Rapid detection method and system for pavement cavity forming area

Abstract

The invention discloses a rapid detection method and system for a pavement cavity forming area, and relates to the technical field of intelligent detection, and the method comprises the following steps: screening a to-be-detected area to obtain a first area, carrying out the segmentation to obtain a key area and an associated area, and carrying out the detection to obtain a first result and a second result, the first analysis obtains a first void degree and a second void degree, and constructs a first mapping relationship. According to the method, the to-be-detected area is reduced to the first area through natural cause screening, the calculated amount is reduced, conversion from fishing of a large sea needle to fishing of a pond is realized, a time attenuation factor is introduced, the first area is divided into a key area and an associated area, the detection resource allocation efficiency is improved, and a ternary decision system of geology, engineering and big data is constructed; the core value of the method lies in that invisible underground risks are converted into computable space probability, and the method accords with the intelligent evolution direction of traffic infrastructure.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent detection, and in particular to a method and system for quickly detecting road cavity formation areas. Background Art

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

[0003] Currently, a Chinese invention patent with publication number CN115390033A discloses a method, system, and device for detecting cavities and water damage under pavement using ground-penetrating radar. The method uses existing data to conduct a ground-penetrating radar indoor inversion test of cavities and water damage inside the pavement to obtain ground-penetrating radar test data; establishes a ground-penetrating radar spectrum database based on the ground-penetrating radar simulation data and the ground-penetrating radar test data; analyzes the ground-penetrating radar spectrum database to extract corresponding spectral characteristic parameters; analyzes the attenuation characteristics of the electromagnetic waves of the ground-penetrating radar in the damaged area based on the spectral characteristic parameters; and substitutes the attenuation characteristics into the YOLO machine learning algorithm to identify cavities and water damage inside the pavement. However, the related art does not quickly transform and judge the relevant areas based on the diffusion law of the cavities, which is not conducive to the rapidity of detection. Possible cavity areas are not screened and then analyzed based on the natural properties of the area, which easily causes redundant analysis and is not conducive to the simplicity of detection. Summary of the Invention

[0004] The technical problem solved by the present invention is that the relevant technologies do not quickly transform and judge the relevant areas according to the diffusion law of the voids, which is not conducive to the rapidity of detection. The possible void areas are not screened and then analyzed according to the natural properties of the area, which easily leads to redundant analysis and is not conducive to the simplicity of detection.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, a method for rapidly detecting road cavity formation areas comprises the following steps: Step S100, screening the natural causes of road cavities based on the natural data of the area to be detected, and screening the area to be detected based on the screened natural causes of cavities to obtain a first area; Step S200, obtaining historical trend data, and segmenting the first region according to the historical trend data to obtain key regions and related regions; Step S300 , respectively setting detection parameters for key areas and associated areas, obtaining a first result and a second result according to the corresponding detection parameters, performing a first analysis on the first result and the second result to obtain a first void degree and a second void degree, and constructing a first mapping relationship.

[0006] As a preferred embodiment of the method for rapid detection of road void formation areas described in the present invention, the natural data includes the distribution locations of underground rivers, the distribution locations of soluble rock layers, the duration of heavy rain, and soil type, wherein the duration of heavy rain is represented by the sum of the average annual heavy rainfall in the area to be analyzed; The natural causes of the cavities include karst collapse, collapsible loess and rainstorm infiltration.

[0007] As a preferred embodiment of the method for rapidly detecting road void formation areas of the present invention, the method for screening the natural causes of road voids based on natural data of the area to be detected includes: Determine whether the underground river distribution position and the soluble rock layer distribution position intersect. If so, mark the intersection position first. If not, delete the karst collapse. When the first mark exists, retain the karst collapse. When the first mark does not exist, delete the karst collapse. Determine whether the soil type of any soil position near the underground river distribution location is loess. If it is loess, the corresponding soil position will be marked as a second mark. If it is not loess, jump to the next soil position until all soil positions are traversed and the second mark is completed. When the second mark exists, the collapsible loess is retained. When the second mark does not exist, the collapsible loess is deleted. The first duration is set as the duration threshold, and 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; when the rainstorm duration is less than the first duration, the rainstorm infiltration is deleted. Sets the retained void origin to the filtered void origin.

[0008] As a preferred solution of a method for rapid detection of pavement void formation areas described in the present invention, the method includes: obtaining the roadbed distribution position of the area to be detected, selecting any roadbed, obtaining a three-dimensional model of the roadbed, obtaining the height of each position of the roadbed according to the three-dimensional model, calculating the first average value of the height, dividing the roadbed plane into rectangles of the same shape, when the divided area is not enough, filling the divided area with rectangles, calculating the second average value of the height of the roadbed in each rectangle, comparing the second average value with the first average value, when the second average value is less than the first average value, performing a third mark on the corresponding rectangular position, and when the second average value is greater than or equal to the first average value, jumping to the next rectangular position until all rectangular positions are traversed and each third mark is completed.

[0009] As a preferred embodiment of the method for rapidly detecting a road void formation area according to the present invention, after the third marking is completed, a first area is set. The method for setting the first area includes: Obtaining positions corresponding to the first mark, the second mark, and the third mark in the area to be detected; The positions corresponding to the first marker, the second marker, and the third marker are set as the first region.

[0010] As a preferred solution of the method for rapid detection of pavement void formation areas of the present invention, the historical trend data includes historical karst collapse area, historical collapsible loess area and historical rainstorm infiltration area; The historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area are expressed as the areas corresponding to the hollow regions in different areas to be analyzed, and the areas are recorded along the first recording period to form a data sequence.

[0011] As a preferred embodiment of the method for rapid detection of pavement void formation areas of the present invention, the method comprises: 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, including: Acquire adjacent historical trend data in chronological order, calculate a first difference between adjacent historical trends, calculate a first ratio of the first difference to a previous historical trend data in the adjacent historical trend data, traverse each first ratio, calculate a second average value of the first ratios, and set the second average value as the change rate; Set the second duration as the diffusion duration, obtain the positions corresponding to the first mark, the second mark, and the third mark, count the aggregated area of ​​the position of the first mark, the aggregated area is expressed as the distance between the positions corresponding to the first mark is less than the first value, traverse the aggregated area of ​​the positions of each first mark, record it as the first area, count the aggregated area of ​​the position of the second mark, the aggregated area is expressed as the distance between the positions corresponding to the second mark is less than the first value, traverse the aggregated area of ​​the positions of each first mark, record it as the second area, count the aggregated area of ​​the position of the third mark, the aggregated area is expressed as the distance between the positions corresponding to the third mark is less than the first value, traverse the aggregated area of ​​the positions of each third mark, record it as the third area.

[0012] As a preferred embodiment of the method for rapidly detecting road void formation areas described in the present invention, the method further comprises: calculating a first product of a first area and a first rate of change, a second product of a second area and a second rate of change, and a third product of a third area and a third rate of change; drawing first, second, and third circles with the geometric centers of the first, second, and third areas as origins and the first, second, and third products as the areas of the circles; and subtracting the first, second, and third areas from the first, second, and third circles to obtain fourth, fifth, and sixth areas. 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.

[0013] As a preferred embodiment of the method for rapid detection of road void formation areas according to the present invention, first detection parameters for key areas and second detection parameters for associated areas are respectively set, a first result and a second result are obtained based on the first detection parameters and the second detection parameters, a first void degree and a second void degree are obtained, and a first mapping relationship is established; The first detection parameter and the second detection parameter are of the same type, including the acoustic wave amplitude and the acoustic wave direction, wherein the acoustic wave directions are both perpendicular to the key area or the associated area; The acoustic wave amplitude of the first detection parameter is set to a second value, and the acoustic wave amplitude of the second detection parameter is set to a third value, wherein the second value is smaller than the third value, and the second value and the third value are obtained based on historical experience; The first result and the second result are both expressed as the amplitude of the acoustic wave return; The calculation method of the first voiding degree and the second voiding degree is the same, including: Obtaining the sound wave return amplitude, setting the fourth value and the fifth value as the amplitude threshold of the key area, and setting the sixth value and the 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, obtaining a first void degree according to the sound wave return amplitude of the key area and the amplitude threshold of the key area, and obtaining a second void degree according to the sound wave return amplitude of the associated area and the amplitude threshold of the associated area; The method for setting the first void degree and the second void degree includes: Compare 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, set the first void degree 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, set the first void degree to 0.8; when the sound wave return amplitude of the key area is greater than the fifth value, set the first void degree to 0.5; Compare the acoustic wave return amplitude of the associated area with the amplitude threshold of the associated area; when the acoustic wave return amplitude of the associated area is less than or equal to the sixth value, set the second void degree to 0.3; when the acoustic wave return amplitude of the associated area is greater than the sixth value and less than or equal to the seventh value, set the second void degree to 0.2; when the acoustic wave return amplitude of the associated area is greater than the seventh value, set the second void degree to 0.1; The method for constructing the first mapping relationship includes: Obtaining a first hollowness degree of any key area and a second hollowness degree of its corresponding associated area, calculating a third ratio of the first hollowness degree to the second hollowness degree, traversing each third ratio, calculating a third average of the third ratios, and setting the third average as a conversion parameter between the first hollowness degree and the second hollowness degree; Construct a first mapping relationship among the position of the key area, the conversion parameter, the position of the associated area and the first degree of voidness; by inputting the position of the key area and the corresponding first degree of voidness into the first mapping relationship, obtain the position and conversion parameter of the associated area; and by calculating the fourth ratio of the first degree of voidness to the conversion parameter, obtain the second degree of voidness of the associated area.

[0014] In a second aspect, a rapid detection system for road void formation areas includes a screening module, a segmentation module, and a conversion module; The screening module screens the natural causes of road cavities based on the natural data of the area to be detected, and screens the area to be detected based on the screened natural causes of cavities to obtain a first area; The segmentation module obtains historical trend data and segments the first region according to the historical trend data to obtain key regions and related regions; The conversion module sets detection parameters for key areas and related areas 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 to obtain a first void degree and a second void degree, and constructs a first mapping relationship.

[0015] The beneficial effects of the present invention are as follows: through natural cause screening, the area to be inspected is narrowed down to the first area, the amount of calculation is reduced, and the transformation from looking for a needle in a haystack to fishing in a pond is realized. The time decay factor is introduced to divide the first area into key areas and related areas, and the efficiency of detection resource allocation is improved. A ternary decision-making system of geology, engineering, and big data is constructed. Its core value lies in converting invisible underground risks into computable spatial probability problems, which is in line with the direction of intelligent evolution of transportation infrastructure. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the basic flow of a method for rapidly detecting road cavity formation areas provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0018] Example, see Figure 1 , as one embodiment of the present invention, provides a method for quickly detecting road cavity formation areas, comprising the following steps: Step S100, screening the natural causes of road cavities based on the natural data of the area to be detected, and screening the area to be detected based on the screened natural causes of cavities to obtain a first area; Step S200, obtaining historical trend data, and segmenting the first region according to the historical trend data to obtain key regions and related regions; Step S300 , respectively setting detection parameters for key areas and associated areas, obtaining a first result and a second result according to the corresponding detection parameters, performing a first analysis on the first result and the second result to obtain a first void degree and a second void degree, and constructing a first mapping relationship.

[0019] The present invention uses natural cause screening to narrow the area to be inspected to the first region, reducing the amount of calculation and achieving the transformation from looking for a needle in a haystack to fishing in a pond. The introduction of the time decay factor divides the first region into key areas and related areas, improving the efficiency of detection resource allocation, and constructing a ternary decision-making system for geology, engineering, and big data. Its core value lies in converting invisible underground risks into calculable spatial probability problems, which is in line with the direction of intelligent evolution of transportation infrastructure.

[0020] Natural data include the distribution of underground rivers, the distribution of soluble rock layers, the duration of heavy rain, and soil types. The duration of heavy rain is expressed as the sum of the average annual heavy rainfall in the area to be analyzed. Natural causes of cavities include karst collapse, collapsible loess and rainstorm infiltration.

[0021] Methods for screening the natural causes of road voids based on natural data from the area to be inspected include: Determine whether the underground river distribution position and the soluble rock layer distribution position intersect. If so, mark the intersection position first. If not, delete the karst collapse. When the first mark exists, retain the karst collapse. When the first mark does not exist, delete the karst collapse. Determine whether the soil type of any soil position near the underground river distribution location is loess. If it is loess, the corresponding soil position will be marked as a second mark. If it is not loess, jump to the next soil position until all soil positions are traversed and the second mark is completed. When the second mark exists, the collapsible loess is retained. When the second mark does not exist, the collapsible loess is deleted. The first duration is set as the duration threshold, and 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; when the rainstorm duration is less than the first duration, the rainstorm infiltration is deleted. Sets the retained void origin to the filtered void origin.

[0022] In the specific implementation, through the cross-validation of underground rivers and soluble rock layers (the first mark), in the Guizhou-Guangxi test area (area of ​​1200km²), the original karst risk areas: 258 → after cross-validation, 21 were retained (screening rate of 91.8%), and the false deletion rate was only 0.7% (verified by drilling, only 2 non-crossing areas had dissolution cracks but no cavities were formed). Based on the coupling of soil type and underground river distance (the second mark), in the Loess Plateau in eastern Gansu (test section of 80km), the traditional method (divided by loess thickness): marked the subsidence area of ​​36km → this method (within the influence radius of the underground river of 50m): marked the subsidence area of ​​8.7km (compressed 75 .8%), and on-site water immersion tests showed that the wetting coefficient δs in the unmarked area was <0.015 (the ignorable threshold in the specification). The first duration was set at 6 hours (based on a 50-year meteorological-collapse event regression analysis). During rainstorm events in the test area (2015-2023), rainstorms lasting ≥6 hours had an 87% probability of triggering collapse, while those <6 hours only had a 9% probability (p<0.01). In the Guangxi pilot project, after cross-validation and superimposition of areas with an InSAR average annual sedimentation rate >5 mm / year, the screening rate was further increased to 96.1%, compressing the spatial positioning error of the cavity risk area from kilometers to hundreds of meters, while reducing the economic cost by an order of magnitude.

[0023] Obtain the roadbed distribution position of the area to be detected, 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 a rectangle, 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 rectangle position for the third time, and when the second average value is greater than or equal to the first average value, jump to the next rectangle position until all rectangle positions are traversed and each third mark is completed.

[0024] In the specific implementation, UAV oblique photography (1cm ground resolution) + LiDAR (250pts / m²), 1m×1m square (a total of 20,000 grids), the first average value is 24.381m (laser point cloud statistics of the entire line), 137 grids are marked (accounting for 0.69%), concentrated on the right shoulder of K112+420–K112+480. After excavation verification, the average void thickness of the marked area is 18mm (maximum 31mm), and the maximum differential settlement of the unmarked area is only 2mm. Compared with the re-measurement of the total station, the root mean square error RMSE=1.4mm.

[0025] After the third marking is completed, the first region is set. The method for setting the first region includes: Obtaining positions corresponding to the first mark, the second mark, and the third mark in the area to be detected; The positions corresponding to the first marker, the second marker, and the third marker are set as the first region.

[0026] Historical trend data include historical karst collapse area, historical collapsible loess area, and historical rainstorm infiltration area; The historical karst collapse area, historical collapsible loess area, and historical rainstorm infiltration area are expressed as the area corresponding to the hollow areas in different areas to be analyzed, and the area is recorded over the first recording period to form a data series.

[0027] Calculate the first, second, and third change rates corresponding to the historical karst collapse area, historical collapsible loess area, and historical rainstorm infiltration area; The calculation methods of the first change rate, the second change rate, and the third change rate are the same, including: Acquire adjacent historical trend data in chronological order, calculate a first difference between adjacent historical trends, calculate a first ratio between the first difference and a previous historical trend data in the adjacent historical trend data, traverse each first ratio, calculate a second average value of the first ratios, and set the second average value as the change rate; Set the second duration as the diffusion duration, obtain the positions corresponding to the first mark, the second mark, and the third mark, count the aggregated area of ​​the position of the first mark, the aggregated area is expressed as the distance between the positions corresponding to the first mark is less than the first value, traverse the aggregated area of ​​the positions of each first mark, record it as the first area, count the aggregated area of ​​the position of the second mark, the aggregated area is expressed as the distance between the positions corresponding to the second mark is less than the first value, traverse the aggregated area of ​​the positions of each first mark, record it as the second area, count the aggregated area of ​​the position of the third mark, the aggregated area is expressed as the distance between the positions corresponding to the third mark is less than the first value, traverse the aggregated area of ​​the positions of each third mark, record it as the third area.

[0028] In the specific implementation, the 36-month sequence from 2021 to 2023, the original area sequence A0…A 35 : Determined monthly by InSAR deformation and manual verification, the minimum patch is 0.3 ha, and the rate of change is calculated as the first difference ΔA per month 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 time (second time) is 90d (empirical value, corresponding to the average incubation period of karst collapse), the first value (aggregation distance d1) = 100m (based on the average influence radius of underground dark rivers), the first area (karst aggregation patches) is obtained from the data of July 2023: there are 14 patches after aggregation, with a total area of ​​41.2ha, an increase of 3.8ha from the previous month, and the error with the R1 estimated value of 3.9ha is only 2.6%. Through the dual-factor model of quantifying the trend by the change rate and quantifying the spatial impact by the aggregation area, the traditional static risk assessment is upgraded to a dynamic spatiotemporal coupling early warning.

[0029] Calculate a first product of the first area and the first rate of change, a second product of the second area and the second rate of change, and a third product of the third area and the third rate of change; draw a first circle, a second circle, and a third circle with the geometric centers of the first area, the second area, and the third area as origins, and the first product, the second product, and the third product as the areas of the circles; and subtract 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; 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.

[0030] In the specific implementation, the first area (karst collapse patch) = 12.4ha; the first change rate R1 = 8.7% / month, the first product S1 = 12.4×8.7 = 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 verification were deployed within the 586m circle, of which 11 groups found hidden fissures (true correlation rate 92%). Low-intensity grouting (0.3MPa) was implemented in the fourth area (karst associated area). The material consumption was only 38% of the traditional solution, and the cost per kilometer was reduced from 95,000 yuan to 33,000 yuan. After 6 months, the settlement rate dropped from 12mm / month to 2mm / month, avoiding a traffic interruption accident (direct loss estimated at 3.5 million yuan). The hit rate in key areas was 93%, and the false alarm rate in associated areas was <10%.

[0031] Setting first detection parameters for key areas and second detection parameters for associated areas respectively, performing a first analysis on the first result and the second result based on the first detection parameters and the second detection parameters to obtain a first void degree and a second void degree, and constructing a first mapping relationship; The first detection parameter and the second detection parameter are of the same type, including the acoustic wave amplitude and the acoustic wave direction, wherein the acoustic wave directions are both perpendicular to the key area or the associated area; The acoustic wave amplitude of the first detection parameter is set to a second value, and the acoustic wave amplitude of the second detection parameter is set to a third value, wherein the second value is smaller than the third value, and the second value and the third value are obtained based on historical experience; The first result and the second result are both expressed as the amplitude of the acoustic wave return; The calculation method of the first voiding degree and the second voiding degree is the same, including: Obtaining the sound wave return amplitude, setting the fourth value and the fifth value as the amplitude threshold of the key area, and setting the sixth value and the 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, obtaining a first void degree according to the sound wave return amplitude of the key area and the amplitude threshold of the key area, and obtaining a second void degree according to the sound wave return amplitude of the associated area and the amplitude threshold of the associated area; The method for setting the first void degree and the second void degree includes: Compare 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, set the first void degree 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, set the first void degree to 0.8; when the sound wave return amplitude of the key area is greater than the fifth value, set the first void degree to 0.5; Compare the acoustic wave return amplitude of the associated area with the amplitude threshold of the associated area; when the acoustic wave return amplitude of the associated area is less than or equal to the sixth value, set the second void degree to 0.3; when the acoustic wave return amplitude of the associated area is greater than the sixth value and less than or equal to the seventh value, set the second void degree to 0.2; when the acoustic wave return amplitude of the associated area is greater than the seventh value, set the second void degree to 0.1; The method for constructing the first mapping relationship includes: Obtain a first hollowness degree of any key area and a second hollowness degree of its corresponding associated area, calculate a third ratio of the first hollowness degree to the second hollowness degree, iterate through each third ratio, calculate a third average value of the third ratios, and set the third average value as a conversion parameter between the first hollowness degree and the second hollowness degree; A first mapping relationship among the position of the key area, the conversion parameter, the position of the associated area and the first degree of void is constructed. The position of the key area and the corresponding first degree of void are input into the first mapping relationship to obtain the position and conversion parameter of the associated area. The second degree of void of the associated area is obtained by calculating the fourth ratio of the first degree of void to the conversion parameter.

[0032] In specific implementation, dual-threshold acoustic wave grading "digitizes" the cavity risk. Acoustic wave amplitude: the second value = 65mV (5% percentile of the historical sample of 432 collapses in the past 5 years), acoustic wave direction: vertically downward (±5°), acoustic wave amplitude: the third value = 110mV (25% percentile of the historical sample), key area: the fourth value = 40mV, the fifth value = 65mV, related area: the sixth value = 75mV, the seventh value = 110mV, on-site calibration: verified on 12 known cavities (0.2-1.3m void thickness), the linear correlation coefficient R between the acoustic wave return amplitude and the void thickness 2 =0.93, 214 groups of “key-related” paired data in the pilot area, the mean of the third ratio (transformation parameter) =0.29, the standard deviation is 0.04, the coefficient of variation is 14%, and the stability is good.

[0033] The present invention uses natural cause screening to narrow the area to be inspected to the first region, reducing the amount of calculation and achieving the transformation from looking for a needle in a haystack to fishing in a pond. The introduction of the time decay factor divides the first region into key areas and related areas, improving the efficiency of detection resource allocation, and constructing a ternary decision-making system for geology, engineering, and big data. Its core value lies in converting invisible underground risks into calculable spatial probability problems, which is in line with the direction of intelligent evolution of transportation infrastructure.

[0034] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0035] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent replacements of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for rapid detection of road cavity formation areas, characterized in that: The following steps are involved: Step S100, screening the natural causes of road cavities based on the natural data of the area to be detected, and screening the area to be detected based on the screened natural causes of cavities to obtain a first area; Step S200: acquiring historical trend data, and segmenting the first region according to the historical trend data to obtain key regions and related regions; Step S300 , respectively setting detection parameters for key areas and associated areas, obtaining a first result and a second result according to the corresponding detection parameters, performing a first analysis on the first result and the second result to obtain a first void degree and a second void degree, and constructing a first mapping relationship.

2. The method for rapid detection of road cavity formation areas according to claim 1, characterized in that: The natural data include the distribution location of underground rivers, the distribution location of soluble rock layers, the duration of heavy rain and soil type, and the duration of heavy rain is expressed as the total annual average heavy rainfall in the area to be analyzed; The natural causes of the cavities include karst collapse, collapsible loess and rainstorm infiltration.

3. The method for rapidly detecting road cavity formation areas according to claim 2, wherein: Methods for screening the natural causes of road voids based on natural data from the area to be inspected include: Determine whether the underground river distribution position and the soluble rock layer distribution position intersect. If so, mark the intersection position first. If not, delete the karst collapse. When the first mark exists, retain the karst collapse. When the first mark does not exist, delete the karst collapse. Determine whether the soil type of any soil position near the underground river distribution location is loess. If it is loess, the corresponding soil position will be marked as a second mark. If it is not loess, jump to the next soil position until all soil positions are traversed and the second mark is completed. When the second mark exists, the collapsible loess is retained. When the second mark does not exist, the collapsible loess is deleted. The first duration is set as the duration threshold, and 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; when the rainstorm duration is less than the first duration, the rainstorm infiltration is deleted. Sets the retained void origin to the filtered void origin.

4. The method for rapid detection of road cavity formation areas according to claim 1, characterized in that: Obtain the roadbed distribution position of the area to be detected, 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 a rectangle, 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 for the third time, and when the second average value is greater than or equal to the first average value, jump to the next rectangular position until all rectangular positions are traversed and each third mark is completed.

5. The method for rapid detection of road cavity formation areas according to claim 4, characterized in that: After the third marking is completed, the first region is set. The method for setting the first region includes: Obtaining positions corresponding to the first mark, the second mark, and the third mark in the area to be detected; The positions corresponding to the first marker, the second marker, and the third marker are set as the first region.

6. The method for rapidly detecting road cavity formation areas according to claim 1, wherein: The historical trend data include historical karst collapse area, historical collapsible loess area and historical rainstorm infiltration area; The historical karst collapse area, the historical collapsible loess area and the historical rainstorm infiltration area are expressed as the areas corresponding to the hollow regions in different areas to be analyzed, and the areas are recorded along the first recording period to form a data sequence.

7. The method for rapid detection of road cavity formation areas according to claim 6, characterized in that: Calculate the first, second, and third change rates corresponding to the historical karst collapse area, historical collapsible loess area, and historical rainstorm infiltration area; The calculation methods of the first change rate, the second change rate, and the third change rate are the same, including: Acquire adjacent historical trend data in chronological order, calculate a first difference between adjacent historical trends, calculate a first ratio of the first difference to a previous historical trend data in the adjacent historical trend data, traverse each first ratio, calculate a second average value of the first ratios, and set the second average value as the change rate; Set the second duration as the diffusion duration, obtain the positions corresponding to the first mark, the second mark, and the third mark, count the aggregated area of ​​the position of the first mark, the aggregated area is expressed as the distance between the positions corresponding to the first mark is less than the first value, traverse the aggregated area of ​​the positions of each first mark, record it as the first area, count the aggregated area of ​​the position of the second mark, the aggregated area is expressed as the distance between the positions corresponding to the second mark is less than the first value, traverse the aggregated area of ​​the positions of each first mark, record it as the second area, count the aggregated area of ​​the position of the third mark, the aggregated area is expressed as the distance between the positions corresponding to the third mark is less than the first value, traverse the aggregated area of ​​the positions of each third mark, record it as the third area.

8. The method for rapidly detecting road cavity formation areas according to claim 7, characterized in that: Calculate a first product of the first area and the first rate of change, a second product of the second area and the second rate of change, and a third product of the third area and the third rate of change; draw a first circle, a second circle, and a third circle with the geometric centers of the first area, the second area, and the third area as origins, and the first product, the second product, and the third product as the areas of the circles; and subtract 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; 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.

9. The method for rapid detection of road cavity formation areas according to claim 1, characterized in that: Setting first detection parameters for key areas and second detection parameters for associated areas respectively, performing a first analysis on the first result and the second result based on the first detection parameters and the second detection parameters to obtain a first void degree and a second void degree, and constructing a first mapping relationship; The first detection parameter and the second detection parameter are of the same type, including the acoustic wave amplitude and the acoustic wave direction, wherein the acoustic wave directions are both perpendicular to the key area or the associated area; The acoustic wave amplitude of the first detection parameter is set to a second value, and the acoustic wave amplitude of the second detection parameter is set to a third value, wherein the second value is smaller than the third value, and the second value and the third value are obtained based on historical experience; The first result and the second result are both expressed as the amplitude of the acoustic wave return; The calculation method of the first voiding degree and the second voiding degree is the same, including: Obtaining the sound wave return amplitude, setting the fourth value and the fifth value as the amplitude threshold of the key area, and setting the sixth value and the 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, obtaining a first void degree according to the sound wave return amplitude of the key area and the amplitude threshold of the key area, and obtaining a second void degree according to the sound wave return amplitude of the associated area and the amplitude threshold of the associated area; The method for setting the first void degree and the second void degree includes: Compare 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, set the first void degree 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, set the first void degree to 0.8; when the sound wave return amplitude of the key area is greater than the fifth value, set the first void degree to 0.5; Compare the acoustic wave return amplitude of the associated area with the amplitude threshold of the associated area; when the acoustic wave return amplitude of the associated area is less than or equal to the sixth value, set the second void degree to 0.3; when the acoustic wave return amplitude of the associated area is greater than the sixth value and less than or equal to the seventh value, set the second void degree to 0.2; when the acoustic wave return amplitude of the associated area is greater than the seventh value, set the second void degree to 0.1; The method for constructing the first mapping relationship includes: Obtaining a first hollowness degree of any key area and a second hollowness degree of its corresponding associated area, calculating a third ratio of the first hollowness degree to the second hollowness degree, traversing each third ratio, calculating a third average of the third ratios, and setting the third average as a conversion parameter between the first hollowness degree and the second hollowness degree; Construct a first mapping relationship among the position of the key area, the conversion parameter, the position of the associated area and the first degree of voidness; by inputting the position of the key area and the corresponding first degree of voidness into the first mapping relationship, obtain the position and conversion parameter of the associated area; and by calculating the fourth ratio of the first degree of voidness to the conversion parameter, obtain the second degree of voidness of the associated area.

10. A rapid detection system for road cavity formation areas, the system being used to execute the rapid detection method for road cavity formation areas according to claim 1, characterized in that: Includes screening module, segmentation module and conversion module; The screening module screens the natural causes of road cavities based on the natural data of the area to be detected, and screens the area to be detected based on the screened natural causes of cavities to obtain a first area; The segmentation module obtains historical trend data and segments the first region according to the historical trend data to obtain key regions and related regions; The conversion module sets detection parameters for key areas and related areas 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 to obtain a first void degree and a second void degree, and constructs a first mapping relationship.

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