Road surface defect monitoring and predicting method and system

Abstract

The invention relates to the technical field of urban traffic management, in particular to a pavement defect monitoring and predicting method and system, and the method comprises the following steps: constructing a pavement digital twinborn model of a target pavement; constructing a historical road surface monitoring data set, and obtaining road surface monitoring index accurate data and road surface high-definition image data in real time; according to the road surface digital twinborn model and the road surface monitoring data set, obtaining the index weight of each road surface monitoring index by using a double-source weight fusion method; and updating the road surface digital twinborn model based on the road surface monitoring index accurate data, the index weight and the road surface high-definition image data, realizing real-time monitoring and prediction of target road surface defects, and formulating a road surface maintenance strategy. On the basis of the road surface digital twin model, the influence degree of each road surface monitoring index on the road surface defect is considered, the real-time, accurate and efficient monitoring and prediction of the road surface defect are realized, the road surface maintenance strategy is formulated in time, and the traffic capacity and safety of the road are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of urban traffic management, and in particular to a road surface defect monitoring and prediction method and system. Background Art

[0002] In the rapidly developing modern transportation system, roads serve as vital infrastructure connecting cities and rural areas and facilitating economic exchange. Their condition is directly linked to the smooth and safe flow of traffic. However, with the surge in vehicle numbers and the increasing age of roads, pavement defects are becoming increasingly prominent, becoming a key factor restricting traffic efficiency and safety. Pavement defects such as cracks and potholes not only affect driving comfort but can also cause traffic accidents, posing a serious threat to people's lives and property.

[0003] Traditional methods for monitoring road surface defects rely primarily on manual inspections and periodic testing. While intuitive, manual inspections are limited by labor costs, frequency, and subjective judgment, making efficient and accurate monitoring of large road areas difficult. Periodic testing, on the other hand, is subject to time lags, often preventing timely detection and resolution of road surface defects. This leads to the accumulation of problems and exacerbates damage to road structures and driving safety. Furthermore, with the rise of intelligent transportation systems, the need for real-time monitoring and early warning of road conditions is becoming increasingly urgent. Traditional monitoring methods suffer from significant deficiencies in data processing, information integration, and intelligent analysis, making them unable to meet the demands of modern traffic management.

[0004] Digital twin technology, a recently emerging advanced technology, offers new insights into pavement defect monitoring and prediction. This technology integrates multiple sensors and monitoring devices to achieve real-time, comprehensive monitoring of pavement conditions. By constructing a digital mirror of the physical world, it enables comprehensive, real-time, and dynamic simulation of physical entities and their operating environments. This allows for the early detection of potential pavement defects, providing scientific decision-making support for road maintenance, effectively reducing maintenance costs and the risk of traffic accidents, and promoting the development of intelligent transportation. Currently, some methods for monitoring pavement defects using digital twin technology exist, but these methods simply update the digital twin model using monitoring data, leaving room for improvement in the accuracy of pavement defect monitoring and prediction. Summary of the Invention

[0005] In response to the deficiencies in the prior art, the present invention provides a road surface defect monitoring and prediction method and system.

[0006] To achieve the above-mentioned objectives, in a first aspect, the present invention provides a pavement defect monitoring and prediction method, the method comprising the following steps: constructing a pavement digital twin model of a target pavement; collecting historical pavement monitoring data to construct a historical pavement monitoring data set, and obtaining precise pavement monitoring indicator data and high-definition pavement image data in real time; obtaining the indicator weights of each pavement monitoring indicator using a dual-source weight fusion method based on the pavement digital twin model and the pavement monitoring data set; updating the pavement digital twin model based on the precise pavement monitoring indicator data, the indicator weights, and the high-definition pavement image data to achieve real-time monitoring of target pavement defects; predicting the precise pavement monitoring indicator data, and then predicting the target pavement defects based on the indicator weights and the pavement digital twin model, and formulating a pavement maintenance strategy. Based on the pavement digital twin model, the present invention takes into account the degree of influence of various pavement monitoring indicators on pavement defects, thereby achieving real-time, accurate, and efficient monitoring and prediction of pavement defects, and then formulating a pavement maintenance strategy in a timely manner to ensure the traffic capacity and safety of the road.

[0007] Optionally, the historical road surface monitoring data includes historical road surface monitoring index data and historical road surface image data; The process of collecting historical road surface monitoring data to construct a historical road surface monitoring data set and obtaining accurate road surface monitoring indicator data and high-definition road surface image data in real time includes the following steps: Collecting historical road surface monitoring data and preprocessing the historical road surface monitoring data to construct a historical road surface monitoring data set; Acquire road surface monitoring index data and road surface image data in real time, and perform preprocessing to obtain the accurate road surface monitoring index data and the high-definition road surface image data.

[0008] Furthermore, indicator weights are obtained based on historical pavement monitoring data sets, and the importance of each pavement monitoring indicator is taken into account when updating pavement information on the pavement digital twin model. In addition, the precise data of pavement monitoring indicators and high-definition image data of the pavement can accurately reflect the real-time condition of the pavement, making the pavement defect monitoring prediction results based on the pavement digital twin model more objective and accurate.

[0009] Optionally, the real-time acquisition of road surface monitoring index data and road surface image data, and preprocessing to obtain the accurate road surface monitoring index data and the high-definition road surface image data comprises the following steps: Performing data cleaning on the road surface monitoring indicator data to obtain accurate data of the road surface monitoring indicator; The road surface image data is smoothed and denoised, and the contrast is enhanced to obtain the road surface high-definition image data.

[0010] Furthermore, by preprocessing the road surface monitoring index data and road surface image data so that they can accurately reflect the real-time conditions of the road surface, the updated road surface digital twin model can also accurately reflect the real-time conditions of the road surface, thereby realizing accurate monitoring of the road surface.

[0011] Optionally, the obtaining the indicator weights of each road surface monitoring indicator using a dual-source weight fusion method based on the road surface digital twin model and the road surface monitoring dataset includes the following steps: Performing image recognition on historical road surface image data in the road surface monitoring data set to obtain road surface defects, wherein the road surface defects include cracks and potholes; Calculating the pavement defect evaluation factor value corresponding to each group of pavement monitoring data in the pavement monitoring data set according to the image recognition result; Based on the pavement monitoring data set and the pavement defect evaluation factor value, using a permutation importance method to obtain a first weight of the pavement monitoring indicator; Obtaining a second weight of the pavement monitoring indicator using an item-by-item analysis method based on the pavement digital twin model, the pavement monitoring dataset, and the pavement defect evaluation factor value; The indicator weight is calculated according to the first weight and the second weight.

[0012] Furthermore, the first weight is obtained based on the machine learning method, and the second weight is obtained based on the statistical test method. Fusion of the two to obtain the indicator weight can avoid the problem of one-sidedness of the weight obtained by a single method and improve the accuracy of the road defect monitoring prediction results.

[0013] Optionally, obtaining the second weight of the pavement monitoring indicator using an item-by-item analysis method based on the pavement digital twin model, the pavement monitoring dataset, and the pavement defect evaluation factor value comprises the following steps: Randomly selecting a group of road surface monitoring data from the road surface monitoring data set as the array to be adjusted, and the remaining groups of road surface monitoring data as the adjustment arrays; For a certain road surface monitoring indicator, sequentially replacing the value of the road surface monitoring indicator in the array to be adjusted with the value of the road surface monitoring indicator in the adjustment array to obtain multiple groups of adjusted arrays, and marking the road surface monitoring indicator as a road surface monitoring change indicator; The adjusted array is used to update the pavement digital twin model, identify pavement defects and calculate multiple pavement defect evaluation factor comparison values, and then the second weight of the pavement monitoring change index is calculated in combination with the pavement defect evaluation factor values.

[0014] Optionally, the second weight satisfies the following relationship:

[0015] in, is the second weight, N is the number of the adjustment arrays, is the comparison value of the road surface defect evaluation factor of the i-th, is the road surface defect evaluation factor value, is the value of the road surface monitoring change index in the i-th adjustment array, is the value of the road surface monitoring change index in the array to be adjusted.

[0016] Optionally, updating the pavement digital twin model based on the precise pavement monitoring indicator data, the indicator weights, and the pavement high-definition image data to achieve real-time monitoring of target pavement defects includes the following steps: Calculating weighted road surface monitoring index data based on the road surface monitoring index precise data and the index weight; The weighted pavement monitoring index data and the pavement high-definition image data are used to update the pavement digital twin model to obtain a real-time twin pavement, thereby realizing real-time monitoring of the target pavement defects.

[0017] Furthermore, by combining the precise data of pavement monitoring indicators and indicator weights to calculate weighted pavement monitoring indicator data, the impact of different pavement monitoring indicators on pavement conditions is fully considered, and high-definition pavement image data is introduced to achieve multi-source data fusion, so that the pavement digital twin model can more comprehensively reflect the actual situation of the pavement, thereby improving the real-time and comprehensiveness of monitoring.

[0018] Optionally, predicting the precise data of the pavement monitoring indicators, predicting the target pavement defects based on the indicator weights and the pavement digital twin model, and formulating a pavement maintenance strategy includes the following steps: Predicting the precise data of the road surface monitoring indicators to obtain the future indicator prediction data of each road surface monitoring indicator; Calculating weighted road surface monitoring index prediction data based on the index prediction data and the index weight; Using the weighted pavement monitoring index prediction data to update the real-time twin pavement to obtain a future twin pavement, thereby achieving prediction of the target pavement defects; formulating a current pavement maintenance strategy based on the weighted pavement monitoring indicator data; A future pavement maintenance strategy is formulated based on the real-time twin pavement, the weighted pavement monitoring index prediction data and the future twin pavement, and the current pavement maintenance strategy is integrated to obtain the pavement maintenance strategy.

[0019] Furthermore, by predicting future road surface defects, road maintenance can be planned in advance to prevent further deterioration. When formulating road maintenance strategies, integrating current and future road maintenance strategies can comprehensively consider the current and future state of the road surface, combining long-term planning with short-term response, and developing more scientific and reasonable maintenance strategies. This helps optimize the allocation of maintenance resources, reduce unnecessary waste, and improve the efficiency and effectiveness of maintenance work.

[0020] Optionally, formulating a future pavement maintenance strategy based on the real-time twin pavement, the weighted pavement monitoring index prediction data, and the future twin pavement, and integrating the current pavement maintenance strategy to obtain the pavement maintenance strategy includes the following steps: Arrange the weighted road surface monitoring index prediction data in descending order; Deleting the smallest data in the weighted road surface monitoring index prediction data in sequence, and using the remaining weighted road surface monitoring index prediction data after each deletion to update the real-time twin road surface to obtain a comparison twin road surface, and calculating the similarity between the comparison twin road surface and the future twin road surface; When the similarity is less than the similarity threshold for the first time, the operation of deleting the weighted road surface monitoring index prediction data is stopped, and the weighted road surface monitoring index prediction data remaining after the last deletion operation is used as the final judgment data; A future pavement maintenance strategy is formulated according to the final judgment data, and the current pavement maintenance strategy is integrated to obtain the pavement maintenance strategy.

[0021] Furthermore, by sorting the weighted pavement monitoring indicator prediction data by size and deleting the smallest data one by one, and using the remaining weighted pavement monitoring indicator prediction data to update the real-time twin pavement, and then calculating the similarity with the future twin pavement, it is possible to more accurately identify the pavement monitoring indicator combination that has the greatest impact on the future state of the pavement, thereby integrating the current pavement maintenance strategy to obtain a more targeted pavement maintenance strategy, minimizing maintenance costs while ensuring maintenance results.

[0022] In the second aspect, the present invention also provides a pavement defect monitoring and prediction system, which uses a pavement defect monitoring and prediction method provided by the present invention. The system includes: a digital twin model construction module, which is used to construct a pavement digital twin model of the target pavement; a data acquisition and preprocessing module, which is used to collect historical pavement monitoring data to construct a historical pavement monitoring data set, and obtain accurate pavement monitoring indicator data and pavement high-definition image data in real time; a pavement defect monitoring and prediction module, which is used to obtain the indicators of each pavement monitoring indicator based on the pavement digital twin model and the pavement monitoring data set using a dual-source weight fusion method. The system comprises the following components: a) a road surface monitoring index precise data, a road surface index weight, and a road surface high-definition image data; b) updating the road surface digital twin model based on the road surface monitoring index precise data, the index weight, and the road surface high-definition image data to achieve real-time monitoring of target road surface defects; c) predicting the road surface monitoring index precise data, and then predicting the target road surface defects based on the index weight and the road surface digital twin model, and formulating a road surface maintenance strategy; a data storage module, the data storage module is used to store the data collected and generated by the data acquisition and preprocessing module, and can also store the data generated by the road surface defect monitoring and prediction module; and c) an information visualization module, the information visualization module is used to display real-time road surface monitoring data and road surface digital twin information, and can output the road surface maintenance strategy. This system is compatible with the method provided by the present invention, can stably execute the method provided by the present invention, and improve the practical application capability of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic flow chart of a road surface defect monitoring and prediction method according to an embodiment of the present invention; Figure 2 Schematic diagram of the framework of a road surface defect monitoring and prediction system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] Specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the present invention. In the following description, numerous specific details are set forth to provide a thorough understanding of the present invention. However, it will be apparent to one of ordinary skill in the art that these specific details are not necessarily required to practice the present invention. In other instances, well-known circuits, software, or methods are not specifically described to avoid obscuring the present invention.

[0026] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "in one embodiment," "in an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in any suitable combinations and / or subcombinations in one or more embodiments or examples. Furthermore, those of ordinary skill in the art will appreciate that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale.

[0027] 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.

[0028] In an alternative embodiment, see Figure 1 The present invention provides a road surface defect monitoring and prediction method, which includes the following steps: S1. Build a digital twin model of the target road surface.

[0029] Specifically, in this embodiment, SketchUp is used to construct a road surface digital twin model using road geometry information, road texture information, and environmental information in the road design CAD drawings.

[0030] Pavement digital twin models can reflect changes in the physical world of pavement in real time, reflecting current pavement conditions in real time. They can also simulate and predict pavement defects based on historical and real-time data, enabling early detection and prevention of cracks and potholes. Furthermore, pavement digital twin models present complex pavement conditions in an intuitive and accessible manner. Users can use the model to conduct various hypotheses and tests to evaluate the effectiveness of different maintenance plans, leading to more informed decision-making. Furthermore, building pavement digital twin models can make pavement management more data-driven and intelligent, improving both efficiency and effectiveness.

[0031] Furthermore, in other optional embodiments, other software may be used to construct a digital twin model of the road surface, such as 3DS MAX, Revit, and Unity3D.

[0032] S2. Collect historical road surface monitoring data to construct a historical road surface monitoring data set, and obtain accurate road surface monitoring indicator data and high-definition road surface image data in real time.

[0033] Wherein, step S2 specifically includes the following steps: S21. Collect historical road surface monitoring data, and pre-process the historical road surface monitoring data to construct a historical road surface monitoring data set.

[0034] Specifically, in this embodiment, the historical road surface monitoring data includes historical road surface monitoring indicator data and historical road surface image data. Road surface monitoring indicators include stress, strain, displacement, flatness, compaction, rutting depth, friction coefficient, temperature, humidity, average monthly rainfall, average daily traffic volume, average annual truck traffic volume, and cumulative standard axle load application times of the target road surface. Historical road surface image data refers to historical road surface images, and each historical road surface image has the same number of pixels. For specific methods for obtaining road surface monitoring indicator data, reference can be made to existing technologies.

[0035] Furthermore, the historical road surface monitoring data is preprocessed. The specific method of preprocessing can be referred to in the subsequent step S22. Preprocessing the historical road surface monitoring data can make the obtained data accurately reflect the road surface conditions, provide a reliable data basis for the subsequent calculation of indicator weights, and thus improve the accuracy of road surface defect monitoring and prediction.

[0036] S22: Acquire road surface monitoring index data and road surface image data in real time, and perform preprocessing to obtain accurate road surface monitoring index data and high-definition road surface image data.

[0037] Wherein, step S22 specifically includes the following steps: S221. Clean the road surface monitoring indicator data to obtain accurate road surface monitoring indicator data.

[0038] Specifically, in this embodiment, data cleaning includes processing missing values, outliers, duplicate values, and data consistency. This data cleaning ensures that the resulting road surface monitoring indicator data accurately reflects the real-time condition of the target road surface, ensuring the real-time and accurate monitoring of the target road surface using the road surface digital twin model.

[0039] S222: Smoothing and noise reduction processing are performed on the road surface image data, and contrast is enhanced to obtain high-definition road surface image data.

[0040] Specifically, in this embodiment, the mean shift filtering algorithm is used to smooth and reduce noise on the road surface image data, and the CLAHE algorithm is used to enhance the contrast of the road surface image data. This can make the road surface image clearer and smoother, and the details of cracks and potholes are more prominent. At the same time, the edges of cracks and potholes are clearer, so that the obtained high-definition road surface image data can accurately reflect the real-time condition of the target road surface, ensuring the real-time and accuracy of the subsequent monitoring of the target road surface using the road surface digital twin model, and at the same time improving the accuracy of subsequent road surface defect identification.

[0041] S3. According to the pavement digital twin model and the pavement monitoring data set, a dual-source weight fusion method is used to obtain the indicator weights of various pavement monitoring indicators.

[0042] Wherein, step S3 specifically includes the following steps: S31. Perform image recognition on the historical road surface image data in the road surface monitoring data set to obtain road surface defects, where the road surface defects include cracks and potholes.

[0043] Specifically, in this embodiment, the YOLOv7 algorithm is used to identify cracks and potholes on the historical road surface image, and the number of pixels of the historical road surface image occupied by the road surface defects is recorded.

[0044] S32. Calculate the pavement defect evaluation factor value corresponding to each group of pavement monitoring data in the pavement monitoring data set according to the image recognition result.

[0045] Specifically, in this embodiment, the pavement defect evaluation factor value is the ratio of the number of pixels of the pavement defect to the number of pixels of the pavement image. The pavement defect evaluation factor value can intuitively reflect the severity of the pavement defect.

[0046] S33. Based on the pavement monitoring data set and the pavement defect evaluation factor value, use the permutation importance method to obtain the first weight of the pavement monitoring index.

[0047] S34. According to the pavement digital twin model, the pavement monitoring data set and the pavement defect evaluation factor value, use an item-by-item analysis method to obtain a second weight of the pavement monitoring index.

[0048] Wherein, step S34 specifically includes the following steps: S341. Randomly select a group of road surface monitoring data from the road surface monitoring data set as an array to be adjusted, and the remaining groups of road surface monitoring data as adjustment arrays.

[0049] Specifically, in this embodiment, the array to be adjusted is recorded as , is the value of the jth road surface monitoring index in the array to be adjusted, and n is the number of road surface monitoring indicators; the adjustment array is recorded as , is the value of the jth road surface monitoring indicator in the kth adjustment array, k=1,2,3,...,N, and N is the number of adjustment arrays.

[0050] S342. For a certain road surface monitoring indicator, the values ​​of the road surface monitoring indicator in the adjustment array are used to replace the values ​​of the road surface monitoring indicator in the array to be adjusted in sequence to obtain multiple groups of adjusted arrays, and the road surface monitoring indicator is marked as a road surface monitoring change indicator.

[0051] Specifically, in this embodiment, it is assumed that The corresponding road surface monitoring indicators are used as road surface monitoring change indicators, and then used in turn 、 、 、... replace , get N adjusted arrays, namely 、 、 、... Since the number of road surface monitoring indicators is n, n×N adjusted arrays can be obtained in sequence.

[0052] S343. Use the adjusted array to update the pavement digital twin model, identify pavement defects and calculate multiple pavement defect evaluation factor comparison values, and then calculate the second weight of the pavement monitoring change index in combination with the pavement defect evaluation factor values.

[0053] Specifically, in this embodiment, the road surface digital twin model is updated using the adjusted array to obtain the twin road surface corresponding to the adjusted array, and then the road surface image in the twin road surface is extracted and the YOLOv7 algorithm is used to identify road surface defects.

[0054] Furthermore, the number of pixels in the road surface image extracted from the twin road surface should be the same as the number of pixels in the historical road surface image.

[0055] More specifically, the second weight satisfies the following relationship:

[0056] in, is the second weight, is the comparison value of the i-th pavement defect evaluation factor, is the pavement defect evaluation factor value, is the value of the road surface monitoring change index in the i-th adjustment array, It is the value of the road surface monitoring change index in the array to be adjusted.

[0057] S35. Calculate the indicator weight according to the first weight and the second weight.

[0058] Specifically, in this embodiment, the indicator weight is the average of the first and second weights. The first weight is derived using machine learning, while the second weight is derived using statistical testing. Combining the two to obtain the indicator weight avoids the bias inherent in using a single method, improving the accuracy of road surface defect monitoring and prediction results.

[0059] S4. Update the pavement digital twin model based on the precise data of the pavement monitoring indicators, the indicator weights, and the pavement high-definition image data to achieve real-time monitoring of target pavement defects.

[0060] Wherein, step S4 specifically includes the following steps: S41. Calculate weighted road surface monitoring index data based on the road surface monitoring index precision data and the index weights.

[0061] Specifically, in this embodiment, the weighted road surface monitoring index data is the product of the road surface monitoring index precision data and the corresponding index weight.

[0062] S42. Use the weighted pavement monitoring index data and the pavement high-definition image data to update the pavement digital twin model to obtain a real-time twin pavement, thereby achieving real-time monitoring of the target pavement defects.

[0063] Specifically, in this embodiment, by calculating weighted road surface monitoring index data, and then fully considering the impact of different road surface monitoring indicators on the road surface condition, and introducing high-definition road surface image data to realize multi-source fusion of quantitative data and image data, the road surface digital twin model can more comprehensively reflect the actual situation of the road surface, thereby improving the real-time and comprehensiveness of monitoring.

[0064] S5. Predict the precise data of the pavement monitoring indicators, and then predict the target pavement defects based on the indicator weights and the pavement digital twin model, and formulate a pavement maintenance strategy.

[0065] Wherein, step S5 specifically includes the following steps: S51. Predict the precise data of the road surface monitoring indicators to obtain the future indicator prediction data of each of the road surface monitoring indicators.

[0066] Specifically, in this embodiment, a prediction model for each road surface monitoring index is constructed using a BP neural network based on the road surface monitoring data set, and the established prediction model is used to obtain index prediction data.

[0067] Furthermore, in other optional embodiments, the indicator prediction data for road surface monitoring indicators such as temperature, humidity and average monthly rainfall can also be obtained based on the weather forecast of the location of the target road surface.

[0068] S52. Calculate weighted road surface monitoring index prediction data based on the index prediction data and the index weight.

[0069] Specifically, in this embodiment, the method for calculating the weighted road surface monitoring index prediction data is the same as the method for calculating the weighted road surface monitoring index data.

[0070] S53. Use the weighted pavement monitoring index prediction data to update the real-time twin pavement to obtain the future twin pavement, thereby realizing the prediction of the target pavement defects.

[0071] Specifically, in this embodiment, the weighted pavement monitoring index prediction data is used to update the real-time twin pavement to obtain the future twin pavement, and then the future pavement image in the future twin pavement is extracted and the YOLOv7 algorithm is used to identify pavement defects to achieve the prediction of the target pavement defects.

[0072] Furthermore, the number of pixels in the future road surface image should be the same as the number of pixels in the historical road surface image.

[0073] S54: Formulate a current pavement maintenance strategy based on the weighted pavement monitoring index data.

[0074] Specifically, in this embodiment, the weighted pavement monitoring index data corresponding to each pavement monitoring index are divided into three level ranges from the lowest to the highest according to expert opinions, namely, below normal range, normal, and above normal range, and then different response plans are retrieved and formulated for different level ranges of different weighted pavement monitoring index data to form a pavement maintenance plan library, and then the corresponding response plans are queried in the pavement maintenance plan library based on the weighted pavement monitoring index data to form the current pavement maintenance strategy.

[0075] S55. Formulate a future pavement maintenance strategy based on the real-time twin pavement, the weighted pavement monitoring index prediction data and the future twin pavement, and integrate the current pavement maintenance strategy to obtain the pavement maintenance strategy.

[0076] Wherein, step S55 specifically includes the following steps: S551. Arrange the weighted road surface monitoring index prediction data in descending order.

[0077] Specifically, in this embodiment, the weighted road surface monitoring index prediction data are arranged in descending order as the weighted road surface monitoring index prediction data sequence, and are recorded as , is the j-th weighted road surface monitoring index prediction data in the weighted road surface monitoring index prediction data sequence.

[0078] S552. Delete the smallest data in the weighted pavement monitoring index prediction data in sequence, and use the remaining weighted pavement monitoring index prediction data to update the real-time twin pavement after each deletion to obtain a comparative twin pavement, and calculate the similarity between the comparative twin pavement and the future twin pavement.

[0079] Specifically, in this embodiment, taking the first deletion of the smallest data in the weighted road surface monitoring index prediction data as an example, the deletion ,get , then use The real-time twin pavement is updated to obtain a comparison twin pavement. Next, a comparison pavement image of the comparison twin pavement is extracted, and the similarity between the comparison pavement image and the future pavement image extracted in step S53 is calculated. The calculated result is used as the similarity between the comparison twin pavement and the future twin pavement. By sorting the weighted pavement monitoring indicator prediction data by size and deleting the smallest data one by one, and using the remaining weighted pavement monitoring indicator prediction data to update the real-time twin pavement, and then calculating the similarity with the future twin pavement, it is possible to more accurately identify the pavement monitoring indicator combination that has the greatest impact on the future state of the pavement, facilitating the subsequent integration of the current pavement maintenance strategy to obtain a more targeted pavement maintenance strategy, while ensuring maintenance results while minimizing maintenance costs.

[0080] Furthermore, the number of pixels in the comparison road surface image should be the same as the number of pixels in the historical road surface image. In addition, this embodiment uses a structural similarity index to represent the similarity between the comparison road surface image and the future road surface image.

[0081] S553: ​​When the similarity is less than the similarity threshold for the first time, the operation of deleting the weighted road surface monitoring index prediction data is stopped, and the weighted road surface monitoring index prediction data remaining after the last deletion operation is used as the final judgment data.

[0082] Specifically, in this embodiment, the similarity threshold is set to 0.92.

[0083] S554: Formulate a future pavement maintenance strategy based on the final judgment data, and integrate the current pavement maintenance strategy to obtain the pavement maintenance strategy.

[0084] Specifically, in this embodiment, based on the final judgment data obtained, a future pavement maintenance strategy is formulated using the method for formulating a current pavement maintenance strategy, and then the current pavement maintenance strategy is integrated with the future pavement maintenance strategy to obtain a pavement maintenance strategy.

[0085] More specifically, the specific process of integrating the current pavement maintenance strategy with the future pavement maintenance strategy is as follows: for a certain pavement monitoring indicator, determine whether the level range of the corresponding weighted pavement monitoring indicator data is not lower than the level range of the corresponding weighted pavement monitoring indicator prediction data. If so, the response plan for the pavement monitoring indicator in the current pavement maintenance strategy will not be changed. If not, the response plan for the pavement monitoring indicator in the future pavement maintenance strategy will be used to replace the response plan for the pavement monitoring indicator in the current pavement maintenance strategy. After completing the judgment of all pavement monitoring indicators, the modified current pavement maintenance strategy, that is, the final pavement maintenance strategy, can be obtained.

[0086] When formulating pavement maintenance strategies, integrating current pavement maintenance strategies with future pavement maintenance strategies can comprehensively consider the current and future status of the pavement, achieve a combination of long-term planning and short-term response, and formulate more scientific and reasonable maintenance strategies. This will help optimize the allocation of maintenance resources, reduce unnecessary waste, and improve the efficiency and benefits of maintenance work.

[0087] 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.

[0088] In an alternative embodiment, see Figure 2 The present invention provides a pavement defect monitoring and prediction system, which uses a pavement defect monitoring and prediction method provided by the present invention. The system includes a digital twin model construction module A1, a data acquisition and preprocessing module A2, a pavement defect monitoring and prediction module A3, a data storage module A4 and an information visualization module A5.

[0089] The digital twin model construction module A1 is used to construct a road surface digital twin model of the target road surface.

[0090] Specifically, in this embodiment, the digital twin model construction module A1 is electrically connected to the pavement defect monitoring and prediction module A3 and the information visualization module A5 respectively. The digital twin model construction module A1 is based on SketchUp software and uses the road geometry information, road texture information and environmental information in the road design CAD drawings to construct a pavement digital twin model.

[0091] The data acquisition and preprocessing module A2 is used to collect historical road surface monitoring data to construct a historical road surface monitoring data set, and to obtain accurate road surface monitoring indicator data and high-definition road surface image data in real time.

[0092] Specifically, in this embodiment, the data acquisition and preprocessing module A2 specifically executes the content described in step S2.

[0093] More specifically, the data acquisition and preprocessing module A2 includes a digital display and various sensors for collecting road monitoring data. These sensors transmit information to the data acquisition and preprocessing module A2 via the Internet of Things. Data that cannot be obtained using sensors can be input into the data acquisition and preprocessing module A2 through the digital display.

[0094] The pavement defect monitoring and prediction module A3 is used to obtain the indicator weights of each pavement monitoring indicator based on the pavement digital twin model and the pavement monitoring data set using a dual-source weight fusion method; update the pavement digital twin model based on the precise data of the pavement monitoring indicators, the indicator weights and the pavement high-definition image data to achieve real-time monitoring of target pavement defects; predict the precise data of the pavement monitoring indicators, and then predict the target pavement defects based on the indicator weights and the pavement digital twin model, and formulate a pavement maintenance strategy.

[0095] Specifically, in this embodiment, the pavement defect monitoring and prediction module A3 is electrically connected to the digital twin model construction module A1, the data acquisition and preprocessing module A2, the data storage module A4 and the information visualization module A5 respectively. The pavement defect monitoring and prediction module A3 specifically executes the contents described in steps S3-S5, which will not be repeated here.

[0096] The data storage module A4 is used to store the data collected and generated by the data collection and preprocessing module A2, and can also store the data generated by the road surface defect monitoring and prediction module A3.

[0097] Specifically, in this embodiment, the data storage module A4 is electrically connected to the data acquisition and preprocessing module A2, the road surface defect monitoring and prediction module A3, and the information visualization module A5 respectively.

[0098] Furthermore, the data storage module A4 also stores a road maintenance plan library.

[0099] The information visualization module A5 is used to display real-time road surface monitoring data and road surface digital twin information, and can output the road surface maintenance strategy.

[0100] In summary, the method provided by the present invention constructs a pavement digital twin model, presenting complex pavement conditions in an intuitive and easy-to-understand manner, thereby enabling the monitoring and prediction of pavement defects. This method preprocesses pavement monitoring indicator data and pavement image data to accurately reflect the real-time pavement conditions. This enables the updated pavement digital twin model to accurately reflect the real-time pavement conditions, thus achieving accurate monitoring of the pavement. When updating the pavement digital twin model, this method calculates weighted pavement monitoring indicator data by combining precise pavement monitoring indicator data with indicator weights, fully considering the impact of different pavement monitoring indicators on pavement conditions. This enables the pavement digital twin model to more comprehensively reflect the actual pavement conditions, improving the real-time, comprehensive, and accurate nature of monitoring. The indicator weights are obtained by fusing the first and second weights, avoiding the one-sidedness of weights obtained by a single method and further improving the accuracy of pavement defect monitoring and prediction results. After monitoring and predicting pavement defects, this method can also formulate corresponding pavement maintenance strategies, ensuring maintenance effectiveness while minimizing maintenance costs, thereby extending the pavement's service life and improving road safety and traffic capacity. In addition, the system provided by the present invention is compatible with the method provided by the present invention, and can stably execute the method provided by the present invention, thereby improving the practical application capability of the present invention.

[0101] 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions 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 description of the present invention.

Claims

1. A road surface defect monitoring and prediction method, characterized in that: The steps include: Build a pavement digital twin model of the target pavement; Collect historical road surface monitoring data to build a historical road surface monitoring data set, and obtain accurate road surface monitoring indicator data and high-definition road surface image data in real time; According to the pavement digital twin model and the pavement monitoring data set, a dual-source weight fusion method is used to obtain the indicator weight of each pavement monitoring indicator; Based on the precise data of the road surface monitoring index, the index weights and the high-definition image data of the road surface, the road surface digital twin model is updated to achieve real-time monitoring of target road surface defects; The precise data of the pavement monitoring indicators are predicted, and then the target pavement defects are predicted based on the indicator weights and the pavement digital twin model, and a pavement maintenance strategy is formulated.

2. A road surface defect monitoring and prediction method according to claim 1, characterized in that: The historical road surface monitoring data includes historical road surface monitoring index data and historical road surface image data; The collecting of historical road surface monitoring data to construct a historical road surface monitoring data set and obtaining accurate road surface monitoring index data and high-definition road surface image data in real time includes the following steps: Collecting historical road surface monitoring data, and preprocessing the historical road surface monitoring data, thereby constructing a historical road surface monitoring data set; The road surface monitoring index data and the road surface image data are acquired in real time, and preprocessed to obtain the accurate road surface monitoring index data and the high-definition road surface image data.

3. A road surface defect monitoring and prediction method according to claim 2, characterized in that: The real-time acquisition of road surface monitoring index data and road surface image data, and preprocessing to obtain the accurate road surface monitoring index data and the high-definition road surface image data includes the following steps: Performing data cleaning on the road surface monitoring indicator data to obtain accurate data of the road surface monitoring indicator; The road surface image data is smoothed and denoised, and the contrast is enhanced to obtain the road surface high-definition image data.

4. A road surface defect monitoring and prediction method according to claim 2, characterized in that: The step of obtaining the index weights of various road surface monitoring indexes by using a dual-source weight fusion method according to the road surface digital twin model and the road surface monitoring data set comprises the following steps: Performing image recognition on the historical road surface image data in the road surface monitoring data set to obtain road surface defects, wherein the road surface defects include cracks and potholes; Calculate the pavement defect evaluation factor value corresponding to each group of pavement monitoring data in the pavement monitoring data set according to the image recognition result; Based on the pavement monitoring data set and the pavement defect evaluation factor value, using a permutation importance method to obtain a first weight of the pavement monitoring index; Obtaining a second weight of the pavement monitoring index using an item-by-item analysis method according to the pavement digital twin model, the pavement monitoring data set, and the pavement defect evaluation factor value; The indicator weight is calculated according to the first weight and the second weight.

5. A road surface defect monitoring and prediction method according to claim 4, characterized in that: The step of obtaining the second weight of the pavement monitoring index by using an item-by-item analysis method according to the pavement digital twin model, the pavement monitoring data set, and the pavement defect evaluation factor value comprises the following steps: Randomly selecting a group of road surface monitoring data from the road surface monitoring data set as the array to be adjusted, and the remaining groups of road surface monitoring data as the adjustment arrays; For a certain road surface monitoring indicator, the value of the road surface monitoring indicator in the adjustment array is used to replace the value of the road surface monitoring indicator in the array to be adjusted in sequence to obtain multiple groups of adjusted arrays, and the road surface monitoring indicator is marked as a road surface monitoring change indicator; The adjusted array is used to update the pavement digital twin model, pavement defects are identified and multiple pavement defect evaluation factor comparison values ​​are calculated, and then the second weight of the pavement monitoring change index is calculated in combination with the pavement defect evaluation factor values.

6. A road surface defect monitoring and prediction method according to claim 5, characterized in that: The second weight satisfies the following relationship: ， in, is the second weight, N is the number of the adjustment arrays, is the comparison value of the ith road surface defect evaluation factor, is the road surface defect evaluation factor value, is the value of the road surface monitoring change index in the i-th adjustment array, is the value of the road surface monitoring change index in the array to be adjusted.

7. A road surface defect monitoring and prediction method according to claim 1, characterized in that: The updating of the road surface digital twin model based on the road surface monitoring index precise data, the index weight and the road surface high-definition image data to achieve real-time monitoring of target road surface defects includes the following steps: Calculate weighted road surface monitoring index data according to the road surface monitoring index accurate data and the index weight; The weighted road surface monitoring index data and the road surface high-definition image data are used to update the road surface digital twin model to obtain a real-time twin road surface, thereby realizing real-time monitoring of the target road surface defects.

8. A road surface defect monitoring and prediction method according to claim 7, characterized in that: The predicting of the accurate data of the pavement monitoring index, and then predicting the target pavement defects according to the index weights and the pavement digital twin model, and formulating a pavement maintenance strategy includes the following steps: Predict the precise data of the road surface monitoring indicators to obtain the future indicator prediction data of each road surface monitoring indicator; Calculate weighted road surface monitoring index prediction data according to the index prediction data and the index weight; Using the weighted pavement monitoring index prediction data to update the real-time twin pavement to obtain the future twin pavement, thereby realizing the prediction of the target pavement defects; Formulate a current pavement maintenance strategy based on the weighted pavement monitoring indicator data; A future pavement maintenance strategy is formulated based on the real-time twin pavement, the weighted pavement monitoring index prediction data and the future twin pavement, and the current pavement maintenance strategy is integrated to obtain the pavement maintenance strategy.

9. A road surface defect monitoring and prediction method according to claim 8, characterized in that: The step of formulating a future pavement maintenance strategy based on the real-time twin pavement, the weighted pavement monitoring index prediction data and the future twin pavement, and integrating the current pavement maintenance strategy to obtain the pavement maintenance strategy includes the following steps: Arrange the weighted road surface monitoring index prediction data in descending order; Deleting the smallest data in the weighted road surface monitoring index prediction data in sequence, and using the remaining weighted road surface monitoring index prediction data after each deletion to update the real-time twin road surface to obtain a comparison twin road surface, and calculating the similarity between the comparison twin road surface and the future twin road surface; When the similarity is less than the similarity threshold for the first time, the operation of deleting the weighted road surface monitoring index prediction data is stopped, and the weighted road surface monitoring index prediction data remaining after the last deletion operation is used as the final judgment data; A future pavement maintenance strategy is formulated according to the final judgment data, and the current pavement maintenance strategy is integrated to obtain the pavement maintenance strategy.

10. A road surface defect monitoring and prediction system, the system using a road surface defect monitoring and prediction method according to any one of claims 1 to 9, characterized in that: The system comprises: A digital twin model construction module, wherein the digital twin model construction module is used to construct a road surface digital twin model of a target road surface; A data acquisition and preprocessing module, which is used to collect historical road surface monitoring data to construct a historical road surface monitoring data set, and obtain accurate road surface monitoring indicator data and high-definition road surface image data in real time; A pavement defect monitoring and prediction module, the pavement defect monitoring and prediction module is used to obtain the indicator weights of various pavement monitoring indicators using a dual-source weight fusion method based on the pavement digital twin model and the pavement monitoring data set; update the pavement digital twin model based on the pavement monitoring indicator precise data, the indicator weights and the pavement high-definition image data to achieve real-time monitoring of target pavement defects; predict the pavement monitoring indicator precise data, and then predict the target pavement defects based on the indicator weights and the pavement digital twin model, and formulate a pavement maintenance strategy; A data storage module, the data storage module is used to store the data collected and generated by the data collection and preprocessing module, and can also store the data generated by the road surface defect monitoring and prediction module; An information visualization module, which is used to display real-time pavement monitoring data and pavement digital twin information, and can output the pavement maintenance strategy.

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