measures are adopted, so that the probability of accident occurrence is reduced.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram illustrating a system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a highway side slope unmanned aerial vehicle inspection early warning system based on multi-source data includes an unmanned aerial vehicle inspection route planning module, a structural appearance parameter acquisition module, a side slope risk analysis module, a risk change trend analysis module, a soil parameter acquisition module, a risk correction module and a management database.
The management database is connected with the risk correction module, the unmanned aerial vehicle inspection route planning module, the risk change trend analysis module and the side slope risk analysis module, the risk correction module is connected with the soil parameter acquisition module and the side slope risk analysis module, the side slope risk analysis module is connected with the risk change trend analysis module and the structural appearance parameter acquisition module, and the unmanned aerial vehicle inspection route planning module is connected with the soil parameter acquisition module and the structural appearance parameter acquisition module.
And the unmanned aerial vehicle inspection route planning module is used for planning an unmanned aerial vehicle inspection route, detecting the vertical height of the road side slope at each measuring point and setting the flight height for the unmanned aerial vehicle.
The specific analysis method of the unmanned aerial vehicle routing planning module comprises the following steps: extracting a highway slope original model from a management database, obtaining a highway slope contour shape according to the highway slope original model, simultaneously respectively selecting a plurality of measuring points on the slope surface of the highway slope according to a preset distance, detecting the vertical height of the highway slope at each measuring point, marking the vertical height as the vertical height of each measuring point of the highway slope, extracting a recommended inspection route set of each highway slope contour shape from the management database, matching a corresponding inspection route according to the highway slope contour shape, marking the inspection route as an unmanned plane inspection route, simultaneously screening out the maximum value from the vertical heights of each measuring point of the highway slope, marking the maximum vertical height as the highway slope, and setting the flight height as an unmanned plane according to the maximum vertical height of the highway slope; by extracting the original model of the highway side slope and the vertical height of each measuring point from the management database, accurate measurement and data acquisition are carried out on the highway side slope before inspection, so that the accuracy of data is ensured, meanwhile, the vertical height of the highway side slope at each measuring point can be used as a reference for setting the flight height of an unmanned plane, the comprehensive inspection of the highway side slope is realized, and the subjectivity and initiative deficiency of manual inspection are eliminated.
And the structural appearance parameter acquisition module is used for detecting the cracking degree, the deformation degree and the displacement of the road side slope of each monitoring period.
The specific analysis process of the cracking degree of the road side slope of each monitoring period is as follows: dividing monitoring periods according to preset time length, marking the monitoring periods as each monitoring period, acquiring surface images of a highway slope at the starting time point of each monitoring period through an unmanned aerial vehicle, and marking the surface images as highway slope images of each monitoring period; the monitoring period is divided by setting the preset time length, the road side slope can be monitored regularly, the unmanned aerial vehicle is utilized to obtain the surface image of the road side slope of each monitoring period, the change condition of the road side slope can be known in real time, and problems and hidden dangers can be found out in time.
Reading each monitoring period highway side slope image, converting the monitoring period highway side slope image into a gray level image, recording the gray level image as each monitoring period highway side slope gray level image, detecting the gray level value of each pixel point of each monitoring period highway side slope gray level image, setting a high threshold value and a low threshold value according to the preset gray level value, comparing the gray level value of each pixel point of each monitoring period highway side slope gray level image with the preset high threshold value and the preset low threshold value, marking the pixel point with the gray level value larger than or equal to the high threshold value as a strong edge pixel point, marking the pixel point with the gray level value between the low threshold value and the high threshold value as a weak edge pixel point, discarding the rest pixel points, counting all the strong edge pixel points and the weak edge pixel points in each monitoring period highway side slope gray level image, forming a complete crack edge by connecting the strong edge pixel points, marking the weak edge pixel points connected with the strong edge pixel points as crack edges, discarding the rest of the weak edge pixel points, and recording the obtained crack edges as the edge contours of each crack edge of each monitoring period highway side slope image; by detecting the crack edge contour in the side slope gray level image, the crack problem of the road side slope can be found early, the crack is an important index of the side slope stability, and the further expansion of the crack and the aggravation of the instability of the side slope can be avoided by timely finding and corresponding maintenance and treatment.
Thirdly, counting the number of pixel points in the edge outline of each crack of the highway side slope gray level image of each monitoring period, and marking as rho mi Where m denotes the number of the mth monitoring period, m=1, 2,.. i=1, 2..q, simultaneously extracting the total number of pixels of the road side slope gray level image of each monitoring period, and recording as ρ m total By the formulaObtaining the cracking degree alpha of the road side slope of each monitoring period m The method comprises the steps of carrying out a first treatment on the surface of the The cracking degree is one of important indexes for evaluating the stability of the side slope, and the stable state of the side slope can be mastered in time through long-term monitoring and analysis of the cracking degree, so that safety accidents and crossing caused by instability of the side slope are avoidedAnd meanwhile, reasonable maintenance and management plans can be formulated according to the change condition of the cracking degree, so that the safety and reliability of the road side slope are ensured.
The concrete analysis process of the deformation degree of each monitoring period highway side slope is as follows: the first step, reading the side slope image of each monitoring period road, carrying out edge contour detection on the side slope image of each monitoring period road, extracting the contour line of each monitoring period road side slope, respectively measuring the vertical height from the top to the bottom of the contour line of each monitoring period road side slope and the slope length, and recording as h '' m 、l m Substituting it into formulaObtaining the gradient theta of the road side slope of each monitoring period m The method comprises the steps of carrying out a first treatment on the surface of the According to the slope value of each monitoring period highway slope, whether the slope exceeds the condition of setting limit can be judged, if the slope value is bigger or abnormal, the slope is indicated to have potential safety hazards such as landslide and collapse, and according to the slope value, reasonable maintenance strategies and safety measures can be formulated, including targeted reinforcement, monitoring or traffic limiting and other preventive measures, so that the safety and smoothness of highway traffic are ensured.
Step two, reading the original model of the highway slope, obtaining the original slope of the highway slope from the original model, and recording the original slope as theta 0 Substituting it into formulaObtaining the deformation degree beta of the road side slope of each monitoring period m Wherein h and l respectively represent the original vertical height, slope length and phi of the original model of the highway slope 1 、φ 2 、φ 3 Weight factors respectively representing the vertical height, the slope length and the slope of the highway slope; the deformation degree is an index for measuring the deformation and the change degree of the side slope, and through evaluation of the deformation degree, the change condition of the side slope can be known, potential deformation risks can be identified, and references are provided for maintenance and management of the side slope.
Specific analysis of displacement of road side slope of each monitoring periodThe method comprises the following steps: the contour line of each monitoring period highway side slope is read, a plurality of measurement characteristic points are arbitrarily taken on the contour line of each monitoring period highway side slope according to a set interval and marked as each characteristic point, the contour line of each monitoring period highway side slope is respectively overlapped with the contour line of the monitoring period highway side slope on the contour line, each characteristic point corresponds to each other one by one, the displacement of each characteristic point is measured, and the displacement d of each characteristic point of each monitoring period highway side slope is marked as each characteristic point displacement d ma Where a denotes the number of the a-th feature point, a=1, 2,..and b, which is substituted into the formula to obtainDisplacement gamma of road side slope in each monitoring period m B represents the number of feature points; the displacement of each monitoring period highway side slope at different positions can be known quantitatively by corresponding each characteristic point one by one and measuring the displacement, and the displacement data can provide visual information about the side slope deformation, thereby being beneficial to identifying the key area and degree of the side slope deformation.
The side slope risk analysis module is used for analyzing the cracking degree, the deformation degree and the displacement of the side slope of each monitoring period to obtain a risk degree coefficient χ of the side slope of the road, and comparing the risk degree coefficient χ with a preset risk degree coefficient threshold value to obtain the risk condition of the side slope of the road.
The analysis process of the slope risk analysis module is as follows: the first step, the cracking degree alpha of the road side slope of each monitoring period is respectively read m Degree of deformation beta m The displacement gammam is substituted into the formulaObtaining a risk degree coefficient χ of the road side slope, wherein α 0 、β 0 、γ 0 Respectively represents the set cracking degree, deformation degree and displacement reference value, w 1 、w 2 、w 3 Weight factors respectively representing the set cracking degree, deformation degree and displacement amount, and w 1 +w 2 +w 3 =1, n represents the number of monitoring cycles; by calculating the risk degree coefficientThe method and the system realize early warning and monitoring of the side slope risk, continuously monitor and evaluate the risk degree, can dynamically grasp the change condition of the side slope, and provide real-time data support for maintenance and management.
Secondly, comparing the risk degree coefficient of the highway side slope with a preset risk degree coefficient threshold value, if the risk degree coefficient of the highway side slope is larger than or equal to the preset risk degree coefficient threshold value, indicating that the risk degree coefficient of the highway side slope is unqualified, pre-warning the system, and if the risk degree coefficient of the highway side slope is smaller than the preset risk degree coefficient threshold value, analyzing the risk prediction duration of the highway side slope; corresponding emergency and maintenance measures are convenient to take in time, so that risks are reduced, and side slope accidents are prevented.
And the risk change trend analysis module is used for analyzing and obtaining the risk prediction duration of the highway side slope according to the risk degree coefficient of the highway side slope.
The specific analysis method for analyzing the predicted highway side slope risk duration comprises the following steps: reading a risk degree coefficient χ of a highway side slope, extracting the duration t' of a monitoring period, and obtaining a formulaObtaining the unit time increment delta 'χ of the highway side slope risk degree coefficient, wherein n is the number of monitoring periods, and substituting the unit time increment delta' χ into a formula +.>Obtaining the time t required for the risk degree coefficient of the highway side slope to reach a preset risk degree coefficient threshold value, and recording the time t as the predicted time of the highway side slope risk, wherein χ is 0 Representing a preset risk degree coefficient threshold value; by calculating the increment of the highway side slope risk degree coefficient, the risk degree can be predicted, potential safety hazards can be found early, corresponding measures can be taken for risk management, and accidents can be effectively avoided or reduced.
And the soil parameter acquisition module is used for acquiring the soil compactness and the soil water content of the highway side slope.
The concrete analysis process of the soil parameter acquisition module is as follows: firstly, drilling a plurality of holes with equal volume from the earth surface to the underground on a highway slope according to a set distance, scanning the inside of the holes by using a scanning instrument to obtain three-dimensional data of holes wall gaps of each hole, extracting the number of the holes wall gaps of each hole, calculating the volume of the holes wall gaps of each hole to obtain the volume of each hole wall gap of each hole, and marking as v jk Where j represents the number of the j-th borehole, j=1, 2, g, k represents the number of the k-th hole wall gap, k=1, 2, f, which is substituted into the formulaObtaining the soil compactness W of the road side slope, wherein v 0 Representing a preset hole wall gap reference volume, wherein g is the number of drilling holes; the stability and the risk degree of the side slope can be predicted by calculating the soil compactness of the side slope of the highway, if the soil compactness exceeds the preset hole wall gap reference volume, the soil is loose and has larger risk, otherwise, if the soil compactness is in a safety range, the stability of the side slope is relatively good, the risk condition of the side slope can be early warned in advance by accurately evaluating the soil compactness, and emergency response can be timely carried out to prevent disaster accidents such as landslide and collapse of the side slope.
Taking a set amount of soil in each drilling hole as a sample to be measured, marking the sample as each soil sample, weighing each soil sample, and marking the weight as the initial wet weight M of each soil sample j wet Drying each soil sample to constant weight at a certain temperature, weighing the dried soil sample, and recording as dry weight M of each soil sample j trunk By the formulaObtaining the soil water content H of the road side slope, wherein g represents the number of drilling holes; soil moisture content is an important factor for evaluating stability of a highway slope, and soil loosening or shrinkage can be caused by over wetting or over drying of the slope soil, so that stability of the slope is affected, and wet weight and dry weight of soil samples in holes of the slope are measuredThe soil moisture content can be calculated, so that the moisture state and the stability degree of the slope soil can be judged, if the soil moisture content is too high or too low, the condition that the slope has higher dangerous degree can be indicated, and corresponding reinforcement measures are needed.
The risk correction module is used for obtaining rainfall and wind power levels in each period in the highway side slope risk prediction duration, analyzing and obtaining highway side slope risk degree correction coefficients by combining the soil compactness and the soil water content of the highway side slope, comparing the highway side slope risk degree correction coefficients with a preset risk degree correction coefficient threshold value, correcting the highway side slope risk prediction duration and feeding back to the system.
The specific analysis process of the risk correction module is as follows: the first step, networking with a local weather monitoring system, and reading rainfall and wind power level in each period of the road side slope risk prediction duration from the network according to the road side slope risk prediction duration, wherein the rainfall and the wind power level are respectively recorded as R x 、F x Where x represents the number of the x-th period, x=1, 2,..and y, and simultaneously reading the soil compactness W of the road side slope and the soil water content H of the road side slope, substituting them into the formulaObtaining the correction coefficient of the road side slope danger degree>Wherein H is 0 、R 0 、F 0 Respectively represents the standard soil water content, rainfall, wind power grade, eta of the highway side slope under the safety condition 1 、η 2 、η 3 、η 4 Respectively representing the set weight factors of soil compactness, soil water content, rainfall and wind power level, wherein y is the number of time periods; the numerical value of the correction coefficient can reflect the stability and the risk degree of the slope under different meteorological conditions, the risks of different grades can be estimated based on the change of the correction coefficient, meanwhile, the soil compactness and the soil water content have important influence on the stability of the slope, the dangerous degree of the slope can be estimated more accurately by comprehensively considering the parameters, and the early warning and management accuracy is improvedAnd (5) certainty.
Step two, reading the road side slope dangerous degree correction coefficientComparing the road side slope risk degree correction coefficient with a preset risk degree correction coefficient threshold value, and substituting the road side slope risk degree correction coefficient into the formula +_, if the road side slope risk degree correction coefficient is greater than or equal to the preset risk degree correction coefficient threshold value>Obtaining the corrected highway slope risk prediction time period t 'mu' 1 The set reference correction prediction time length is represented, e is a natural constant, and the natural constant is fed back to the system; by reading and comparing the road slope risk degree correction coefficient, the system can timely find the road slope with high risk, so that corresponding preventive and management measures are timely taken, the possibility of accident occurrence is reduced, meanwhile, the corrected road slope risk prediction duration can more objectively reflect the actual risk degree of the current slope, and the establishment of a systematic road risk management framework is facilitated, so that more reasonable and effective management measures can be formulated, and the comprehensive management and control capability of road safety is improved.
And the management database is used for storing the original model of the road side slope.
According to the system, on one hand, the cracking degree, the deformation degree and the displacement of the highway side slope are regularly inspected through planning of the unmanned aerial vehicle inspection route and the flying height, the risk degree coefficient of the highway side slope is obtained through analysis, the highway side slope risk prediction duration is obtained through further analysis, potential side slope problems are warned in advance, corresponding measures are convenient to take, the safety and the reliability of the highway side slope are further improved, on the other hand, the rainfall and the wind power level in each period in the highway side slope risk prediction duration are obtained through obtaining, the soil compactness and the soil water content of the highway side slope are combined, the highway side slope risk degree correction coefficient is obtained through analysis, the highway side slope risk prediction duration is corrected, the safety and the reliability of the highway side slope are improved, the disaster loss is reduced, scientific basis and decision support are provided, the operation and maintenance efficiency is improved, the cost is reduced, and the system has important significance for the development of highway traffic management and society.
While embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention, which is also intended to be covered by the present invention.
Claims (9)
1. The highway side slope unmanned aerial vehicle inspection early warning system based on the multi-source data is characterized by comprising the following modules:
the unmanned aerial vehicle inspection route planning module is used for planning an unmanned aerial vehicle inspection route, detecting the vertical height of the road side slope at each measuring point and setting the flight height for the unmanned aerial vehicle;
the structural appearance parameter acquisition module is used for detecting the cracking degree, the deformation degree and the displacement of the road side slope of each monitoring period;
the side slope risk analysis module is used for analyzing the cracking degree, the deformation degree and the displacement of the side slope of the highway according to each monitoring period to obtain a risk degree coefficient χ of the side slope of the highway, and comparing the risk degree coefficient χ with a preset risk degree coefficient threshold value to obtain the risk condition of the side slope of the highway;
the risk change trend analysis module is used for analyzing and obtaining the risk prediction duration of the highway slope according to the risk degree coefficient of the highway slope;
the soil parameter acquisition module is used for acquiring the soil compactness of the road side slope and the soil water content;
the risk correction module is used for acquiring rainfall and wind power levels in each period in the highway side slope risk prediction duration, analyzing and obtaining highway side slope risk degree correction coefficients by combining the soil compactness and the soil water content of the highway side slope, comparing the highway side slope risk degree correction coefficients with a preset risk degree correction coefficient threshold value, correcting the highway side slope risk prediction duration and feeding back to the system;
and the management database is used for storing the original model of the road side slope.
2. The highway side slope unmanned aerial vehicle inspection early warning system based on the multi-source data according to claim 1, wherein the specific analysis method of the unmanned aerial vehicle inspection route planning module is as follows:
extracting a highway slope original model from a management database, obtaining a highway slope outline shape according to the highway slope original model, simultaneously respectively selecting a plurality of measuring points on the slope surface of the highway slope according to a preset distance, detecting the vertical height of the highway slope at each measuring point, marking the vertical height as each measuring point of the highway slope, extracting a recommended inspection route set of each highway slope outline shape from the management database, marking the corresponding inspection route as an unmanned plane inspection route according to the matching of the highway slope outline shape, simultaneously screening out the maximum value from the vertical heights of each measuring point of the highway slope, marking the maximum vertical height as the highway slope, and setting the flight height as the unmanned plane according to the maximum vertical height of the highway slope.
3. The highway side slope unmanned aerial vehicle inspection early warning system based on the multi-source data according to claim 1, wherein the specific analysis process of the cracking degree of each monitoring period highway side slope is as follows:
dividing monitoring periods according to preset time length, marking the monitoring periods as each monitoring period, acquiring surface images of a highway slope at the starting time point of each monitoring period through an unmanned aerial vehicle, and marking the surface images as highway slope images of each monitoring period;
reading each monitoring period highway side slope image, converting the monitoring period highway side slope image into a gray level image, recording the gray level image as each monitoring period highway side slope gray level image, detecting the gray level value of each pixel point of each monitoring period highway side slope gray level image, setting a high threshold value and a low threshold value according to the preset gray level value, comparing the gray level value of each pixel point of each monitoring period highway side slope gray level image with the preset high threshold value and the preset low threshold value, marking the pixel point with the gray level value larger than or equal to the high threshold value as a strong edge pixel point, marking the pixel point with the gray level value between the low threshold value and the high threshold value as a weak edge pixel point, discarding the rest pixel points, counting all the strong edge pixel points and the weak edge pixel points in each monitoring period highway side slope gray level image, forming a complete crack edge by connecting the strong edge pixel points, marking the weak edge pixel points connected with the strong edge pixel points as crack edges, discarding the rest of the weak edge pixel points, and recording the obtained crack edges as the edge contours of each crack edge of each monitoring period highway side slope image;
thirdly, counting the number of pixel points in the edge outline of each crack of the highway side slope gray level image of each monitoring period, and marking as rho mi Where m denotes the number of the mth monitoring period, m=1, 2,.. i=1, 2..q, simultaneously extracting the total number of pixels of the road side slope gray level image of each monitoring period, and recording as ρ m total By the formulaObtaining the cracking degree alpha of the road side slope of each monitoring period m 。
4. The inspection early warning system of the highway side slope unmanned aerial vehicle based on the multi-source data according to claim 3, wherein the specific analysis process of the deformation degree of each monitoring period highway side slope is as follows:
the first step, reading the side slope image of each monitoring period road, carrying out edge contour detection on the side slope image of each monitoring period road, extracting the contour line of each monitoring period road side slope, respectively measuring the vertical height from the top to the bottom of the contour line of each monitoring period road side slope and the slope length, and recording as h '' m 、l m Substituting it into formulaObtaining the gradient theta of the road side slope of each monitoring period m ；
Step two, reading the original model of the highway slope, and obtaining the highway slope from the original modelOriginal grade, noted as θ 0 Substituting it into formulaObtaining the deformation degree beta of the road side slope of each monitoring period m Wherein h and l respectively represent the original vertical height, slope length and phi of the original model of the highway slope 1 、φ 2 、φ 3 The weight factors respectively represent the vertical height of the road side slope, the length of the slope surface and the gradient.
5. The highway side slope unmanned aerial vehicle inspection early warning system based on the multi-source data according to claim 4, wherein the specific analysis method of the displacement of each monitoring period highway side slope is as follows:
the contour line of each monitoring period highway side slope is read, a plurality of measurement characteristic points are arbitrarily taken on the contour line of each monitoring period highway side slope according to a set interval and marked as each characteristic point, the contour line of each monitoring period highway side slope is respectively overlapped with the contour line of the monitoring period highway side slope on the contour line, each characteristic point corresponds to each other one by one, the displacement of each characteristic point is measured, and the displacement d of each characteristic point of each monitoring period highway side slope is marked as each characteristic point displacement d ma Where a denotes the number of the a-th feature point, a=1, 2,..and b, which is substituted into the formula to obtainDisplacement gamma of road side slope in each monitoring period m B represents the number of feature points.
6. The multi-source data-based highway slope unmanned aerial vehicle inspection early warning system according to claim 5, wherein the analysis process of the slope risk analysis module is as follows:
the first step, the cracking degree alpha of the road side slope of each monitoring period is respectively read m Degree of deformation beta m Displacement amount gamma m Substituting it into formulaObtaining a risk degree coefficient χ of the road side slope, wherein α 0 、β 0 、γ 0 Respectively represents the set cracking degree, deformation degree and displacement reference value, w 1 、w 2 、w 3 Weight factors respectively representing the set cracking degree, deformation degree and displacement amount, and w 1 +w 2 +w 3 =1, n represents the number of monitoring cycles;
and secondly, comparing the risk degree coefficient of the highway side slope with a preset risk degree coefficient threshold value, if the risk degree coefficient of the highway side slope is larger than or equal to the preset risk degree coefficient threshold value, indicating that the risk degree coefficient of the highway side slope is unqualified, pre-warning the system, and if the risk degree coefficient of the highway side slope is smaller than the preset risk degree coefficient threshold value, analyzing the risk prediction duration of the highway side slope.
7. The multi-source data-based highway side slope unmanned aerial vehicle inspection early warning system according to claim 6, wherein the specific analysis method for analyzing the highway side slope risk prediction duration is as follows:
reading a risk degree coefficient χ of a highway side slope, extracting the duration t' of a monitoring period, and obtaining a formulaObtaining the unit time increment delta 'χ of the highway side slope risk degree coefficient, wherein n is the number of monitoring periods, and substituting the unit time increment delta' χ into a formulaObtaining the time t required for the risk degree coefficient of the highway side slope to reach a preset risk degree coefficient threshold value, and recording the time t as the predicted time of the highway side slope risk, wherein χ is 0 Representing a preset risk level coefficient threshold.
8. The highway side slope unmanned aerial vehicle inspection early warning system based on the multi-source data according to claim 1, wherein the specific analysis process of the soil parameter acquisition module is as follows:
firstly, drilling a plurality of holes with equal volume from the earth surface to the underground on a highway slope according to a set distance, scanning the inside of the holes by using a scanning instrument to obtain three-dimensional data of holes wall gaps of each hole, extracting the number of the holes wall gaps of each hole, calculating the volume of the holes wall gaps of each hole to obtain the volume of each hole wall gap of each hole, and marking as v jk Where j represents the number of the j-th borehole, j=1, 2, g, k represents the number of the k-th hole wall gap, k=1, 2, f, which is substituted into the formulaObtaining the soil compactness W of the road side slope, wherein v 0 Representing a preset hole wall gap reference volume, wherein g is the number of drilling holes;
taking a set amount of soil in each drilling hole as a sample to be measured, marking the sample as each soil sample, weighing each soil sample, and marking the weight as the initial wet weight M of each soil sample j wet Drying each soil sample to constant weight at a certain temperature, weighing the dried soil sample, and recording as dry weight M of each soil sample j trunk By the formulaAnd obtaining the soil water content H of the road side slope, wherein g represents the number of drilling holes.
9. The highway side slope unmanned aerial vehicle inspection early warning system based on multi-source data according to claim 8, wherein the specific analysis process of the risk correction module is as follows:
the first step, networking with a local meteorological monitoring system, setting a future monitoring time period according to the highway slope risk prediction time length, reading rainfall and wind power levels in each time period in the highway slope risk prediction time length from the future monitoring time period, and respectively marking the rainfall and the wind power levels as R x 、F x Wherein x represents the x-thThe number of the period, x=1, 2,..y, y, simultaneously reads the soil compactness W of the road side slope, the soil moisture content H of the road side slope, and substitutes it into the formulaObtaining the correction coefficient of the road side slope danger degree>Wherein H is 0 、R 0 、F 0 Respectively represents the standard soil water content, rainfall, wind power grade, eta of the highway side slope under the safety condition 1 、η 2 、η 3 、η 4 Respectively representing the set weight factors of soil compactness, soil water content, rainfall and wind power level, wherein y is the number of time periods;
step two, reading the road side slope dangerous degree correction coefficientComparing the road side slope risk degree correction coefficient with a preset risk degree correction coefficient threshold value, and substituting the road side slope risk degree correction coefficient into the formula +_, if the road side slope risk degree correction coefficient is greater than or equal to the preset risk degree correction coefficient threshold value>Obtaining the corrected highway slope risk prediction time period t 'mu' 1 And e is a natural constant and is fed back to the system.
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| CN118278293A (en) * | 2024-06-03 | 2024-07-02 | 山东恒通公路工程有限公司 | Highway construction safety protection system based on data analysis |
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| CN118278293A (en) * | 2024-06-03 | 2024-07-02 | 山东恒通公路工程有限公司 | Highway construction safety protection system based on data analysis |
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