A method and system for rapid detection of a road surface cavity formation area

Abstract

The application discloses a kind of quick detection method and system of road surface cavity formation area, it is related to the technical field of intelligent detection, including the following steps, to be detected area is screened, obtain first area, segmentation obtains key area and associated area, first result and second result are detected, first cavity degree and second cavity degree are obtained by first analysis, and first mapping relationship is constructed.The application is screened by natural cause, and the area to be detected is reduced to the first area, the amount of calculation is reduced, the change from finding a needle in a haystack to catching fish in a pond is realized, the first area is divided into key area and associated area by introducing time attenuation factor, the detection resource allocation efficiency is improved, a ternary decision system of geology, engineering and big data is constructed, and its core value lies in that invisible underground risk is converted into calculable spatial probability problem, which accords with the intelligent evolution direction of traffic infrastructure.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent detection, and in particular to a rapid detection method and system for a road surface cavity formation area. BACKGROUND

[0002] In recent years, the rapid detection technology for road surface cavity formation areas has formed a development pattern of vehicle-mounted platforms, multi-sensor fusion, and AI intelligent recognition. The core is to realize high-resolution, three-dimensional imaging, and real-time early warning of hidden diseases such as cavities, voids, and loose bodies without interrupting traffic and reducing vehicle speed.

[0003] At present, in the Chinese invention patent with the publication number CN115390033A, a method, system, and device for detecting road surface cavities and water damage based on ground penetrating radar are disclosed. The method performs ground penetrating radar indoor inversion tests on road internal cavities and water damage based on existing data to obtain ground penetrating radar test data. Based on the ground penetrating radar simulation data and the ground penetrating radar test data, a ground penetrating radar spectrum database is established. The ground penetrating radar spectrum database is analyzed to extract corresponding spectrum feature parameters. The attenuation characteristics of the electromagnetic waves of the ground penetrating radar in the damage area are analyzed based on the spectrum feature parameters. The attenuation characteristics are substituted into the YOLO machine learning algorithm to identify road internal cavities and water damage. However, in related technologies, there is no rapid conversion and judgment of the relevant area according to the diffusion law of the cavity, which is not conducive to the rapidity of detection. There is no screening of possible cavity areas according to the natural properties of the area before analysis, which can easily cause redundant analysis and is not conducive to the simplicity of detection. SUMMARY

[0004] The technical problem solved by the present application is that in related technologies, there is no rapid conversion and judgment of the relevant area according to the diffusion law of the cavity, which is not conducive to the rapidity of detection. There is no screening of possible cavity areas according to the natural properties of the area before analysis, which can easily cause redundant analysis and is not conducive to the simplicity of detection.

[0005] To solve the above technical problems, the present application provides the following technical solutions: in a first aspect, a rapid detection method for a road surface cavity formation area includes the following steps:

[0006] Step S100: screening road surface cavity natural causes according to natural data of a to-be-detected area, screening the to-be-detected area according to the screened cavity natural causes, and obtaining a first area;

[0007] Step S200: obtaining historical trend data, segmenting the first area according to the historical trend data, and obtaining a key area and a related area;

[0008] Step S300, respectively setting the detection parameters of the key area and the associated area, obtaining the first result and the second result according to the corresponding detection parameters, performing the first analysis on the first result and the second result, obtaining the first cavity degree and the second cavity degree, and constructing the first mapping relationship.

[0009] As a preferred scheme of the method for quickly detecting the road cavity forming area, the natural data includes the underground river distribution position, the soluble rock layer distribution position, the rainstorm duration and the soil type, and the rainstorm duration is represented as the total annual average rainstorm amount of the area to be analyzed.

[0010] The natural causes of the cavity include karst collapse, collapsible loess and rainstorm infiltration.

[0011] As a preferred scheme of the method for quickly detecting the road cavity forming area, the method for screening the natural causes of the road cavity according to the natural data of the area to be detected includes:

[0012] It is judged whether the underground river distribution position and the soluble rock layer distribution position intersect, if intersecting, the intersecting position is first marked, if not intersecting, the karst collapse is deleted, when there is the first mark, the karst collapse is retained, and when there is no first mark, the karst collapse is deleted.

[0013] It is judged whether the soil type of any soil position near the underground river distribution position is loess, if it is loess, the corresponding soil position is second marked, if it is not loess, the next soil position is jumped to until all soil positions are traversed, the second mark is completed, when there is the second mark, the collapsible loess is retained, and when there is no second mark, the collapsible loess is deleted.

[0014] The first duration is set as the duration threshold, the rainstorm duration is compared with the first duration, when the rainstorm duration is greater than or equal to the first duration, the rainstorm infiltration is retained, and when the rainstorm duration is less than the first duration, the rainstorm infiltration is deleted.

[0015] The retained natural causes of the cavity are set as the screened natural causes of the cavity.

[0016] As a preferred scheme of the road surface cavity forming area rapid detection method, the roadbed distribution position of the area to be detected is obtained, any roadbed is selected, the three-dimensional model of the roadbed is obtained, the height of each position of the roadbed is obtained according to the three-dimensional model, the first average value of the height is calculated, the roadbed plane is divided into various shapes of rectangles, when the divided area is not enough, the divided area is filled into a rectangle, the second average value of the height of the roadbed in each rectangle is calculated, the second average value and the first average value are compared, when the second average value is less than the first average value, the corresponding rectangular position is marked thirdly, when the second average value is greater than or equal to the first average value, the next rectangular position is jumped to, until all the rectangular positions are traversed, and each third mark is completed.

[0017] As a preferred scheme of the road surface cavity forming area rapid detection method, when the third mark is completed, a first area is set, and the setting method of the first area comprises:

[0018] The positions corresponding to the first mark, the second mark and the third mark in the area to be detected are obtained.

[0019] The positions corresponding to the first mark, the second mark and the third mark are set as the first area.

[0020] As a preferred scheme of the road surface cavity forming area rapid detection method, the historical trend data comprises a historical karst collapse area, a historical collapsible loess area and a historical rainstorm infiltration area.

[0021] The historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area are represented as the areas of the cavity areas in different areas to be analyzed, and the areas are recorded in a first recording period to form a data sequence.

[0022] As a preferred scheme of the road surface cavity forming area rapid detection method, the first change rate, the second change rate and the third change rate corresponding to the historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area are calculated.

[0023] The calculation methods of the first change rate, the second change rate and the third change rate are the same, comprising:

[0024] According to the time sequence, the adjacent historical trend data are obtained, the first difference value between the adjacent historical trends is calculated, the first ratio of the first difference value to the previous historical trend data in the adjacent historical trend data is calculated, each first ratio is traversed, the second average value of the first ratio is calculated, and the second average value is set as the change rate.

[0025] The second time length is set as a diffusion time length, positions corresponding to the first markers, the second markers and the third markers are obtained, an aggregate area of the positions of the first markers is counted, the aggregate area is represented as a distance between positions corresponding to the first markers being less than a first value, an aggregate area of the positions of each first marker is counted and recorded as a first area, an aggregate area of the positions of the second markers is counted, the aggregate area is represented as a distance between positions corresponding to the second markers being less than the first value, an aggregate area of the positions of each first marker is counted and recorded as a second area, an aggregate area of the positions of the third markers is counted, the aggregate area is represented as a distance between positions corresponding to the third markers being less than the first value, and an aggregate area of the positions of each third marker is counted and recorded as a third area.

[0026] As a preferred scheme of the method for quickly detecting a road cavity formation area, a first product of the first area and a first change rate, a second product of the second area and a second change rate, and a third product of the third area and a third change rate are calculated, a first circle, a second circle and a third circle are drawn with the geometric centers of the first area, the second area and the third area as the origins and the first product, the second product and the third product as the circle areas, and fourth area, fifth area and sixth area are obtained by removing the first area, the second area and the third area from the first circle, the second circle and the third circle.

[0027] The first area, the second area and the third area are set as key areas, and the fourth area, the fifth area and the sixth area are set as associated areas.

[0028] As a preferred scheme of the method for quickly detecting a road cavity formation area, first detection parameters of the key areas and second detection parameters of the associated areas are respectively set, first results and second results are analyzed according to the first detection parameters and the second detection parameters to obtain first cavity degrees and second cavity degrees, and a first mapping relationship is constructed.

[0029] The first detection parameters and the second detection parameters are of the same type and include sound wave amplitudes and sound wave directions, wherein the sound wave directions are perpendicular to the key areas or the associated areas.

[0030] The sound wave amplitudes of the first detection parameters are set as a second value, and the sound wave amplitudes of the second detection parameters are set as a third value, wherein the second value is less than the third value, and the second value and the third value are obtained according to historical experience.

[0031] The first results and the second results are represented as sound wave return amplitudes.

[0032] The first cavity degrees and the second cavity degrees are calculated in the same way, including:

[0033] The fourth value and the fifth value are set as the amplitude threshold of the key area, and the sixth value and the seventh value are set as the amplitude threshold of the key area, wherein the fourth value, the fifth value, the sixth value and the seventh value are in ascending order, the first cavity degree is obtained according to the sound wave return amplitude of the key area and the amplitude threshold of the key area, and the second cavity degree is obtained according to the sound wave return amplitude of the associated area and the amplitude threshold of the associated area.

[0034] The setting method of the first cavity degree and the second cavity degree comprises:

[0035] The sound wave return amplitude of the key area is compared with the amplitude threshold of the key area, when the sound wave return amplitude of the key area is less than or equal to the fourth value, the first cavity degree is set to 1, when the sound wave return amplitude of the key area is greater than the fourth value and less than or equal to the fifth value, the first cavity degree is set to 0.8, and when the sound wave return amplitude of the key area is greater than the fifth value, the first cavity degree is set to 0.5.

[0036] The sound wave return amplitude of the associated area is compared with the amplitude threshold of the associated area, when the sound wave return amplitude of the associated area is less than or equal to the sixth value, the second cavity degree is set to 0.3, when the sound wave return amplitude of the associated area is greater than the sixth value and less than or equal to the seventh value, the second cavity degree is set to 0.2, and when the sound wave return amplitude of the associated area is greater than the seventh value, the second cavity degree is set to 0.1.

[0037] The method for constructing the first mapping relationship comprises:

[0038] The first cavity degree of any key area and the second cavity degree of the corresponding associated area are obtained, the third ratio of the first cavity degree to the second cavity degree is calculated, each third ratio is traversed, the third average value of the third ratio is calculated, and the third average value is set as the conversion parameter of the first cavity degree and the second cavity degree.

[0039] The first mapping relationship of the key area position, the conversion parameter, the associated area position and the first cavity degree is constructed, the position of the associated area and the conversion parameter are obtained by inputting the position of the key area and the corresponding first cavity degree into the first mapping relationship, and the second cavity degree of the associated area is obtained by calculating the fourth ratio of the first cavity degree to the conversion parameter.

[0040] The second aspect relates to a rapid detection system for a road cavity forming area, comprising a screening module, a segmentation module and a conversion module.

[0041] The screening module screens the natural causes of the road cavity according to the natural data of the to-be-detected area, screens the to-be-detected area according to the screened natural causes of the cavity, and obtains a first area.

[0042] The segmentation module obtains historical trend data, segments the first region according to the historical trend data, and obtains a key region and a related region;

[0043] The transformation module sets detection parameters of the key region and the related region respectively, obtains first results and second results according to the corresponding detection parameters, performs first analysis on the first results and the second results, obtains first cavity degrees and second cavity degrees, and constructs a first mapping relationship.

[0044] The beneficial effects of the present application are: through natural cause screening, the detection area is reduced to the first region, the calculation amount is reduced, the transformation from finding a needle in a sea to catching a fish in a pond is realized, the time decay factor is introduced, the first region is divided into the key region and the related region, the detection resource allocation efficiency is improved, and a ternary decision system of geology, engineering and big data is constructed. The core value lies in converting invisible underground risks into calculable spatial probability problems, which conforms to the intelligent evolution direction of traffic infrastructure. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 A basic flowchart of a rapid detection method of a road surface cavity forming area is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0046] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments.

[0047] Embodiments, refer to Figure 1 For an embodiment of the present application, a rapid detection method of a road surface cavity forming area is provided, which includes the following steps:

[0048] Step S100: screening road surface cavity natural causes according to natural data of a detection area, and screening the detection area according to the screened cavity natural causes to obtain a first region;

[0049] Step S200: obtaining historical trend data, segmenting the first region according to the historical trend data to obtain a key region and a related region;

[0050] Step S300: setting detection parameters of the key region and the related region respectively, obtaining first results and second results according to the corresponding detection parameters, performing first analysis on the first results and the second results, obtaining first cavity degrees and second cavity degrees, and constructing a first mapping relationship.

[0051] The application realizes the transformation from finding a needle in the sea to catching fish in a pond by natural cause screening, reduces the calculation amount, divides the first area into a key area and a related area by introducing a time decay factor, improves the detection resource allocation efficiency, and constructs a ternary decision system of geology, engineering and big data, the core value of which is to convert invisible underground risks into calculable spatial probability problems, which conforms to the intelligent evolution direction of traffic infrastructure.

[0052] The natural data includes the distribution positions of underground rivers, soluble rock layers, rainstorm duration and soil types, and the rainstorm duration is represented as the total annual average rainstorm amount of the area to be analyzed.

[0053] The cavity natural causes include karst collapse, collapsible loess and rainstorm infiltration.

[0054] The method for screening the road cavity natural causes according to the natural data of the area to be detected comprises the following steps:

[0055] It is judged whether the distribution positions of the underground rivers and the soluble rock layers intersect, if they intersect, the intersecting positions are marked first, if they do not intersect, the karst collapse is deleted, when there is the first mark, the karst collapse is retained, and when there is no first mark, the karst collapse is deleted;

[0056] It is judged whether the soil type of any soil position near the distribution position of the underground river is loess, if it is loess, the corresponding soil position is marked second, if it is not loess, the next soil position is jumped to until all soil positions are traversed and the second mark is completed, when there is the second mark, the collapsible loess is retained, and when there is no second mark, the collapsible loess is deleted;

[0057] The first duration is set as a duration threshold, the rainstorm duration is compared with the first duration, when the rainstorm duration is greater than or equal to the first duration, the rainstorm infiltration is retained, and when the rainstorm duration is less than the first duration, the rainstorm infiltration is deleted;

[0058] The retained cavity natural causes are set as the screened cavity natural causes.

[0059] In specific implementation, through the underground river-soluble rock layer cross verification (first mark), in the Guizhou-Guangxi test area (area 1200 km²), the original karst risk area: 258 places → after cross verification, 21 places are reserved (screening rate 91.8%), and the false deletion rate is only 0.7% (verified by drilling, only 2 places in the non-crossing area have dissolution cracks but do not form cavities), based on the coupling of soil type-river distance (second mark), in the Longdong Loess Plateau (test section 80 km), the traditional method (divided according to the thickness of loess): mark 36 km of collapsible area → the method (within the influence radius of 50 m of the underground river): mark 8.7 km of collapsible area (compression 75.8%), field immersion test shows that the non-marked area has a collapsibility coefficient δs<0.015 (the specification can be ignored threshold), set the first time length = 6 hours (based on 50 years of meteorological-collapsing event regression analysis), in the test area rainstorm event (2015-2023), the rainstorm lasting ≥6h triggers the collapse probability of 87%, <6h only triggers 9% (p<0.01), in the Guangxi pilot, the area with an annual average subsidence rate >5mm / year after cross verification and superposition of InSAR, the screening rate is further improved to 96.1%, the spatial positioning error of the cavity risk area is compressed from kilometer level to hundred meter level, and the economic cost is reduced by one order of magnitude.

[0060] Obtain the roadbed distribution position of the to-be-detected area, select any roadbed, obtain a three-dimensional model of the roadbed, obtain the height of each position of the roadbed according to the three-dimensional model, calculate a first average value of the height, divide the roadbed plane into various rectangular shapes of the same shape, when the divided area is insufficient, fill the divided area into a rectangle, calculate a second average value of the height of the roadbed in each rectangle, compare the second average value with the first average value, when the second average value is less than the first average value, mark the corresponding rectangular position, when the second average value is greater than or equal to the first average value, jump to the next rectangular position, until all the rectangular positions are traversed, and each third mark is completed.

[0061] In specific implementation, the unmanned aerial vehicle oblique photography (1 cm ground resolution) + LiDAR (250 pts / m²), 1 m x 1 m square (a total of 20,000 grids), the first average value is 24.381 m (statistical value of full laser point cloud), 137 grids are marked (accounting for 0.69%), which are concentrated on the right side of K112+420-K112+480 shoulder, verified by excavation, the average void thickness of the marked area is 18 mm (maximum 31 mm), and the maximum differential settlement of the unmarked area is only 2 mm, compared with the total station re-measurement, the root mean square error RMSE=1.4 mm.

[0062] When the third mark is completed, a first area is set, and the setting method of the first area includes:

[0063] Obtain the positions corresponding to the first mark, the second mark and the third mark in the to-be-detected area.

[0064] The positions corresponding to the first mark, the second mark and the third mark are set as the first region.

[0065] The historical trend data includes historical karst collapse area, historical collapsible loess area and historical rainstorm infiltration area.

[0066] The historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area are represented as the areas of the hollow regions in different to-be-analyzed regions, and the areas are recorded with the first record period to form a data sequence.

[0067] The first change rate, the second change rate and the third change rate corresponding to the historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area are calculated.

[0068] The calculation methods of the first change rate, the second change rate and the third change rate are the same, including:

[0069] According to the time sequence, the adjacent historical trend data are obtained, the first difference between the adjacent historical trends is calculated, the first ratio of the first difference to the previous historical trend data in the adjacent historical trend data is calculated, each first ratio is traversed, a second average value of the first ratios is calculated, and the second average value is set as the change rate.

[0070] The second time length is set as the diffusion time length, the positions corresponding to the first mark, the second mark and the third mark are obtained, the aggregate area of the positions of the first mark is counted, the aggregate area is represented as the distance between the positions corresponding to the first mark being less than a first value, the aggregate area of each position of the first mark is traversed and recorded as a first area, the aggregate area of the positions of the second mark is counted, the aggregate area is represented as the distance between the positions corresponding to the second mark being less than the first value, the aggregate area of each position of the first mark is traversed and recorded as a second area, the aggregate area of the positions of the third mark is counted, the aggregate area is represented as the distance between the positions corresponding to the third mark being less than the first value, and the aggregate area of each position of the third mark is traversed and recorded as a third area.

[0071] In a specific implementation, a 36-month sequence from 2021 to 2023, an original area sequence A0…A 35 : determined by InSAR deformation and manual verification every month, the minimum patch is 0.3 ha, and the change rate is calculated: the first difference ΔA i = A i – A i-1 , the first ratio r i = ΔA i / A i-1, the second average value (i.e. the first change rate) R1=8.7% / month, the diffusion duration (the second duration) is 90d (an empirical value corresponding to the average incubation period of karst collapse), the first value (aggregated distance d1)=100m (based on the average influence radius of underground river), and the first area (karst aggregated patch) is obtained from the data in July 2023: after aggregation, there are 14 patches with a total area of 41.2ha, an increase of 3.8ha compared with the previous month, with an error of only 2.6% compared with the R1 estimated value of 3.9ha. Through the double-factor model of quantifying trends by change rate and quantifying spatial impact by aggregated area, the traditional static risk assessment is upgraded to dynamic spatio-temporal early warning.

[0072] A first product of the first area and the first change rate, a second product of the second area and the second change rate, and a third product of the third area and the third change rate are calculated, and a first circle, a second circle and a third circle are drawn with the geometric centers of the first area, the second area and the third area as the origins and the first product, the second product and the third product as the circle areas, and the first area, the second area and the third area are removed from the first circle, the second circle and the third circle to obtain a fourth area, a fifth area and a sixth area;

[0073] The first area, the second area and the third area are set as key areas, and the fourth area, the fifth area and the sixth area are set as associated areas.

[0074] In specific implementation, the first area (karst collapse patch)=12.4ha; the first change rate R1=8.7% / month, the first product S1=12.4x8.7=108ha, the first circle radius r1=√(S1 / π)=586m, and the fourth area (associated area)=108-12.4=95.6ha. Twelve groups of geological radar are arranged within the 586m ring, among which 11 groups find hidden fissures (true correlation rate 92%), low-intensity grouting (0.3MPa) is implemented on the fourth area (karst associated area), the material consumption is only 38% of that of the traditional scheme, and the single-kilometer cost is reduced from 95,000 yuan to 33,000 yuan; after 6 months, the settlement rate is reduced from 12mm / month to 2mm / month, avoiding a traffic interruption accident (direct loss estimated value 3.5 million yuan), the hit rate of the key area is 93%, and the false positive rate of the associated area is less than 10%.

[0075] First detection parameters of the key area and second detection parameters of the associated area are respectively set, and first analysis is performed on the first result and the second result according to the first detection parameters and the second detection parameters to obtain a first cavity degree and a second cavity degree, and a first mapping relationship is constructed;

[0076] The first detection parameters and the second detection parameters are of the same type, including sound wave amplitude and sound wave direction, wherein the sound wave direction is perpendicular to the key area or the associated area;

[0077] The sound wave amplitude of the first detection parameter is set to a second value, and the sound wave amplitude of the second detection parameter is set to a third value, wherein the second value is less than the third value, and the second value and the third value are obtained according to historical experience;

[0078] The first result and the second result are both represented as sound wave return amplitudes;

[0079] The first cavity degree and the second cavity degree are calculated in the same way, including:

[0080] The sound wave return amplitude is obtained, the fourth value and the fifth value are set as the amplitude threshold of the key area, the sixth value and the seventh value are set as the amplitude threshold of the key area, wherein the fourth value, the fifth value, the sixth value and the seventh value are in ascending order, the first cavity degree is obtained according to the sound wave return amplitude of the key area and the amplitude threshold of the key area, and the second cavity degree is obtained according to the sound wave return amplitude of the associated area and the amplitude threshold of the associated area;

[0081] The setting method of the first cavity degree and the second cavity degree includes:

[0082] The sound wave return amplitude of the key area is compared with the amplitude threshold of the key area, when the sound wave return amplitude of the key area is less than or equal to the fourth value, the first cavity degree is set to 1, when the sound wave return amplitude of the key area is greater than the fourth value and less than or equal to the fifth value, the first cavity degree is set to 0.8, and when the sound wave return amplitude of the key area is greater than the fifth value, the first cavity degree is set to 0.5;

[0083] The sound wave return amplitude of the associated area is compared with the amplitude threshold of the associated area, when the sound wave return amplitude of the associated area is less than or equal to the sixth value, the second cavity degree is set to 0.3, when the sound wave return amplitude of the associated area is greater than the sixth value and less than or equal to the seventh value, the second cavity degree is set to 0.2, and when the sound wave return amplitude of the associated area is greater than the seventh value, the second cavity degree is set to 0.1;

[0084] The method for constructing the first mapping relationship includes:

[0085] The first cavity degree of any key area and the second cavity degree of the corresponding associated area are obtained, the third ratio of the first cavity degree to the second cavity degree is calculated, each third ratio is traversed, the third average value of the third ratio is calculated, and the third average value is set as the conversion parameter of the first cavity degree and the second cavity degree;

[0086] The first mapping relationship of the key area position, the conversion parameter, the associated area position and the first cavity degree is constructed, the position of the key area and the corresponding first cavity degree are input into the first mapping relationship, the position of the associated area and the conversion parameter are obtained, the fourth ratio of the first cavity degree and the conversion parameter is calculated, and the second cavity degree of the associated area is obtained.

[0087] In the implementation, the double-threshold acoustic wave grading digitizes the cavity risk, the acoustic wave amplitude: the second value = 65 mV (5% quantile of 432 samples of the historical 5 years), the acoustic wave direction: vertically downward (± 5°), the acoustic wave amplitude: the third value = 110 mV (25% quantile of the historical sample), the key area: the fourth value 40 mV, the fifth value 65 mV, the associated area: the sixth value 75 mV, the seventh value 110 mV, the field calibration: verification on 12 known cavities (0.2-1.3 m of empty thickness), the linear correlation coefficient R of the acoustic wave return amplitude and the empty thickness = 0.93, the third ratio mean value (conversion parameter) = 0.29 of the 214 group of 'key-association' paired data in the pilot area, the standard deviation 0.04, the coefficient of variation 14%, and the stability is good. 2

[0088] The application reduces the calculation amount by natural cause screening, changes from finding a needle in a haystack to catching fish in a pond, introduces a time decay factor, divides the first area into a key area and an associated area, improves the detection resource allocation efficiency, and constructs a ternary decision system of geology, engineering and big data, the core value of which is to convert invisible underground risks into calculable spatial probability problems, which conforms to the intelligent evolution direction of traffic infrastructure.

[0089] ​Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (or computer- readable storage media) having computer-usable program code embodied in the medium. The medium may Figure 1 one or more functions specified in the flow or flows and / or blocks Figure 1 one or more functions specified in the flow or flows and / or blocks

[0090] It should be noted that the above-mentioned embodiments are only used to illustrate but not to limit the technical solutions of the present application. Although the present application is described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application, and they should be covered in the scope of the claims of the present application.

Claims

1. A method for rapidly detecting a road surface cavity formation area, characterized by, The method comprises the following steps: Step S100, screening road cavity natural causes according to natural data of a region to be detected, screening the region to be detected according to the screened cavity natural causes, and obtaining a first region, wherein the natural data comprises underground river distribution position, soluble rock stratum distribution position, rainstorm duration and soil type, the rainstorm duration is represented as the total amount of annual average rainstorm of the region to be analyzed, and the cavity natural causes comprise karst collapse, collapsible loess and rainstorm infiltration; Step S200, obtaining historical trend data, segmenting the first region according to the historical trend data, and obtaining a key region and a related region, wherein the historical trend data comprises historical karst collapse area, historical collapsible loess area and historical rainstorm infiltration area; calculating a first change rate, a second change rate and a third change rate corresponding to the historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area; The calculation methods of the first change rate, the second change rate and the third change rate are the same, comprising: obtaining adjacent historical trend data in time sequence, calculating a first difference value between adjacent historical trends, calculating a first ratio of the first difference value to a previous historical trend data in the adjacent historical trend data, traversing each first ratio, calculating a second average value of the first ratio, and setting the second average value as the change rate; setting a second duration as a diffusion duration, obtaining positions corresponding to the first mark, the second mark and the third mark, counting aggregated areas of positions of the first mark, the aggregated areas being represented as distances between positions corresponding to the first mark being less than a first value, traversing aggregated areas of positions of each first mark, denoted as a first area, counting aggregated areas of positions of the second mark, the aggregated areas being represented as distances between positions corresponding to the second mark being less than the first value, traversing aggregated areas of positions of each first mark, denoted as a second area, counting aggregated areas of positions of the third mark, the aggregated areas being represented as distances between positions corresponding to the third mark being less than the first value, traversing aggregated areas of positions of each third mark, denoted as a third area; calculating a first product of the first area and the first change rate, a second product of the second area and the second change rate, and a third product of the third area and the third change rate, taking geometric centers of the first area, the second area and the third area as origins, and taking the first product, the second product and the third product as circle areas, drawing a first circle, a second circle and a third circle, and removing the first area, the second area and the third area from the first circle, the second circle and the third circle to obtain a fourth area, a fifth area and a sixth area; setting the first area, the second area and the third area as the key region, and setting the fourth area, the fifth area and the sixth area as the related region; Step S300, respectively set the detection parameters of the key area and the associated area, obtain the first result and the second result according to the corresponding detection parameters, perform the first analysis on the first result and the second result, obtain the first cavity degree and the second cavity degree, obtain the first cavity degree according to the sound wave return amplitude of the key area and the amplitude threshold value of the key area, obtain the second cavity degree according to the sound wave return amplitude of the associated area and the amplitude threshold value of the associated area, and construct the first mapping relationship.

2. The method of claim 1, wherein the method comprises: The method for screening the natural causes of the road cavity according to the natural data of the to-be-detected area comprises the following steps: ​ determine whether the distribution positions of the underground river and the soluble rock layer intersect, if yes, mark the intersection position, if not, delete the karst collapse, when there is the first mark, keep the karst collapse, and when there is no first mark, delete the karst collapse; determine whether the soil type of any soil position near the distribution position of the underground river is loess, if yes, mark the corresponding soil position, if not, jump to the next soil position until all the soil positions are traversed and the second mark is completed, when there is the second mark, keep the collapsible loess, and when there is no second mark, delete the collapsible loess; set the first duration as the duration threshold value, compare the duration of the rainstorm with the first duration, when the duration of the rainstorm is greater than or equal to the first duration, keep the rainstorm infiltration, and when the duration of the rainstorm is less than the first duration, delete the rainstorm infiltration; set the retained natural causes of the cavity as the screened natural causes of the cavity.

3. The method for rapid detection of road cavity formation areas according to claim 1, characterized in that: obtain the distribution position of the roadbed in the to-be-detected area, select any roadbed, obtain the three-dimensional model of the roadbed, obtain the height of each position of the roadbed according to the three-dimensional model, calculate the first average value of the height, divide the roadbed plane into rectangles of the same shape, when the divided area is not enough, fill the divided area with rectangles, calculate the second average value of the height of the roadbed in each rectangle, compare the second average value with the first average value, when the second average value is less than the first average value, mark the corresponding rectangular position with the third mark, and when the second average value is greater than or equal to the first average value, jump to the next rectangular position until all the rectangular positions are traversed and all the third marks are completed.

4. The method of claim 3, wherein the step of determining the location of the cavity formation area is performed by using a neural network. After the third mark is completed, set the first area, and the setting method of the first area comprises: obtain the positions corresponding to the first mark, the second mark and the third mark in the to-be-detected area; set the positions corresponding to the first mark, the second mark and the third mark as the first area.

5. The method for rapid detection of road cavity formation areas according to claim 1, characterized in that: The historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area represent the areas of the cavity areas in different to-be-analyzed areas, and the areas are recorded with the first recording period to form a data sequence.

6. The method of claim 1, wherein: respectively set the first detection parameter of the key area and the second detection parameter of the associated area, obtain the first result and the second result according to the first detection parameter and the second detection parameter, perform the first analysis on the first result and the second result, obtain the first cavity degree and the second cavity degree, and construct the first mapping relationship; The first detection parameter and the second detection parameter are of the same type, including a sound wave amplitude and a sound wave direction, wherein the sound wave direction is perpendicular to the key area or the associated area; The sound wave amplitude of the first detection parameter is set as a second value, and the sound wave amplitude of the second detection parameter is set as a third value, wherein the second value is less than the third value, and the second value and the third value are obtained according to historical experience; The first result and the second result are both represented as a sound wave return amplitude; The first cavity degree and the second cavity degree are calculated in the same way, including: obtaining the sound wave return amplitude, setting a fourth value and a fifth value as the amplitude threshold of the key area, and setting a sixth value and a seventh value as the amplitude threshold of the key area, wherein the fourth value, the fifth value, the sixth value and the seventh value are in ascending order; The setting method of the first cavity degree and the second cavity degree includes: comparing the sound wave return amplitude of the key area with the amplitude threshold of the key area, when the sound wave return amplitude of the key area is less than or equal to the fourth value, setting the first cavity degree as 1, when the sound wave return amplitude of the key area is greater than the fourth value and less than or equal to the fifth value, setting the first cavity degree as 0.8, and when the sound wave return amplitude of the key area is greater than the fifth value, setting the first cavity degree as 0.5; comparing the sound wave return amplitude of the associated area with the amplitude threshold of the associated area, when the sound wave return amplitude of the associated area is less than or equal to the sixth value, setting the second cavity degree as 0.3, when the sound wave return amplitude of the associated area is greater than the sixth value and less than or equal to the seventh value, setting the second cavity degree as 0.2, and when the sound wave return amplitude of the associated area is greater than the seventh value, setting the second cavity degree as 0.1; The method for constructing the first mapping relationship includes: obtaining the first cavity degree of any key area and the second cavity degree of the corresponding associated area, calculating a third ratio of the first cavity degree to the second cavity degree, traversing each third ratio, calculating a third average value of the third ratio, and setting the third average value as a conversion parameter of the first cavity degree and the second cavity degree; constructing a first mapping relationship of the key area position, the conversion parameter, the associated area position and the first cavity degree, inputting the position of the key area and the corresponding first cavity degree into the first mapping relationship to obtain the position of the associated area and the conversion parameter, and calculating a fourth ratio of the first cavity degree to the conversion parameter to obtain the second cavity degree of the associated area.

7. A system for rapidly detecting a road cavity formation area, the system being used to perform the method for rapidly detecting a road cavity formation area according to claim 1, characterized by, including a screening module, a segmentation module and a conversion module; The screening module screens the natural causes of the road cavity according to the natural data of the to-be-detected area, screens the to-be-detected area according to the screened natural causes of the cavity, and obtains a first region; The segmentation module obtains historical trend data, segments the first region according to the historical trend data, and obtains a key area and an associated area; The transformation module sets detection parameters of a key area and a related area respectively, obtains a first result and a second result according to the corresponding detection parameters, performs a first analysis on the first result and the second result, obtains a first cavity degree and a second cavity degree, and constructs a first mapping relationship.

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