Risk zoning and early warning methods for geological hazards on mountain highway slopes

Abstract

The invention relates to the technical field of highway slope geological disaster management, and discloses a risk zoning and early warning method for mountain highway slope geological disasters, comprising the following steps: step 1, obtaining the slope geological data of the mountain highway through a network connection to form a geological data set; step 2, obtaining the real-time meteorological data of the mountain highway through a network connection to a big data platform to form a meteorological data set; step 3, obtaining the real-time video data of the mountain highway through a network connection to a monitoring device to form a monitoring data set; step 4, analyzing and generating risk scores and average scores according to the geological data set, dividing the geological disaster risk levels of all road sections from high to low, and comprehensively evaluating the disaster risk more accurately; step 5, identifying the video data set through image recognition technology, judging whether the mountain highway slope has geological disasters, and generating a corresponding disaster area, and then generating an early warning report or an alarm report in combination with the meteorological data set, so that the prevention and control capability is strong and the early warning effect is good.

Description

Technical Field

[0001] The invention relates to the technical field of highway slope geological disaster management, and in particular to a risk zoning and early warning method for highway slope geological disasters in mountainous areas. Background Art

[0002] Highway geological disaster hazards refer to the geological disaster points or sections that may exist along the highway. The purpose is to identify and evaluate these potential danger points so that preventive measures can be taken. The division of risk areas requires the use of a variety of technical means to conduct geological surveys of road cut slopes, including preliminary interpretation of high-precision optical remote sensing images, quantitative surface detection using InSAR technology, and detailed exploration using LiDAR technology. According to the "Specifications for Risk Assessment of Geological Hazards on Mountain Highway Slopes", the basic requirements and evaluation steps for risk assessment at different stages are clarified. Geological disaster early warning focuses on capturing the entire process of local deformation and destruction of landslide bodies, and then uses scientific methods to identify, evaluate and monitor potential sources of danger, and take effective preventive measures, which can greatly reduce the risks brought by geological disasters. With the continuous advancement and application of technology, geological disaster risk zoning and early warning methods will become more refined and efficient, safeguarding the safety of highway construction and operation.

[0003] At present, the traditional risk zoning and early warning methods for geological disasters on mountain highway slopes fail to fully consider all potential risk factors, resulting in the difficulty in ensuring the timeliness and reliability of geological disaster risk level assessment. In addition, the ability to prevent and control geological disasters is poor and the early warning effect is not good. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the existing technology, the present invention provides a risk zoning and early warning method for geological disasters on the slopes of mountainous roads, which has the advantages of more accurate comprehensive assessment of disaster risks, strong prevention and control capabilities and good early warning effects. It solves the problems of poor timeliness and effectiveness of risk assessment, poor disaster prevention and control capabilities and poor early warning effects in traditional risk zoning and early warning methods for geological disasters on the slopes of mountainous roads.

[0006] (II) Technical solution

[0007] To achieve the above object, the present invention provides the following technical solution: a method for zoning and early warning of geological disaster risks on mountain highway slopes, comprising the following steps:

[0008] Step 1: Obtain the slope geological data of the mountain road by connecting the multi-dimensional monitoring instrument through the network, and then form a geological data set by combining the acquired data;

[0009] Step 2: Obtain real-time meteorological data of mountain roads through a network connection to a big data platform, and then assemble the acquired data into a meteorological data set;

[0010] Step 3: Acquire real-time video data of mountain roads through network connection monitoring devices, and then form a monitoring data set with the acquired data;

[0011] Step 4: Analyze and generate risk scores based on geological data sets and average rating , and then based on the risk scores of all sections of mountain roads , classify the geological disaster risk levels of all road sections from high to low;

[0012] Step 5: Use image recognition technology to identify the video data set, determine whether there is a geological disaster on the mountain road slope, and generate the corresponding disaster area , and then combined with the meteorological data set to generate the corresponding early warning report or alarm report.

[0013] Preferably, in step 1, the expression of the geological data set is , to The slope geological data of each section of the mountain highway are corresponded in turn. represents the slope geological data, which includes slope gradient, surface displacement and soil moisture content. Indicates the section name of the mountain road, 1 to Indicates that the mountain road has part.

[0014] Preferably, in step 2, the expression of the meteorological data set is , to The meteorological data of each time point on the mountain highway are corresponded in sequence, with the superscript Indicates the time point of obtaining meteorological data, 1 to Indicates that the obtained mountain highway meteorological data is Group.

[0015] Preferably, in step 3, the expression of the monitoring data set is , to Corresponding to the video data of each time point of the mountain road, the superscript Indicates the time point of obtaining video data, subscript Indicates the section name of the mountain road, 1 to Indicates that the obtained mountain highway video data has Group.

[0016] Preferably, in step 4, the risk score The calculation process is as follows:

[0017] Extracting the mountain roads from the geological data set The slope geological data of each road section and the The slope of the road section is marked as , the surface displacement is marked as , soil moisture content is marked as ;

[0018]

[0019] In the formula, represents the risk score, It represents the fixed constant used in the risk score calculation formula to measure the risk level of the slope, that is, the safety standard slope. Indicates The difference between the side slope of a road section and the safety standard slope, Represents the weight of the slope difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of surface displacement, i.e., the safety standard displacement. Indicates The difference between the surface displacement of each road section and the safety standard displacement, represents the weight of the displacement difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of soil moisture content, which is the safe standard moisture content. Indicates The difference between the soil moisture content of each road section and the safety standard moisture content, Represents the weight of the soil moisture difference in the risk score calculation formula, , and are fixed constants, and , Indicates according to , and Weight, comprehensive The sum of the slope difference, displacement difference and soil moisture content difference of each road section is the risk score.

[0020] Preferably, in step 4, the risk scores of all sections of the mountain highway are Marked as , , , , , subscript Indicates the section name of the mountain road, 1 to Indicates that the mountain road has Then, based on the risk scores of all sections of mountain roads , analysis generates average rating , and its calculation formula is as follows:

[0021]

[0022] In the formula, Indicates the average rating. Represents the risk score of all sections of mountain roads The sum is divided by the total number of road segments, and the resulting value is the average score.

[0023] Preferably, in step 4, the risk scores of all sections of the mountain highway are Arrange from high to low, and then score the risk Greater than or equal to average rating The mountainous highway sections are divided into high-risk sections and the risk scores are Below average rating The mountainous highway sections are classified as low-risk sections.

[0024] Preferably, in step 5, if the road surface of the mountainous highway in the video data set is covered by gravel, soil, water or plants, it means that a geological disaster has occurred on the slope of the mountainous highway, and then the road surface coverage area is identified by image recognition technology and marked as , corresponding to the generated disaster area If the mountain road surface in the video dataset is flat and not covered, it means that there is no geological disaster on the mountain road slope, and no disaster area is generated. .

[0025] Preferably, in step 5, the disaster area The calculation formula is as follows:

[0026]

[0027] In the formula, Indicates the disaster area, Indicates comprehensive calculation of mountain roads The sum of the covered areas of the road sections is the disaster area.

[0028] Preferably, in step 5, if there is precipitation or snowfall in the meteorological data set, the risk scores of all sections of the mountain highway are calculated. If there is precipitation or snowfall in the meteorological data set, and the road surface of the mountain road in the video data set is covered by gravel, mud, water or plants, the risk score of all sections of the mountain road is used to generate an early warning report. , geological disaster risk level and disaster area Generate an alarm report.

[0029] Compared with the prior art, the present invention provides a risk zoning and early warning method for geological disasters on mountain highway slopes, which has the following beneficial effects:

[0030] 1. The present invention obtains the slope geological data of mountain roads through network connection with multi-dimensional monitoring instruments, obtains the real-time meteorological data of mountain roads through big data platforms, and obtains the real-time video data of mountain roads through monitoring devices, and classifies them into geological data sets, meteorological data sets, and monitoring data sets. According to the geological data sets, risk scores are generated through analysis. and average rating , comprehensively assess the potential risk factors of each section of mountain roads, and then assign risk scores to all sections of mountain roads. The geological disaster risk levels of all road sections are divided from high to low, making the comprehensive assessment of disaster risks more accurate.

[0031] 2. The present invention uses image recognition technology to identify video data sets to determine whether there are geological disasters on the mountain road slopes. If the video data shows that the mountain road surface is covered with sand, soil, water or plants, it means that the mountain road slopes have already experienced geological disasters. Then, the image recognition technology is used to identify the road surface coverage area. , corresponding to the generated disaster area ,The total statistical of road coverage area can assist decision makers in determining which areas need priority maintenance or reinforcement, thereby reducing disaster losses.,If the mountain road pavement in the video dataset is flat and not covered, it means that no geological disaster occurs on the mountain road slope, and no disaster area is generated. If there is precipitation or snowfall in the meteorological data set, the risk score of all sections of the mountain highway is The system generates early warning reports based on the risk level of geological disasters, notifies possible disaster risks in advance, and helps mountain managers improve their awareness of geological disaster prevention. If there is precipitation or snowfall in the meteorological data set, and the road surface of the mountain road in the video data set is covered by sand, mud, water or plants, the risk score of all sections of the mountain road is used to generate early warning reports based on the risk level of geological disasters. , geological disaster risk level and disaster area Generate warning reports, provide detailed information after a disaster occurs, assist governments and rescue agencies in quickly formulating emergency response plans, and have strong prevention and control capabilities and good early warning effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] Since the traditional risk zoning and early warning methods for geological disasters on mountain highway slopes fail to fully consider all potential risk factors, the timeliness and reliability of geological disaster risk level assessment are difficult to guarantee. In addition, the prevention and control capabilities of geological disasters are poor and the early warning effect is not good. Therefore, the risk zoning and early warning methods for geological disasters on mountain highway slopes are provided. Please refer to Figure 1 , the method comprises the following steps:

[0035] Step 1: Obtain the slope geological data of the mountain road through the network connection multi-dimensional monitoring instrument, and then form the acquired data into a geological data set. The expression of the geological data set is , to The slope geological data of each section of the mountain highway are corresponded in turn. It represents the geological data of slope, including slope gradient, surface displacement and soil moisture content. Steeper slopes are more prone to landslides, collapses and other geological disasters. By measuring the slope gradient, high-slope areas can be effectively divided into high-risk areas, so that more stringent land use control and monitoring measures can be implemented in these areas. The monitoring of surface displacement directly reveals the deformation trend and rate of the mountain, providing a direct basis for judging the stability of the slope. The monitoring of soil moisture content directly reflects the looseness of the slope soil, and can output early warning reports in advance. Indicates the section name of the mountain road, 1 to Indicates that the mountain road has part;

[0036] Step 2: Connect to the big data platform through the network to obtain real-time meteorological data of mountain roads, and then form a meteorological data set with the acquired data. The expression of the meteorological data set is: , to The meteorological data corresponding to each time point of the mountain highway are shown in sequence. Continuous rainfall is one of the main factors that induce geological disasters such as landslides and mud-rock flows. Real-time monitoring of rainfall can accurately predict the risk of geological disasters. Indicates the time point of obtaining meteorological data, 1 to Indicates that the obtained mountain highway meteorological data is Group;

[0037] Step 3: Connect the monitoring device through the network to obtain real-time video data of the mountain road, and then form a monitoring data set with the acquired data. The expression of the monitoring data set is: , to The video data corresponding to each time point of the mountain road is used to monitor the traffic status of the mountain road in real time. It can quickly find out whether the road surface of the mountain road is covered by sand, mud, water or plants, which effectively improves the disaster management capacity in the mountainous area. Indicates the time point of obtaining video data, subscript Indicates the section name of the mountain road, 1 to Indicates that the obtained mountain highway video data has Group;

[0038] Step 4: Analyze and generate risk scores based on geological data sets and average rating , and then based on the risk scores of all sections of mountain roads , classify the geological disaster risk levels of all road sections from high to low;

[0039] Extracting the mountain roads from the geological data set The slope geological data of each road section and the The slope of the road section is marked as , the surface displacement is marked as , soil moisture content is marked as ;

[0040]

[0041] In the formula, represents the risk score, It represents the fixed constant used in the risk score calculation formula to measure the risk level of the slope, that is, the safety standard slope. Indicates The difference between the side slope of a road section and the safety standard slope, Represents the weight of the slope difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of surface displacement, i.e., the safety standard displacement. Indicates The difference between the surface displacement of each road section and the safety standard displacement, represents the weight of the displacement difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of soil moisture content, which is the safe standard moisture content. Indicates The difference between the soil moisture content of each road section and the safety standard moisture content, Represents the weight of the soil moisture difference in the risk score calculation formula, , and are fixed constants, and , Indicates according to , and Weight, comprehensive The sum of the slope difference, displacement difference and soil moisture difference of each road section is the risk score, which comprehensively evaluates the potential risk factors of each section of mountain roads;

[0042] Risk scores for all sections of mountain roads The above formula is used to calculate the risk scores of all sections of mountain roads. Marked as , , , , , subscript Indicates the section name of the mountain road, 1 to Indicates that the mountain road has part;

[0043] Based on the risk score of all sections of mountain roads , analysis generates average rating , and its calculation formula is as follows:

[0044]

[0045] In the formula, Indicates the average rating. Represents the risk score of all sections of mountain roads The sum is divided by the total number of road segments, and the resulting value is the average score;

[0046] Risk scores for all sections of mountain roads Arrange from high to low, and then score the risk Greater than or equal to average rating The mountainous highway sections are divided into high-risk sections and the risk scores are Below average rating The mountainous highway sections are divided into low-risk sections, and the comprehensive assessment of disaster risks is more accurate;

[0047] Step 5: Identify the video dataset through image recognition technology. For example, cloud platforms such as Google Cloud Vision AI and Amazon Rekognition can provide image recognition services to analyze road images, determine whether there are geological disasters on the slopes of mountain roads, and generate the corresponding disaster area. , and then combined with the meteorological data set to generate corresponding early warning reports or alarm reports;

[0048] If the road surface of a mountain road in the video data set is covered by gravel, soil, water or plants, it means that a geological disaster has occurred on the slope of the mountain road. Then, the road surface coverage area is identified through image recognition technology and marked as , corresponding to the generated disaster area , and its calculation formula is as follows:

[0049]

[0050] In the formula, Indicates the disaster area, Indicates comprehensive calculation of mountain roads The sum of the road surface coverage areas is the disaster area. Statistical sum of road surface coverage areas can help decision makers determine which areas need priority maintenance or reinforcement, thereby reducing disaster losses.

[0051] If the mountain road surface in the video dataset is flat and not covered, it means that there is no geological disaster on the mountain road slope, and no disaster area is generated. ;

[0052] If there is precipitation or snowfall in the meteorological data set, the risk score of all sections of the mountain road is The system generates early warning reports based on the risk level of geological disasters, notifies possible disaster risks in advance, and helps mountain managers improve their awareness of geological disaster prevention. If there is precipitation or snowfall in the meteorological data set, and the road surface of the mountain road in the video data set is covered by sand, mud, water or plants, the risk score of all sections of the mountain road is used to generate early warning reports based on the risk level of geological disasters. , geological disaster risk level and disaster area Generate warning reports, provide detailed information after a disaster occurs, assist governments and rescue agencies in quickly formulating emergency response plans, and have strong prevention and control capabilities and good early warning effects.

[0053] Example 1: In this experiment, a highway with a slope of 20% and a surface displacement of 0.7m was selected as the experimental object. After testing, the soil moisture content of the highway slope was 18%, and the risk score was The safety standard slope in the calculation formula is set to 10%, the safety standard displacement is set to 0.3m, and the safety standard moisture content is 8%. , and The weights are set to 0.4, 0.3, and 0.3, and the risk score of this mountain road The calculation formula is as follows:

[0054]

[0055] In the formula, represents the risk score, It represents the fixed constant used in the risk score calculation formula to measure the risk level of the slope. 10% is the safety standard slope. Indicates the difference between the side slope of the road section and the safety standard slope. Represents the weight of the slope difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of ground displacement. 0.3m is the safety standard displacement. It represents the difference between the surface displacement of the road section and the safety standard displacement. represents the weight of the displacement difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of soil moisture content. 8% is the safe standard moisture content. It indicates the difference between the soil moisture content of the road section and the safety standard moisture content. Represents the weight of the soil moisture difference in the risk score calculation formula, , and are fixed constants, and , Indicates according to , and The weight is the sum of the slope difference, displacement difference and soil moisture difference of the road section. 7.12 is the risk score of the road section.

[0056] Example 2: In this experiment, five sections of mountain roads were selected as experimental objects, and the risk scores of the five sections were The average ratings of the five sections are 6.2, 4.8, 4.9, 9.6 and 2.2 respectively. The calculation formula is as follows:

[0057]

[0058]

[0059] In the formula, Indicates the average rating. Represents the risk score of all sections of mountain roads The sum is divided by the total number of road sections, and 5.54 is the average score.

[0060] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. Risk zoning and early warning method for geological disasters on mountain highway slopes, characterized by: The following steps are involved: Step 1: Obtain the slope geological data of the mountain road by connecting the multi-dimensional monitoring instrument through the network, and then form a geological data set by combining the acquired data; Step 2: Connect to the big data platform through the network to obtain real-time meteorological data of mountain roads, and then form a meteorological data set with the obtained data; Step 3: Acquire real-time video data of mountain roads through network connection monitoring devices, and then form a monitoring data set with the acquired data; Step 4: Analyze and generate risk scores based on geological data sets and average rating , and then based on the risk scores of all sections of mountain roads , classify the geological disaster risk levels of all road sections from high to low; Risk Scoring The calculation process is as follows: Extracting the mountain roads from geological data sets The slope geological data of each road section and the The slope of the road section is marked as , the surface displacement is marked as , soil moisture content is marked as ; In the formula, represents the risk score, It represents the fixed constant used in the risk score calculation formula to measure the risk level of the slope, that is, the safety standard slope. Indicates The difference between the side slope of a road section and the safety standard slope, Represents the weight of the slope difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of surface displacement, i.e., the safety standard displacement. Indicates The difference between the surface displacement of each road section and the safety standard displacement, represents the weight of the displacement difference in the risk score calculation formula, It represents the fixed constant used in the risk score calculation formula to measure the risk level of soil moisture content, which is the safe standard moisture content. Indicates The difference between the soil moisture content of each road section and the safety standard moisture content, Represents the weight of the soil moisture difference in the risk score calculation formula, , and are fixed constants, and , Indicates according to , and Weight, comprehensive The sum of the slope difference, displacement difference and soil moisture difference of each road section is the risk score; Step 5: Use image recognition technology to identify the video data set, determine whether there is a geological disaster on the mountain road slope, and generate the corresponding disaster area , combined with the meteorological data set, to generate the corresponding early warning report or alarm report.

2. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 1 is characterized by: In step 1, the expression of the geological data set is: , to The slope geological data of each section of the mountain highway are corresponded in turn. represents the slope geological data, which includes slope gradient, surface displacement and soil moisture content. Indicates the section name of the mountain road, 1 to Indicates that the mountain road has part.

3. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 2 is characterized by: In step 2, the expression of the meteorological data set is: , to The meteorological data of each time point on the mountain highway are corresponded in sequence, with the superscript Indicates the time point of obtaining meteorological data, 1 to Indicates that the obtained mountain highway meteorological data is Group.

4. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 3 is characterized by: In step 3, the expression of the monitoring data set is: , to Corresponding to the video data of each time point of the mountain road, the superscript Indicates the time point of obtaining video data, subscript Indicates the section name of the mountain road, 1 to Indicates that the obtained mountain road video data has Group.

5. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 4 is characterized by: In step 4, the risk scores of all sections of mountain roads are calculated. Marked as , , , , , subscript Indicates the section name of the mountain road, 1 to Indicates that the mountain road has Then, based on the risk scores of all sections of mountain roads , analysis generates average rating , and its calculation formula is as follows: In the formula, Indicates the average rating. Represents the risk score of all sections of mountain roads The sum is divided by the total number of road segments, and the resulting value is the average score.

6. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 5 is characterized by: In step 4, the risk scores of all sections of mountain roads are calculated. Arrange from high to low, and then score the risk Greater than or equal to average rating The mountainous highway sections are divided into high-risk sections and the risk scores are Below average rating The mountainous highway sections are classified as low-risk sections.

7. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 6 is characterized by: In step 5, if the road surface of the mountainous highway in the video data set is covered by gravel, soil, water or plants, it means that a geological disaster has occurred on the slope of the mountainous highway. Then, the road surface coverage area is identified by image recognition technology and marked as , corresponding to the generated disaster area If the mountain road surface in the video dataset is flat and not covered, it means that there is no geological disaster on the mountain road slope, and no disaster area is generated. .

8. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 7 is characterized by: In step 5, the disaster area The calculation formula is as follows: In the formula, Indicates the disaster area, Indicates comprehensive calculation of mountain roads The sum of the covered areas of the road sections is the disaster area.

9. The method for zoning and early warning of geological disaster risks on mountain highway slopes according to claim 8 is characterized by: In step 5, if there is precipitation or snowfall in the meteorological data set, the risk scores of all sections of the mountain highway are calculated. If there is precipitation or snowfall in the meteorological data set, and the road surface of the mountain road in the video data set is covered by gravel, mud, water or plants, the risk score of all sections of the mountain road is used to generate an early warning report. , geological disaster risk level and disaster area Generate an alarm report.

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