Expressway slope collapse identification and early warning method

Abstract

The embodiment of the invention discloses an expressway slope collapse identification and early warning method, which comprises the steps of delimiting an electronic fence, dividing the electronic fence into a plurality of sub-road sections to be monitored, monitoring and collecting vehicle motion data, judging whether the sub-road sections are early warning road sections or not, judging whether the sub-road sections are risk road sections or not for the early warning road sections, and judging whether the sub-road sections are risk road sections or not for the risk road sections. And judging whether a warning signal needs to be sent or not, and executing risk early warning measures for the road section sending the warning signal. The position, speed and acceleration information of the vehicle is obtained based on the GPS and IMU sensors of the vehicle running on the highway, the road characteristic parameters and the difference degree are calculated according to the acceleration characteristics of the vehicles on the two sides of the double-amplitude road section in the gravity direction, the road condition is analyzed, an early warning signal is sent out in time, and the early warning effect is achieved. And precious time is won for timely intervention of expressway management personnel, so that personnel casualty accidents caused by large-range expressway slope collapse are avoided to the greatest extent.

Description

Technical Field

[0001] The invention relates to the field of landslide identification, and in particular to a method for identifying and warning of highway slope landslides. Background Art

[0002] Highway slope collapse seriously affects road traffic safety and endangers the lives and property of drivers and passengers. In the existing technology, it is necessary to set up a physical monitoring structure on the highway, and the early warning signal is issued late, which cannot buy response time for managers and vehicles on the highway. Summary of the invention

[0003] The purpose of the present invention is to provide a method for identifying and warning of highway slope collapse, which obtains the position, speed and acceleration information of the vehicle based on the GPS and IMU sensors carried by the vehicle traveling on the highway, calculates the road characteristic parameters and the difference according to the acceleration characteristics in the gravity direction of the vehicles on both sides of the double-width road section, analyzes the road conditions, and issues warning signals in time, so as to buy precious time for highway management personnel to intervene in time, thereby avoiding casualties caused by large-scale highway slope collapse to the greatest extent.

[0004] The present invention provides a method for identifying and warning of highway slope collapse, the method comprising:

[0005] Demarcate electronic fences on operational sections of expressways;

[0006] Determine a plurality of sub-sections to be monitored within the electronic fence, wherein each of the sub-sections to be monitored includes a destination sub-section and an incoming sub-section;

[0007] Real-time monitoring of the motion data of vehicles passing through each of the sub-road sections to be monitored, and calculating a first difference between the outgoing sub-road section and the incoming sub-road section of each of the sub-road sections to be monitored within a first time period according to the motion data, and when the first difference is within a first range, the sub-road section to be monitored corresponding to the first difference is determined to be a warning section;

[0008] For the warning section, extract first motion data of vehicles within a first number range passing through the warning section, and calculate a second difference between the outgoing sub-section and the incoming sub-section of the warning section according to the first motion data;

[0009] When the second difference is within the first range, the warning section is determined to be a risk section, and the information of the risk section is transmitted to the management center to issue a risk signal;

[0010] The first difference and the second difference are determined based on the acceleration in the gravity direction of vehicles passing through the sub-section to be monitored.

[0011] As a further improvement of the present invention, the step of calculating the first difference between the outgoing sub-segment and the incoming sub-segment of each sub-segment to be monitored within the first time period according to the motion data includes:

[0012] A first characteristic parameter in the first time period is calculated according to the motion data, and the first difference in the first time period is calculated using the first characteristic parameter.

[0013] As a further improvement of the present invention, the first time period includes a plurality of unit time periods, and the unit difference between the outgoing sub-segment and the incoming sub-segment of each sub-segment to be monitored in each unit time period is calculated according to the motion data;

[0014] The unit difference degree in each unit time period obeys a normal distribution, and the first range is expressed as (μ-3σ, μ+3σ);

[0015] Wherein, μ is the mean of the multiple unit differences that obey the normal distribution, and σ is the variance of the multiple unit differences that obey the normal distribution.

[0016] As a further improvement of the present invention, the first characteristic parameter in the first time period is calculated according to the motion data, which is expressed as:

[0017] a Ind LL =0.3a Mean LL +0.3a Med LL +0.4a Std LL ;

[0018] a Ind RL =0.3a Mean RL +0.3a Med RL +0.4a Std RL ;

[0019] Among them, a Mean LL and a Mean RL respectively represent the average values ​​of the accelerations in the gravity direction of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a MedLL and a Med RL respectively represent the median of the acceleration in the gravity direction of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Std LL and a Std RL represents the standard deviation of the acceleration in the gravity direction of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Ind LL and a Ind RL They are respectively represented as the first characteristic parameters of the outgoing sub-segment and the incoming sub-segment in each unit time period in the first time period.

[0020] As a further improvement of the present invention, the first difference degree in each first time period is calculated by using the first characteristic parameter, which is expressed as:

[0021] γ=(a Ind LL -a Ind RL ) / a Ind LL ;

[0022] Wherein, γ represents the first difference degree in the first time period, a Ind LL and a Ind RL They are respectively represented as the first characteristic parameters of the outgoing sub-segment and the incoming sub-segment in the first time period.

[0023] As a further improvement of the present invention, when the second difference is not within the first range, the warning section is determined to be the sub-section to be monitored.

[0024] As a further improvement of the present invention, the step of calculating the second difference between the outgoing sub-segment and the incoming sub-segment of the warning section according to the first motion data includes:

[0025] A second characteristic parameter of the warning section is calculated according to the first motion data, and the second difference of the warning section is calculated according to the second characteristic parameter.

[0026] As a further improvement of the present invention, the method comprises:

[0027] For the risky road section, extract the second motion data of the risky road section within the first unit time period after being designated as the sub-road section to be monitored, calculate the third characteristic parameter of the risky road section according to the second motion data, and use the third characteristic parameter as the indicator parameter;

[0028] The second characteristic parameter of the outgoing sub-section and the second characteristic parameter of the incoming sub-section are respectively compared with the index parameter, and the outgoing sub-section or the incoming sub-section in the risk section is determined as a specific risk section according to the comparison result.

[0029] As a further improvement of the present invention, the motion data includes the position coordinates, vehicle speed and acceleration in the direction of gravity of the vehicle.

[0030] As a further improvement of the present invention, the method comprises:

[0031] The risky road section is monitored in real time to obtain third motion data, a fourth characteristic parameter of the risky road section is calculated based on the third motion data, a difference rate between the fourth characteristic parameter of the outgoing sub-section and the fourth characteristic parameter of the incoming sub-section on the risky road is calculated, and a warning signal is issued based on the difference rate.

[0032] The beneficial effects of the present invention are as follows: the road characteristic parameters and the difference are calculated according to the acceleration characteristics in the gravity direction of the vehicles on both sides of the double-width road section, the road conditions are analyzed, and early warning signals are issued in time to buy precious time for the highway management personnel to intervene in time, thereby avoiding casualties caused by large-scale highway slope collapse to the greatest extent. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0034] Figure 1 A schematic diagram of the monitoring section division and data collection in an exemplary embodiment of the present invention;

[0035] Figure 2 The present invention is a logic diagram of a method for identifying and warning of highway slope collapse in an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0038] In addition, in the description of the present invention, the terms used are only for illustrative purposes and are not intended to limit the scope of the present invention. The terms "include" and / or "comprise" are used to specify the existence of the elements, steps, operations and / or components, but do not exclude the existence or addition of one or more other elements, steps, operations and / or components. The terms "first", "second" and the like may be used to describe various elements, do not represent the order, and do not limit these elements. In addition, in the description of the present invention, unless otherwise specified, the meaning of "multiple" is two and more than two. These terms are only used to distinguish one element from another element. In conjunction with the following drawings, these and / or other aspects become apparent, and it is easier for a person of ordinary skill in the art to understand the description of the embodiments of the present invention. The accompanying drawings are used to describe the embodiments of the present invention for illustrative purposes only. Those skilled in the art will easily recognize from the following description that, without departing from the principles of the present invention, alternative embodiments of the structures and methods shown in the present invention can be adopted.

[0039] The basic principle of the embodiment described in the present invention is as follows: in the early stage of collapse under the highway pavement, the soil underneath begins to loosen partially, which usually causes the single-width pavement (i.e., the outgoing sub-section or the incoming sub-section) to loosen first. When an early collapse occurs under a certain pavement, the acceleration of the vehicles passing through this pavement in the direction of gravity will show a certain regularity. In the early stage of collapse of a double-width pavement, the acceleration of the vehicles on one side of the pavement in the direction of gravity will show a unique change, while the acceleration of the vehicles on the other side of the pavement in the direction of gravity will remain relatively unchanged. As the collapse trend evolves, the difference in the acceleration of the vehicles on both sides of the pavement in the direction of gravity will tend to be significant. Therefore, the collapse of the highway slope can be identified early according to the variation law of the acceleration of the vehicles in the direction of gravity. Since this method is aimed at the early stage of collapse of the highway slope, the extreme situation of the direct collapse of the double-width section is not considered by this method. In addition, special sections such as bridges and tunnels are not considered by this method.

[0040] When using the method for identifying and warning of highway slope collapse described in an embodiment of the present invention, it is necessary to interlock the cloud server with the GPS and IMU sensor signals of passing vehicles on the operating section, wherein the cloud server is installed in the highway management center, and the GPS and IMU sensors can collect vehicle motion data.

[0041] The cloud server includes a data collection module, a data processing module, a data storage device and a control module. The data collection module can collect the number of vehicles traveling on the highway and the vehicle motion data from the vehicle GPS and IMU sensors; the data processing module can analyze and process the information from the data collection module; the storage device is used to store historical data; the control module can control the road infrastructure and information release device, and issue early warnings based on the analysis results of the data processing module.

[0042] A method for identifying and warning of highway slope collapse according to an embodiment of the present invention comprises:

[0043] Demarcate electronic fences on operational sections of expressways;

[0044] Determine a plurality of sub-sections to be monitored within the electronic fence, wherein each of the sub-sections to be monitored includes a destination sub-section and an incoming sub-section;

[0045] Real-time monitoring of the motion data of vehicles passing through each of the sub-road sections to be monitored, and calculating a first difference between the outgoing sub-road section and the incoming sub-road section of each of the sub-road sections to be monitored within a first time period according to the motion data, and when the first difference is within a first range, the sub-road section to be monitored corresponding to the first difference is determined to be a warning section;

[0046] For the warning section, extract first motion data of vehicles within a first number range passing through the warning section, and calculate a second difference between the outgoing sub-section and the incoming sub-section of the warning section according to the first motion data;

[0047] When the second difference is within the first range, the warning section is determined to be a risk section, and the information of the risk section is transmitted to the management center to issue a risk signal;

[0048] The first difference and the second difference are determined based on the acceleration in the gravity direction of vehicles passing through the sub-section to be monitored.

[0049] Specifically, Figure 1 , Figure 2 As shown, a method for identifying and warning of highway slope collapse according to an embodiment of the present invention comprises the following steps:

[0050] S1: Determine the early landslide identification range based on the actual situation of the expressway and set up an electronic fence for the operating section of the expressway;

[0051] The actual conditions include geological conditions, topography, surrounding environment and historical landslide records. By analyzing the actual conditions of the expressway sections, the set of sections to be monitored is preliminarily determined, which is specifically expressed as X = {X1, X2, ..., X n};

[0052] The road sections to be monitored are gathered together to define an electronic fence. The tool for defining the electronic fence is not specifically limited here. Preferably, the boundaries of the early landslide identification range are accurately marked on the map, and various changes within the electronic fence are monitored in real time on the cloud server to promptly discover various abnormal situations that may lead to landslides and issue an alarm so that relevant personnel can take measures quickly.

[0053] S2: Determine a road segmentation method, and determine a plurality of sub-road segments to be monitored within the electronic fence;

[0054] like Figure 1 As shown, multiple monitoring sections are determined within the electronic fence, and the monitoring sections are divided into sub-sections to be monitored. First, the monitoring section X is determined. n The total length of the road is specifically represented by L. Each sub-section includes a double-lane road (i.e., lanes on both sides), specifically a going sub-section and an incoming sub-section, and the going sub-section and the incoming sub-section are respectively named LL i and RL i , L is divided into equal intervals according to the length L0, and finally a total of N = L / / L0 sub-segments to be monitored are obtained. The set of sub-segments to be monitored is represented as {(LL1,RL1),(LL2,RL2),…,(LL N ,RL N )};

[0055] Based on the data set of the traffic management department, and with the help of advanced measurement tools and geographic information system technology, the length of each section is ensured to be accurate. At the same time, the boundaries of the sections should be clearly marked to facilitate the installation of monitoring equipment and data collection;

[0056] According to the actual situation of the expressway, the length of the interval section L0 in different areas is appropriately adjusted. In areas with complex geological conditions and prone to landslides, the length of the interval section can be appropriately shortened to increase the monitoring density, and the length of the sub-section to be monitored is set to L0(1-α); in areas with relatively stable geological conditions, the length of the interval section can be appropriately expanded to reduce the monitoring cost, and the length of the sub-section is set to L0(1+α), where α represents the length adjustment coefficient.

[0057] S3: Real-time monitoring of the motion data of vehicles passing through each of the sub-segments to be monitored; identifying and tracking the vehicles on each sub-segment (LLi, RLi) through the cloud monitoring system. After the system is turned on, the motion data of the vehicle is obtained in real time. Optionally, the motion data includes the position coordinates, speed V and acceleration in the direction of gravity of the vehicle.

[0058] Multiple storage devices are set up in the cloud database to store each road segment (LL i ,RL i ) vehicle motion data, recording the acceleration of the vehicle in the direction of gravity, specifically including the acceleration of the vehicle in the direction of gravity of the going sub-segment and the acceleration of the vehicle in the direction of gravity of the coming sub-segment, where the gravity acceleration of the vehicle in the going sub-segment is represented by a i LL,ID , the gravity acceleration of the vehicle on the incoming sub-segment is expressed as a i RL,ID The gravity acceleration of the vehicles in the outgoing sub-section and the gravity acceleration of the vehicles in the incoming sub-section can reflect the acceleration, deceleration and vehicle bumps during the driving process. The LL and RL tables represent the outgoing and incoming roads, i represents the number of the sub-section to be monitored, and ID represents the number of the vehicle passing through this section. When the system is turned on, the ID of the first vehicle passing through the sub-section is recorded as 1, and then counted in sequence;

[0059] After the data is acquired, the data is preprocessed to exclude vehicle data with a speed lower than 30 km / h in the road section, collect data of other vehicles, and use statistical methods to remove outliers and erroneous data in the data to ensure the accuracy and reliability of the data. The preprocessed data is used as motion data, wherein the statistical method is selected according to actual conditions and is not specifically limited in this application. Preferably, the statistical method is the standard deviation method or the box plot method.

[0060] S4: Determine whether it is a warning section;

[0061] Calculate, according to the motion data, a first difference between the outgoing sub-segment and the incoming sub-segment of each of the sub-segments to be monitored within a first time period;

[0062] Calculate the first characteristic parameter in the first time period according to the motion data, calculate the average value, median and standard deviation of the acceleration in the gravity direction of all vehicles passing through the outgoing sub-segment and the incoming sub-segment of the sub-segment to be monitored in the first time period, respectively, and calculate the average value a Mean LL and a Mean RL Reflect sub-section (LL i ,RLi ) is the overall level of acceleration in the direction of gravity of the vehicle; through the median a Med LL and a Med RL Reflects the center position of the acceleration data in the direction of gravity to avoid being affected by extreme values; standard deviation a Std LL 、a Std RL It indicates the degree of dispersion of vehicle speed. The larger the standard deviation, the greater the fluctuation of acceleration in the gravity direction. Due to the potential changes in the terrain of the early collapsed road surface, the vertical acceleration fluctuates greatly. Therefore, the standard deviation a Std LL and a Std RL It can better reflect the trend of early landslide. Finally, the sub-section (LL i ,RL i )The first characteristic parameter of the roads on both sides can be further expressed as

[0063] a Ind LL =0.3a Mean LL +0.3a Med LL +0.4a Std LL

[0064] a Ind RL =0.3a Mean RL,I +0.3a Med RL +0.4a Std RL

[0065] Among them, a Mean LL and a Mean RL respectively represent the average values ​​of the accelerations in the direction of gravity of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Med LL and a Med RL respectively represent the median of the acceleration in the gravity direction of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Std LL and a Std RLrepresents the standard deviation of the acceleration in the gravity direction of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Ind LL and a Ind RL They are respectively represented as the first characteristic parameters of the outgoing sub-segment and the incoming sub-segment in each unit time period in the first time period.

[0066] The above method is a characteristic parameter calculation method, and the characteristic parameters calculated below all use this method.

[0067] The first difference degree in each of the first time periods is calculated using the first characteristic parameter, which is expressed as:

[0068] γ=(a Ind LL -a Ind RL ) / a Ind LL ;

[0069] Wherein, γ represents the first difference degree in each first time period, a Ind LL and a Ind RL They are respectively represented as the first characteristic parameters of the outgoing sub-segment and the incoming sub-segment in each unit time period in the first time period.

[0070] The above method is a difference calculation method, and the difference calculations mentioned below all use this method.

[0071] The first time period includes a plurality of unit time periods, and the unit difference between the outgoing sub-segment and the incoming sub-segment of each sub-segment to be monitored in each unit time period is calculated according to the difference calculation method, and the unit difference in each unit time period obeys a normal distribution with a mean of μ and a variance of σ, thereby determining the first range to be (μ-3σ, μ+3σ);

[0072] When the first difference degree γ is within a first range (μ-3σ, μ+3σ), the to-be-monitored sub-section corresponding to the first difference degree is determined as a warning section.

[0073] S5: Determine whether it is a risky road section;

[0074] For the warning section, the first motion data of vehicles within a first number range passing through the warning section are extracted. This method does not limit the first number range and can be set according to actual conditions. Preferably, the motion data of the outgoing subsection and the incoming subsection of the warning section when 100 vehicles pass through each are extracted as the first motion data. According to the first motion data, the second characteristic parameter of the warning section is calculated according to the characteristic parameter calculation method. According to the second characteristic parameter, the second difference between the outgoing subsection and the incoming subsection of the warning section is calculated according to the difference calculation method. When the second difference is within the first range, the warning section is determined as a risk section, and the information of the risk section is transmitted to the management center to send out a risk signal. When the second difference is not within the first range, the warning section is determined as the subsection to be monitored.

[0075] Optionally, for the risky road section, second motion data of the risky road section within the first unit time period after being designated as the sub-road section to be monitored is extracted, a third characteristic parameter of the risky road section is calculated according to the second motion data, and the third characteristic parameter is used as an indicator parameter;

[0076] The second characteristic parameter of the outgoing sub-section and the second characteristic parameter of the incoming sub-section are respectively compared with the index parameter, and the outgoing sub-section or the incoming sub-section in the risk section is determined as a specific risk section according to the comparison result.

[0077] Specifically, extract the second motion data of the risk section within the first unit time period after being designated as the sub-section to be monitored. It can be understood that the second motion data is the data of the risk section within the first unit time period after being designated within the electronic fence. According to the second motion data, the third characteristic parameter of the risk section is calculated according to the characteristic parameter calculation method. The third characteristic parameter is used as the index parameter. The index parameter of the destination sub-section and the index parameter of the arrival sub-section of the risk section are respectively expressed as a Ind-nor LL and a Ind-nor RL ;

[0078] The second characteristic parameter of the outgoing sub-section is compared with the indicator parameter of the outgoing sub-section of the risk section to obtain the outgoing difference, and the second characteristic parameter of the incoming sub-section is compared with the indicator parameter of the incoming sub-section of the risk section to obtain the incoming difference. According to the comparison result, it is determined that the outgoing sub-section or the incoming sub-section in the risk section is a specific risk section. When the incoming difference is greater than the outgoing difference, it can be judged that the incoming sub-section of the risk section is a specific risk section. When the outgoing difference is greater than the incoming difference, it can be judged that the outgoing sub-section of the risk section is a specific risk section.

[0079] The path risk status and location data of specific risk sections are sent to the management center to facilitate manual review by staff.

[0080] S6: Determine whether a warning signal needs to be issued;

[0081] For the risk section, the risk section is monitored in real time to obtain third motion data, and the fourth characteristic parameter of the risk section is calculated according to the third motion data and the characteristic parameter calculation method, and the difference rate between the fourth characteristic parameter of the outgoing sub-section and the fourth characteristic parameter of the incoming sub-section on the risk road is calculated. Specifically, the calculation formula of the difference rate Γ is:

[0082] Γ=(γ h+1 -γ h ) / γ h

[0083] Among them, γ h+1 represents the difference at time h+1, γ h Represents the difference at time h.

[0084] When the rate of increase of the difference rate exceeds a first standard, a warning signal is issued according to the difference rate, wherein the first standard can be determined according to actual conditions, and the present method does not make specific limitations on this. Preferably, when the rate of increase of the difference rate exceeds 5% per hour, a warning signal is immediately issued to notify the management personnel and take a series of emergency measures.

[0085] S7: Early warning methods; emergency measures include:

[0086] Remind drivers to pay attention to road conditions and use smartphones and electronic maps for warnings. Under normal circumstances, the advance warning distance is 300 meters to ensure that drivers have enough time to react. At night, when visibility is low and the risk is high, the warning distance is 500 meters in advance to give drivers more reaction time. At the same time, drivers should also be reminded to reduce speed and maintain safe driving.

[0087] Send warning information to drivers in a variety of ways. Push notifications pop up on drivers' phones to attract their attention. Voice prompts convey warning information in a timely manner when drivers use navigation or other applications; at the same time, variable information signs are set up on highways to increase drivers' vigilance, adjust according to actual conditions, and update road information in a timely manner to provide drivers with the latest road conditions guidance.

[0088] Among them, the order of risk size in the risk levels involved in this method is: risk of the sub-section to be monitored < risk of the warning section < risk of the risk section.

[0089] The first difference, second difference, first motion data, second motion data, third motion data, first characteristic parameter, second characteristic parameter, third characteristic parameter and fourth characteristic parameter mentioned in this application are all nicknames for calculation results in different calculation projects, and have no semantic order.

[0090] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0091] In addition, it will be understood by those skilled in the art that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims, any one of the claimed embodiments can be used in any combination.

[0092] It will be appreciated by those skilled in the art that, although the present invention has been described with reference to exemplary embodiments, various changes may be made and equivalents may be substituted for its elements without departing from the scope of the present invention. In addition, many modifications may be made to adapt specific circumstances or materials to the teachings of the present invention without departing from the essential scope of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed, but the present invention will include all embodiments falling within the scope of the appended claims.

Claims

1. A method for identifying and warning of highway slope collapse, characterized in that: The method comprises: Demarcate electronic fences on operational sections of expressways; Determine a plurality of sub-sections to be monitored within the electronic fence, wherein each of the sub-sections to be monitored includes a destination sub-section and an incoming sub-section; Real-time monitoring of the motion data of vehicles passing through each of the sub-road sections to be monitored, and calculating a first difference between the outgoing sub-road section and the incoming sub-road section of each of the sub-road sections to be monitored within a first time period according to the motion data, and when the first difference is within a first range, the sub-road section to be monitored corresponding to the first difference is determined to be a warning section; For the warning section, extract first motion data of vehicles within a first number range passing through the warning section, and calculate a second difference between the outgoing sub-section and the incoming sub-section of the warning section according to the first motion data; When the second difference is within the first range, the warning section is determined to be a risk section, and the information of the risk section is transmitted to the management center to issue a risk signal; The first difference and the second difference are determined based on the acceleration in the gravity direction of vehicles passing through the sub-section to be monitored.

2. The method for identifying and warning highway slope collapse according to claim 1, characterized in that: The calculating, according to the motion data, a first difference between the outgoing sub-segment and the incoming sub-segment of each sub-segment to be monitored within a first time period includes: A first characteristic parameter in the first time period is calculated according to the motion data, and the first difference in the first time period is calculated using the first characteristic parameter.

3. The method for identifying and warning highway slope collapse according to claim 2, characterized in that: The first time period includes a plurality of unit time periods, and a unit difference between the outgoing sub-segment and the incoming sub-segment of each sub-segment to be monitored in each unit time period is calculated according to the motion data; The unit difference degree in each unit time period obeys a normal distribution, and the first range is expressed as (μ-3σ, μ+3σ); Wherein, μ is the mean of the multiple unit differences that obey the normal distribution, and σ is the variance of the multiple unit differences that obey the normal distribution.

4. The method for identifying and warning of highway slope collapse according to claim 2, characterized in that: The first characteristic parameter in the first time period is calculated according to the motion data, and is expressed as: <h2 style=";text-align:left;direction:ltr">a<h2 style=";text-align:left;direction:ltr"> Ind <h2 style=";text-align:left;direction:ltr"> LL <h2 style=";text-align:left;direction:ltr"> =0.3a<h2 style=";text-align:left;direction:ltr"> Mean <h2 style=";text-align:left;direction:ltr"> LL <h2 style=";text-align:left;direction:ltr"> +0.3a<h2 style=";text-align:left;direction:ltr"> Med <h2 style=";text-align:left;direction:ltr"> LL <h2 style=";text-align:left;direction:ltr"> +0.4a<h2 style=";text-align:left;direction:ltr"> Std <h2 style=";text-align:left;direction:ltr"> LL <h2 style=";text-align:left;direction:ltr"> ; <h2 style=";text-align:left;direction:ltr">a<h2 style=";text-align:left;direction:ltr"> Ind <h2 style=";text-align:left;direction:ltr"> RL <h2 style=";text-align:left;direction:ltr"> =0.3a<h2 style=";text-align:left;direction:ltr"> Mean <h2 style=";text-align:left;direction:ltr"> RL <h2 style=";text-align:left;direction:ltr"> +0.3a<h2 style=";text-align:left;direction:ltr"> Med <h2 style=";text-align:left;direction:ltr"> RL <h2 style=";text-align:left;direction:ltr"> +0.4a<h2 style=";text-align:left;direction:ltr"> Std <h2 style=";text-align:left;direction:ltr"> RL <h2 style=";text-align:left;direction:ltr"> ; Among them, a Mean LL and a Mean RL respectively represent the average values ​​of the accelerations in the direction of gravity of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Med LL and a Med RL respectively represent the median of the acceleration in the gravity direction of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Std LL and a Std RL represents the standard deviation of the acceleration in the gravity direction of all the vehicles passing through the outgoing sub-section and the incoming sub-section of the road section to be monitored in the first time period, a Ind LL and a Ind RL They are respectively represented as the first characteristic parameters of the outgoing sub-segment and the incoming sub-segment in each unit time period in the first time period.

5. The method for identifying and warning highway slope collapse according to claim 2, characterized in that: The first difference degree in each first time period is calculated by using the first characteristic parameter, which is expressed as: γ=(a Ind LL -a Ind RL ) / a Ind LL ; Wherein, γ represents the first difference degree in the first time period, a Ind LL and a Ind RL They are respectively represented as the first characteristic parameters of the outgoing sub-segment and the incoming sub-segment in the first time period.

6. The method for identifying and warning highway slope collapse according to claim 1, characterized in that: When the second difference is not within the first range, the warning section is determined to be the sub-section to be monitored.

7. The method for identifying and warning of highway slope collapse according to claim 1, characterized in that: The calculating, according to the first motion data, a second difference between the outgoing sub-segment and the incoming sub-segment of the warning section includes: A second characteristic parameter of the warning section is calculated according to the first motion data, and the second difference of the warning section is calculated according to the second characteristic parameter.

8. The method for identifying and warning of highway slope collapse according to claim 7, characterized in that: The method comprises: For the risky road section, extract the second motion data of the risky road section within the first unit time period after being designated as the sub-road section to be monitored, calculate the third characteristic parameter of the risky road section according to the second motion data, and use the third characteristic parameter as the indicator parameter; The second characteristic parameter of the outgoing sub-section and the second characteristic parameter of the incoming sub-section are respectively compared with the index parameter, and the outgoing sub-section or the incoming sub-section in the risk section is determined as a specific risk section according to the comparison result.

9. The method for identifying and warning of highway slope collapse according to claim 1, characterized in that: The motion data includes the position coordinates, vehicle speed, and acceleration in the direction of gravity of the vehicle.

10. The method for identifying and warning highway slope collapse according to claim 1, characterized in that: The method comprises: The risky road section is monitored in real time to obtain third motion data, a fourth characteristic parameter of the risky road section is calculated based on the third motion data, a difference rate between the fourth characteristic parameter of the outgoing sub-section and the fourth characteristic parameter of the incoming sub-section on the risky road is calculated, and a warning signal is issued based on the difference rate.

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