Highway health state monitoring and predicting method and system

Abstract

The invention relates to the technical field of highway monitoring, in particular to a highway health state monitoring and predicting method and system, and the method comprises the following steps: collecting and preprocessing highway historical basic data, and constructing a highway data resource pool; constructing a road surface damage identification model and a road health state evaluation model according to the road data resource pool to obtain the road surface damage of the target road section and the overall health state of the road; road elevation data are obtained, regional health state evaluation is carried out according to road surface damage and the overall health state of a road, and a road health state distribution base map is drawn; and predicting the future condition of the target road section based on a numerical simulation technology, and correcting the road health state distribution base map to obtain a road health state distribution map. According to the method, the accurate overall health state of the highway can be obtained, and related personnel can quickly and accurately know the health state of each region of the highway, so that the use performance of the highway is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of highway monitoring, and in particular to a method and system for monitoring and predicting the health status of a highway. Background Art

[0002] As the artery of transportation, the health of highways is crucial to ensuring driving safety and improving traffic efficiency. However, with the continuous increase in traffic volume and vehicle load, the problem of highway damage has become increasingly prominent, bringing huge challenges to highway maintenance and management. In order to effectively monitor and predict the health status of highways and take maintenance measures in time, highway health status monitoring technology has become a research hotspot. However, the current highway health status monitoring methods have some shortcomings.

[0003] First, most methods usually only conduct an overall assessment of the health status of the highway based on highway damage data, such as pavement crack data, pavement pothole data, and pavement settlement data, but lack consideration of the mutual influence between damages, resulting in the evaluation results often being unable to fully and accurately reflect the health status of the highway.

[0004] Secondly, the existing highway health status assessment methods also have limitations in the presentation of results. Traditional assessment methods usually present the assessment results in the form of highway damage level or highway health level, or simply mark the damaged areas. In addition, due to factors such as the time and resources required for repair work and traffic interruption, many highways will continue to be used for a period of time when they are in poor health. The existing highway health status presentation method is not both intuitive and accurate, which is not conducive to relevant personnel quickly and accurately understanding the health status of various areas of the highway. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a method and system for monitoring and predicting the health status of a highway.

[0006] In order to achieve the above-mentioned purpose, in the first aspect, the present invention provides a method for monitoring and predicting the health status of a highway, the method comprising the following steps: collecting historical basic data of the highway, and preprocessing the historical basic data of the highway, thereby constructing a highway data resource pool; constructing a road surface damage identification model and a highway health status evaluation model based on the highway data resource pool, and respectively using them to obtain the road surface damage of the target section and the overall health status of the highway; obtaining highway elevation data, and then performing regional health status assessment based on the road surface damage and the overall health status of the highway, thereby drawing a highway health status distribution base map; predicting the future status of the target section based on numerical simulation technology, and then correcting the highway health status distribution base map to obtain a highway health status distribution map. The present invention can obtain accurate overall highway health status, and can enable relevant personnel to quickly and accurately understand the health status of each area of ​​the highway, thereby improving the performance of the highway.

[0007] Optionally, the collecting of historical highway basic data and preprocessing of the historical highway basic data to construct a highway data resource pool comprises the following steps: Collecting the highway historical basic data and preprocessing the highway historical basic data; Calculating the historical data of the highway health status evaluation index according to the historical basic data of the highway, and then obtaining the historical overall health status of the highway; The highway data resource pool is constructed using the highway historical basic data, the highway health status evaluation index historical data and the highway historical overall health status.

[0008] Furthermore, this method constructs a highway data resource pool to integrate data from different locations, different times, and different types, providing sufficient data for subsequent model construction, which is conducive to improving the accuracy of highway health status monitoring and prediction.

[0009] Optionally, the highway historical basic data includes historical smoothness index, historical damaged area, historical deflection measurement data and historical lateral force measurement data, and the highway health status evaluation index historical data includes historical road surface smoothness, historical road surface damage condition, historical road surface bearing capacity and historical road surface anti-skid capacity; The step of calculating the historical data of the highway health status evaluation index based on the historical basic data of the highway, and then obtaining the historical overall health status of the highway comprises the following steps: Determine the historical smoothness of the road surface, the historical damage condition of the road surface, the historical bearing capacity of the road surface and the historical anti-skid capacity of the road surface according to the historical smoothness index, the historical damaged area, the historical deflection measurement data and the historical lateral force measurement data respectively; The historical overall health status of the highway is calculated based on the principal component analysis method according to the historical smoothness of the road surface, the historical damage condition of the road surface, the historical bearing capacity of the road surface and the historical anti-skid capacity of the road surface.

[0010] Furthermore, after calculating the historical data of highway health status evaluation indicators, this method calculates the overall historical health status of the highway based on the principal component analysis method, and then considers the mutual influence of different highway health status evaluation indicators when calculating the overall historical health status of the highway, providing a data basis for subsequent model construction.

[0011] Optionally, the highway historical basic data further includes historical damage images, and the road surface damage includes damage type and damage area; The step of constructing a road surface damage identification model and a road health status evaluation model according to the road data resource pool, and respectively using them to obtain the road surface damage of a target road section and the overall health status of the road, comprises the following steps: According to the historical damage images in the highway data resource pool, the road surface damage recognition model is constructed using the YOLOv3 algorithm to identify the damage type and damage area of ​​the target road section; According to the historical data of highway health status evaluation indicators and the historical overall health status of the highway in the highway data resource pool, a highway health status evaluation model is constructed using a BP neural network to obtain the overall health status of the highway.

[0012] Furthermore, this method constructs a road surface damage identification model and a highway health status evaluation model to obtain accurate road surface damage and overall highway health status, providing a basis for obtaining a highway health status distribution map and improving the intelligence level of highway health status monitoring and prediction.

[0013] Optionally, the acquiring of highway elevation data, and then performing regional health status assessment according to the road surface damage and the overall health status of the highway, and then drawing a highway health status distribution base map comprises the following steps: Collecting current highway elevation data of the target road section, and dividing the highway elevation data into damaged area elevation data and normal area elevation data according to the damaged area; Downsampling the elevation data of the normal area, and then calculating the health distribution factor of each elevation point in different areas according to the elevation data of the damaged area and the elevation data of the normal area; The overall health status of the highway and the health distribution factor are used to calculate the health value of each elevation point, and then the highway health status distribution base map is drawn based on the health value.

[0014] Furthermore, this method calculates the health values ​​of elevation points and then draws a base map of highway health status distribution, which provides a basis for further accurately and intuitively displaying the health status of each area of ​​the highway on the basis of obtaining the overall health status of the highway.

[0015] Optionally, the health allocation factor satisfies the following relationship:

[0016] in, assigning a factor to said health, is the elevation value of the ith elevation point in the nth damage area, is the initial elevation of the road surface, is the number of elevation points in the nth damage area, N is the number of damage areas, is an adjustable parameter, S is the road surface area of ​​the target road section, is the area of ​​the nth damaged region, is the elevation point, A is the damaged area, M is the number of elevation points in the normal area, is the elevation value of the jth elevation point in the normal area, is the area of ​​the normal region.

[0017] Furthermore, the calculation of the health distribution factor comprehensively considers the overall health status of the highway, the area where the elevation point is located, and the change in the elevation value of the elevation point. Based on the health distribution factor, reasonable health values ​​can be assigned to the elevation points, providing a basis for drawing an accurate and reliable highway health status distribution base map.

[0018] Optionally, the calculating the health value of each elevation point using the overall health state of the highway and the health distribution factor, and then drawing the highway health state distribution base map based on the health value comprises the following steps: Calculating the health value of each elevation point using the overall health status of the highway and the health allocation factor, and clustering the elevation points using a clustering algorithm according to the health value; The road surface of the target road section is divided into a plurality of health identification areas according to the clustering result, thereby obtaining the highway health status distribution base map.

[0019] Furthermore, the method clusters the elevation points according to their health values, and draws a highway health status distribution base map based on the clustering results, so that the highway health status distribution base map can more accurately reflect the health status of each area of ​​the target section.

[0020] Optionally, the predicting the future condition of the target road section based on numerical simulation technology, and then correcting the highway health status distribution base map to obtain the highway health status distribution map comprises the following steps: Using numerical simulation technology to perform numerical simulation on the target road section, and performing regional health status assessment on the target road section according to the simulation results, thereby obtaining a highway health status distribution prediction map; The highway health status distribution base map is modified according to the simulation results and the highway health status distribution prediction map to obtain the highway health status distribution map.

[0021] Furthermore, this method uses the highway health status distribution prediction map to correct the highway health status distribution base map, which can not only enhance the reliability of highway health status monitoring, but also foresee potential problems and improve user experience.

[0022] Optionally, the step of modifying the highway health status distribution base map according to the simulation results and the highway health status distribution prediction map to obtain the highway health status distribution map comprises the following steps: Comparing the simulation result with the current target road section to determine the potential damaged area, and determining the health prediction value of the potential damaged area according to the highway health status distribution prediction map; The potential damaged areas and their health prediction values ​​are marked on the highway health status distribution base map, thereby obtaining the highway health status distribution map.

[0023] Furthermore, the method uses the highway health status distribution prediction map to modify the highway health status distribution base map to obtain a highway health status distribution map, so that the highway health status distribution map can not only display the health status of each current functional area, but also achieve early warning of risks, provide decision support for relevant personnel, and improve the performance of highway use.

[0024] In a second aspect, the present invention provides a highway health status monitoring and prediction system, the highway health status monitoring and prediction system comprising: a data acquisition device, a data output device, a processor and a storage, the storage comprising a computer-readable storage medium, the computer-readable storage medium storing a computer program, the computer program comprising program instructions, the program instructions when executed by the processor enable the processor to implement a highway health status monitoring and prediction method provided by the present invention. The system can improve the efficiency of highway health status monitoring and prediction, and can improve the practicality of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0026] Figure 1 A schematic diagram of a flow chart of a highway health status monitoring and prediction method according to an embodiment of the present invention; Figure 2 The figure is a schematic diagram of the framework of a highway health status monitoring and prediction system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

[0028] Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and / or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

[0029] It should be noted in advance that, in an optional embodiment, except for independent explanations, the same symbols or letters appearing in all formulas have the same meanings and values.

[0030] In an alternative embodiment, see Figure 1 The present invention provides a highway health status monitoring and prediction method, the method comprising the following steps: S1. Collect historical basic highway data, and pre-process the historical basic highway data to build a highway data resource pool.

[0031] Wherein, step S1 specifically includes the following steps: S11, collecting the historical basic data of the highway, and preprocessing the historical basic data of the highway.

[0032] Specifically, in this embodiment, the collected historical basic highway data includes historical flatness index, historical damaged area, historical deflection measurement data, historical lateral force measurement data, and historical damage images. In the collected historical basic highway data, the historical flatness index, historical damaged area, historical deflection measurement data, historical lateral force measurement data, and historical damage images belonging to the same group are from the same road section, while the historical flatness index, historical damaged area, historical deflection measurement data, historical lateral force measurement data, and historical damage images of different groups may be from different road sections.

[0033] Furthermore, the historical basic data of highways are preprocessed to improve the accuracy of the data and provide a reliable data basis for accurately monitoring and predicting the health status of highways. For the historical flatness index, historical damaged area, historical deflection measurement data and historical lateral force measurement data, the preprocessing includes outlier removal and missing value supplementation; for the historical damage images, the preprocessing includes image denoising, image enhancement and image cropping, as well as marking the damage type and damaged area.

[0034] S12. Calculate historical data of highway health status evaluation indicators based on the historical basic data of the highway, and then obtain the historical overall health status of the highway.

[0035] The historical data of highway health status evaluation indicators include historical road surface flatness, historical road surface damage, historical road surface bearing capacity and historical road surface anti-skid capacity. Step S12 specifically includes the following steps: S121. Determine the historical smoothness of the road surface, the historical damage condition of the road surface, the historical bearing capacity of the road surface and the historical anti-skid capacity of the road surface according to the historical smoothness index, the historical damaged area, the historical deflection measurement data and the historical lateral force measurement data respectively.

[0036] Specifically, in this embodiment, this step is a prior art method, so the detailed calculation process is not described here.

[0037] S122. Calculate the overall historical health status of the highway based on the principal component analysis method according to the historical smoothness of the road surface, the historical damage condition of the road surface, the historical bearing capacity of the road surface and the historical anti-skid capacity of the road surface.

[0038] Specifically, in this embodiment, based on the calculated historical smoothness of the road surface, the historical damage condition of the road surface, the historical bearing capacity of the road surface, and the historical anti-skid capacity of the road surface, the principal component analysis method is used to obtain multiple principal components, and then each principal component and its cumulative variance contribution rate are normalized respectively, and the normalized result of the cumulative variance contribution rate of each principal component is used as the weight of the corresponding principal component to calculate the overall health status of the highway history. The overall health status of the highway history specifically satisfies the following relationship:

[0039] Among them, P is the overall health status of the highway in history, K is the number of principal components, is the normalized value of the kth principal component, is the weight of the kth principal component. The traditional highway health status evaluation method usually takes the weighted sum of highway health status evaluation indicators as the overall highway health status, thus ignoring the mutual influence between various highway health status evaluation indicators. However, this step takes into account the mutual influence between different highway health status evaluation indicators when calculating the overall health status of the highway history, providing a data basis for the subsequent use of the model to predict the overall health status of the highway.

[0040] It should be noted that if the overall highway health status can be collected based on the mutual influence between various highway health status evaluation indicators, step S12 is not necessary.

[0041] S13: construct the highway data resource pool using the highway historical basic data, the highway health status evaluation index historical data and the highway historical overall health status.

[0042] This embodiment builds a highway data resource pool to integrate data from different locations, different times, and different types, providing sufficient data for subsequent model construction, which is conducive to accurately monitoring and predicting the health status of highways.

[0043] S2. Constructing a road surface damage identification model and a road health status evaluation model based on the road data resource pool, and using them to obtain the road surface damage of the target road section and the overall health status of the road respectively.

[0044] The road surface damage includes the damage type and the damage area. Step S2 specifically includes the following steps: S21. Based on the historical damage images in the highway data resource pool, the YOLOv3 algorithm is used to build the road surface damage recognition model to identify the damage type and damage area of ​​the target road section.

[0045] Specifically, in this embodiment, historical damage images in the highway data resource pool are extracted and an image set is constructed, and then the image set is used to complete the training and verification of YOLOv3 to obtain a road surface damage recognition model.

[0046] The target road section is photographed by a high-definition camera, and the photographed images are input into the road surface damage recognition model to identify the current damaged area of ​​the target road section and the damage type in the damaged area, providing a data basis for the subsequent drawing of the highway health status distribution map.

[0047] S22: Based on the historical data of highway health status evaluation indicators and the historical overall health status of the highway in the highway data resource pool, a highway health status evaluation model is constructed using a BP neural network to obtain the overall health status of the highway.

[0048] Specifically, in this embodiment, the highway health status evaluation index historical data and the highway historical overall health status composition data set in the highway data resource pool are extracted to complete the training and verification of the BP neural network to obtain the highway health status evaluation model. Among them, the input of the highway health status evaluation model is the highway health status evaluation index historical data of various highway health status evaluation indicators, and the output is the highway historical overall health status.

[0049] Furthermore, the current smoothness index, damaged area, deflection measurement data and lateral force measurement data of the target section are collected, and then the current values ​​of various highway health status evaluation indicators of the target section are calculated, and the obtained highway health status evaluation index values ​​are input into the highway health status evaluation model to obtain the current overall highway health status of the target section.

[0050] There are four reasons why this embodiment uses the BP neural network to construct a highway health status evaluation model to predict the overall health status of the highway on the basis of the method for calculating the historical overall health status of the highway given in step S122. First, in the evaluation of the health status of the highway, there may be complex nonlinear relationships between the various evaluation indicators, and the BP neural network has a strong nonlinear mapping ability, which can capture these relationships and make accurate predictions based on these relationships. Second, the BP neural network is adaptable and flexible, and can continuously update and optimize the model as new data is added. This means that as time goes by and the data changes, the neural network model can maintain its predictive performance, while PCA, as a static method, may not be able to fully adapt to these changes. Third, by constructing a highway health status evaluation model through the BP neural network, data-driven prediction can be achieved, which means that when necessary, the model can predict the future health status of the highway based on the laws and relationships learned from historical data. Fourth, step S12 can be selected whether to be executed according to actual conditions.

[0051] S3. Obtain highway elevation data, and then perform regional health status assessment based on the road surface damage and the overall health status of the highway, and then draw a highway health status distribution base map.

[0052] After step S1 and step S2, the overall health status of the target section of the highway can be obtained, but the health status of each area of ​​the highway cannot be displayed intuitively. Therefore, step S3 further draws a base map of the highway health status distribution, which provides a basis for further accurately and intuitively displaying the health status of each area of ​​the highway on the basis of obtaining the overall health status of the highway. Step S3 specifically includes the following steps: S31, collecting current highway elevation data of the target road section, and dividing the highway elevation data into damaged area elevation data and normal area elevation data according to the damaged area.

[0053] Specifically, in this embodiment, road surface damage such as cracks, ruts, potholes and subsidence can most directly reflect the health of the highway, and these road surface damages will cause a significant change in the shape of the highway pavement, while the elevation data of the road surface can directly reflect the degree of deformation of the highway pavement. Therefore, this embodiment collects the current highway elevation data of the target road section to provide a data basis for drawing a base map of the highway health status distribution.

[0054] More specifically, this embodiment obtains the current highway elevation data of the target road section through a three-dimensional laser radar, and uses the highway elevation data of the damaged area as the damaged area elevation data, and the highway elevation data of other areas as the normal area elevation data.

[0055] Furthermore, since there may be multiple damaged areas in the target road section and the damage types in different damaged areas may be different, in order to improve the accuracy of road health monitoring, different damaged areas will be distinguished when calculating the health allocation factor later, and there will be multiple sets of damaged area elevation data accordingly, while non-damaged areas will be uniformly treated as normal areas, and there will be only one set of normal area elevation data accordingly.

[0056] S32, downsampling the elevation data of the normal area, and then calculating the health distribution factor of each elevation point in different areas according to the elevation data of the damaged area and the elevation data of the normal area.

[0057] Specifically, in this embodiment, there is no damage in the non-damaged area or the damage is negligible, so it is considered that it is not an area that requires special attention, so the elevation data of the normal area is downsampled, which is conducive to reducing the amount of data processing.

[0058] Furthermore, after downsampling the elevation data of the normal area, the health distribution factor of each elevation point in different areas is calculated based on the elevation data of the damaged area and the elevation data of the normal area. The health distribution factor specifically satisfies the following relationship:

[0059] in, Assign factors to health, is the elevation value of the ith elevation point in the nth damage area, is the initial elevation of the road surface, is the number of elevation points in the nth damage area, N is the number of damage areas, is an adjustable parameter, S is the road surface area of ​​the target section, is the area of ​​the nth damaged region, is the elevation point, A is the damaged area, M is the number of elevation points in the normal area, is the elevation value of the jth elevation point in the normal area, is the area of ​​the normal region. The initial elevation of the road surface is the average of all elevation points on the road surface when the expressway is built, or it can be the average of all elevation points in the normal area. It can be taken as 0.4 to assign a smaller health value to the damaged area and a larger health value to the normal area.

[0060] The calculation of the health distribution factor comprehensively considers the overall health status of the highway, the area where the elevation point is located, and the change in the elevation value of the elevation point. Based on the health distribution factor, reasonable health values ​​can be assigned to the elevation points, providing a basis for drawing an accurate and reliable highway health status distribution base map.

[0061] S33: Calculate the health value of each elevation point using the overall health status of the highway and the health distribution factor, and then draw a base map of the highway health status distribution based on the health value.

[0062] Wherein, step S33 specifically includes the following steps: S331. Calculate the health value of each elevation point using the overall health status of the highway and the health distribution factor, and cluster the elevation points using a clustering algorithm according to the health value.

[0063] Specifically, in this embodiment, the overall health status of the highway is regarded as the average of the health values ​​of each area of ​​the target road section, so the product of the overall health status of the highway and the health allocation factor can be used as the health value of the elevation point, and then the health value of each elevation point is calculated using the overall health status of the highway and the health allocation factor. Then, the number of cluster centers is selected, and the K-means algorithm is used to divide the elevation points into multiple clusters according to the health value.

[0064] Furthermore, before clustering the elevation points according to the health values, the health values ​​of the elevation points may be standardized or normalized to improve the clustering effect.

[0065] S332. Divide the road surface of the target road section into a plurality of health identification areas according to the clustering result, thereby obtaining a base map of the highway health status distribution.

[0066] Specifically, in this embodiment, each cluster is regarded as a health identification zone according to the clustering results. The elevation points in each zone have similar health values, so they can be considered to be in similar health states, and the average of the health values ​​of each elevation point in the health identification zone can be used as the health value of the health identification zone. Use GIS software to superimpose the health identification zone on the map of the target road section, and use different colors to mark different health identification zones, and mark the health values ​​of each health identification zone, so as to obtain a base map of the highway health status distribution.

[0067] More specifically, a green mark is used for the health identification area with the highest health value, and a red mark is used for the health identification area with the lowest health value.

[0068] This embodiment clusters the elevation points according to their health values, and draws a highway health status distribution base map according to the clustering results, so that the highway health status distribution base map can more accurately reflect the current health status of each area of ​​the target road section.

[0069] S4. Predicting the future status of the target road section based on numerical simulation technology, and then correcting the highway health status distribution base map to obtain a highway health status distribution map.

[0070] Wherein, step S4 specifically includes the following steps: S41. Use numerical simulation technology to perform numerical simulation on the target road section, and perform regional health status assessment on the target road section according to the simulation results, so as to obtain a highway health status distribution prediction map.

[0071] Specifically, in this embodiment, ABAQUS is used to perform numerical simulation on the current target road section to obtain the future highway condition of the target road section, and then the predicted values ​​of various highway health status evaluation indicators are calculated based on the simulation results, and the highway health status evaluation model is used to obtain the overall health status of the highway in the future.

[0072] Furthermore, when the current highway elevation data of the target section has been obtained, the simulation results are compared with the current target section to obtain the highway elevation data of the target section in the simulation results, and then the highway health status distribution prediction map is obtained using the same method as that for obtaining the highway health status distribution base map.

[0073] S42: modify the highway health status distribution base map according to the simulation results and the highway health status distribution prediction map to obtain the highway health status distribution map.

[0074] Wherein, step S42 specifically includes the following steps: S421, comparing the simulation result with the current target road section to determine a potential damaged area, and determining a health prediction value of the potential damaged area according to the highway health status distribution prediction map.

[0075] Specifically, in this embodiment, the future damage area of ​​the target road section can be obtained according to the simulation results, and the current damage area of ​​the target road section can be obtained in step S21. Therefore, the potential damage area can be determined by comparing the future damage area of ​​the target road section with the current damage area. Specifically, the part of the future damage area of ​​the target road section that exceeds the current damage area is taken as the potential damage area.

[0076] Furthermore, since a potential damaged area may be divided into multiple parts by different health identification zones on the highway health status distribution prediction map, if a potential damaged area is divided into multiple parts, then the health prediction value of each part is the health value of the health identification zone where they are located on the highway health status distribution prediction map.

[0077] S422: Mark the potential damaged areas and their health prediction values ​​on the highway health status distribution base map, thereby obtaining the highway health status distribution map.

[0078] Specifically, in this embodiment, it is only necessary to mark the potential damaged area with a dotted line boundary on the highway health status distribution base map, and indicate the health value of the potential damaged area. In addition, it is understandable that the potential damaged area may also overlap with multiple healthy identification areas on the highway health status distribution base map.

[0079] Furthermore, this embodiment modifies the highway health status distribution base map according to the simulation results and the highway health status distribution prediction map to obtain a highway health status distribution map, so that the highway health status distribution map can not only intuitively display the current health status of each area of ​​the highway, but also foresee potential problems. The obtained highway health status distribution map can be published on the Internet, or printed out as a paper document and displayed on the target road section through a bulletin board. This not only enables relevant personnel to quickly and accurately understand the health status of each area of ​​the highway, and maintain the highway in time to improve the performance of the highway, but also enables drivers to understand the health status of each area of ​​the highway before the highway is repaired, which helps drivers optimize driving routes to avoid risks and improve the driving experience of drivers.

[0080] It should be noted that, in some cases, the actions described in the specification can be performed in a different order and still achieve the desired results. In this embodiment, the order of steps given is only to make the embodiment appear clearer and easier to explain, rather than to limit it.

[0081] In an alternative embodiment, see Figure 2 In order to improve the efficiency of highway health status monitoring and prediction and improve the practicality of the method, the present invention also provides a highway health status monitoring and prediction system, the highway health status monitoring and prediction system comprises: a data acquisition device 1, a data output device 2, a processor 3 and a storage 4, the storage 4 comprises a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program comprises program instructions, and when the program instructions are executed by the processor 3, the processor 3 implements the contents of steps S1 to S4.

[0082] Specifically, in this embodiment, the data acquisition device 1 includes a USB interface, a three-dimensional laser radar, a high-definition camera and a digital display screen. The USB interface is used to import historical basic data of the highway from other devices, the three-dimensional laser radar is used to obtain highway elevation data, the high-definition camera is used to shoot the target road section, and the digital display screen is used to manually input the flatness index, damaged area, deflection measurement data, lateral force measurement data and data required for numerical simulation of the target road section. The data output device 2 includes a digital display screen for displaying the overall health status of the highway, the base map of the highway health status distribution, the highway health status distribution prediction map and the highway health status distribution map.

[0083] In summary, this method provides a new idea for highway health monitoring and prediction. First, this method constructs a road surface damage identification model and a highway health status evaluation model to obtain accurate road surface damage and the overall health status of the highway, and provides a basis for obtaining the highway health status distribution map; secondly, this method divides the highway elevation data into damaged area elevation data and normal area elevation data according to the road surface damage identification results, and calculates the health distribution factor of each elevation point in different areas, and then calculates the health value of each elevation point according to the overall health status of the highway and the health distribution factor to draw the highway health status distribution base map, which can more accurately reflect the health status of each area of ​​the target section; finally, this method obtains the highway health status distribution prediction map of the target section based on numerical simulation technology, which is used to correct the highway health status distribution base map, and finally obtains the highway health status distribution map, which enables relevant personnel to quickly and accurately understand the health status of each area of ​​the highway, and can foresee potential problems, improve the use performance and user experience of the highway. In addition, as a system compatible with this method, this system can not only improve the efficiency of highway health status monitoring and prediction, but also improve the practicality of this method.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A highway health status monitoring and prediction method, characterized in that: The steps include: Collecting historical basic highway data and preprocessing the historical basic highway data to build a highway data resource pool; A road surface damage identification model and a road health status evaluation model are constructed according to the road data resource pool, and are used to obtain the road surface damage of the target road section and the overall health status of the road respectively; Obtaining highway elevation data, and then performing a regional health status assessment based on the road surface damage and the overall health status of the highway, and then drawing a highway health status distribution base map; The future condition of the target road section is predicted based on numerical simulation technology, and then the highway health status distribution base map is corrected to obtain a highway health status distribution map.

2. A highway health status monitoring and prediction method according to claim 1, characterized in that: The collecting of historical basic highway data and preprocessing of the historical basic highway data to construct a highway data resource pool includes the following steps: Collecting the highway historical basic data and preprocessing the highway historical basic data; Calculating the historical data of the highway health status evaluation index according to the historical basic data of the highway, and then obtaining the historical overall health status of the highway; The highway data resource pool is constructed using the highway historical basic data, the highway health status evaluation index historical data and the highway historical overall health status.

3. A highway health status monitoring and prediction method according to claim 2, characterized in that: The historical basic data of the highway include historical smoothness index, historical damaged area, historical deflection measurement data and historical lateral force measurement data; the historical data of highway health status evaluation indicators include historical road surface smoothness, historical road surface damage condition, historical road surface bearing capacity and historical road surface anti-skid capacity; The step of calculating the historical data of the highway health status evaluation index based on the historical basic data of the highway, and then obtaining the historical overall health status of the highway comprises the following steps: Determine the historical smoothness of the road surface, the historical damage condition of the road surface, the historical bearing capacity of the road surface and the historical anti-skid capacity of the road surface according to the historical smoothness index, the historical damaged area, the historical deflection measurement data and the historical lateral force measurement data respectively; The historical overall health status of the highway is calculated based on the principal component analysis method according to the historical smoothness of the road surface, the historical damage condition of the road surface, the historical bearing capacity of the road surface and the historical anti-skid capacity of the road surface.

4. A highway health status monitoring and prediction method according to claim 1, characterized in that: The highway historical basic data also includes historical damage images, and the road surface damage includes damage type and damage area; The step of constructing a road surface damage identification model and a road health status evaluation model according to the road data resource pool, and respectively using them to obtain the road surface damage of a target road section and the overall health status of the road, comprises the following steps: According to the historical damage images in the highway data resource pool, the road surface damage recognition model is constructed using the YOLOv3 algorithm to identify the damage type and damage area of ​​the target road section; According to the historical data of highway health status evaluation indicators and the historical overall health status of the highway in the highway data resource pool, a highway health status evaluation model is constructed using a BP neural network to obtain the overall health status of the highway.

5. A highway health status monitoring and prediction method according to claim 4, characterized in that: The obtaining of highway elevation data, and then performing regional health status assessment based on the road surface damage and the overall health status of the highway, and then drawing a highway health status distribution base map comprises the following steps: Collecting current highway elevation data of the target road section, and dividing the highway elevation data into damaged area elevation data and normal area elevation data according to the damaged area; Downsampling the elevation data of the normal area, and then calculating the health distribution factor of each elevation point in different areas according to the elevation data of the damaged area and the elevation data of the normal area; The overall health status of the highway and the health distribution factor are used to calculate the health value of each elevation point, and then the highway health status distribution base map is drawn based on the health value.

6. A highway health status monitoring and prediction method according to claim 5, characterized in that: The health allocation factor satisfies the following relationship: ， in, assigning a factor to said health, is the elevation value of the ith elevation point in the nth damage area, is the initial elevation of the road surface, is the number of elevation points in the nth damage area, N is the number of damage areas, is an adjustable parameter, S is the road surface area of ​​the target road section, is the area of ​​the nth damaged region, is the elevation point, A is the damaged area, M is the number of elevation points in the normal area, is the elevation value of the jth elevation point in the normal area, is the area of ​​the normal region.

7. A highway health status monitoring and prediction method according to claim 5, characterized in that: The step of calculating the health value of each elevation point using the overall health state of the highway and the health distribution factor, and then drawing the highway health state distribution base map based on the health value comprises the following steps: Calculating the health value of each elevation point using the overall health status of the highway and the health allocation factor, and clustering the elevation points using a clustering algorithm according to the health value; The road surface of the target road section is divided into a plurality of health identification areas according to the clustering result, thereby obtaining the highway health status distribution base map.

8. A highway health status monitoring and prediction method according to claim 1, characterized in that: The method of predicting the future condition of the target road section based on numerical simulation technology and then correcting the highway health status distribution base map to obtain the highway health status distribution map comprises the following steps: Using numerical simulation technology to perform numerical simulation on the target road section, and performing regional health status assessment on the target road section according to the simulation results, thereby obtaining a highway health status distribution prediction map; The highway health status distribution base map is modified according to the simulation results and the highway health status distribution prediction map to obtain the highway health status distribution map.

9. A highway health status monitoring and prediction method according to claim 8, characterized in that: The step of modifying the highway health status distribution base map according to the simulation results and the highway health status distribution prediction map to obtain the highway health status distribution map comprises the following steps: Comparing the simulation result with the current target road section to determine the potential damaged area, and determining the health prediction value of the potential damaged area according to the highway health status distribution prediction map; The potential damaged areas and their health prediction values ​​are marked on the highway health status distribution base map, thereby obtaining the highway health status distribution map.

10. A highway health status monitoring and prediction system, characterized in that: The highway health status monitoring and prediction system includes: a data acquisition device, a data output device, a processor and a storage device, the storage device includes a computer-readable storage medium, the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by the processor, the processor implements a highway health status monitoring and prediction method as described in any one of claims 1-9.

Images