A method, system, equipment and medium for determining the construction sequence of highway maintenance

Abstract

The present invention provides a method, system, device, and medium for determining the construction sequence for highway maintenance, belonging to the technical field of highway maintenance. The method comprises the following steps: acquiring image data of a road section to be maintained, inputting the image data into a pavement defect detection model, and using the pavement defect detection model to detect the type, location, and severity of pavement defects; partitioning the area to be maintained according to the type and location of the pavement defects to obtain multiple sub-areas; obtaining the actual traffic area of ​​the area to be maintained before blocking; setting a traffic speed threshold for the road section to be maintained after blocking, and calculating the maximum traffic area of ​​each sub-area after blocking based on the actual traffic area; determining the maintenance sequence of the multiple sub-areas according to the maximum traffic area and the severity of the pavement defects, and maintaining the multiple sub-areas one by one according to the maintenance sequence. The present invention can improve traffic congestion during highway maintenance.

Description

Technical Field

[0001] The present invention belongs to the technical field of highway maintenance, and in particular relates to a method, system, equipment and medium for determining a road construction sequence for highway maintenance. Background Art

[0002] With rapid economic development, and particularly the increase in transportation demand, highway construction has seen a significant boost worldwide. In China in particular, highway construction has not only supported rapid economic growth but also driven urbanization. By the end of 2023, my country's total highway mileage reached over 5.4 million kilometers. However, due to factors such as traffic volume and the environment, highway pavements can become damaged over time. Repeated traffic on damaged pavements can cause severe damage and compromise traffic safety. Therefore, timely maintenance of damaged road pavement defects is crucial.

[0003] Currently, after detecting road defects, when maintaining the road defects, the area requiring maintenance will be blocked as a whole, and then maintenance will be carried out; however, when using this method for blocking, if the blocked area is large, it will cause large-scale congestion during peak traffic hours, thereby affecting the normal operation of traffic. Summary of the Invention

[0004] In order to overcome the above-mentioned problem of large-scale road congestion during highway maintenance, the present invention provides a method for determining a road construction sequence for highway maintenance, comprising the following steps:

[0005] Acquire image data of the road section to be maintained, analyze the image data using a pavement defect detection model, and obtain identification information of the area to be maintained on the road section to be maintained, wherein the identification information includes the category, location, and severity of multiple pavement defects in the area to be maintained; and partition the area to be maintained according to the category and location of the multiple pavement defects to obtain multiple sub-areas.

[0006] Obtaining the peak traffic flow and maximum lane capacity of the road section to be maintained in the maintenance area before the road section is blocked, and determining the actual traffic area of ​​the road section to be maintained in the maintenance area before the road section is blocked based on the peak traffic flow and maximum lane capacity;

[0007] Setting a speed threshold for the road section to be maintained after the maintenance area is blocked, and calculating the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked, based on the actual traffic area and the speed threshold;

[0008] The maintenance sequence of the multiple sub-areas is determined according to the maximum traffic area and the severity of the road surface defects, and the multiple sub-areas are maintained area by area according to the maintenance sequence.

[0009] Preferably, the determining of the actual traffic area of ​​the road section to be maintained before the maintenance area is blocked based on the peak traffic flow and the maximum lane capacity comprises the following steps:

[0010] Obtain the road length of the road section to be maintained before it is blocked; calculate the lane occupancy coefficient based on the road length of the road section to be maintained before it is blocked, traffic flow, and maximum lane capacity;

[0011] Obtain the road width, road length and lane occupancy coefficient of the road section to be maintained before it is blocked to determine the actual traffic area of ​​the road section to be maintained before it is blocked.

[0012] Preferably, the calculation of the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked comprises the following steps:

[0013] Obtain the area of ​​the sub-region, and determine the traffic area of ​​the road section to be maintained after blocking based on the difference between the actual traffic area of ​​the road section to be maintained before blocking and the area of ​​the sub-region;

[0014] The traffic capacity saturation corresponding to the traffic area of ​​the road section to be maintained after blocking is obtained, and the maximum traffic area corresponding to the traffic speed threshold after blocking of the sub-area is determined according to the traffic area of ​​the road section to be maintained after blocking and the corresponding traffic capacity saturation.

[0015] Preferably, determining the maintenance order of the multiple sub-areas according to the maximum traffic area and the severity of the road surface defects includes the following steps:

[0016] The severity of the road surface defects in each sub-area is graded from severe to mild, and a value is assigned to each grade;

[0017] Perform a weighted summation of the maximum traffic area and road surface defect severity level after blocking each sub-area;

[0018] The weighted sum values ​​are sorted from largest to smallest, and each sub-region is maintained in this order.

[0019] Preferably, the weighted sum of the maximum traffic area and the severity level of the road surface defects after blocking each sub-area is performed, specifically using the following formula:

[0020] Q=ω1A i +ω2D i ;

[0021] Where Q is the weighted sum value; ω1 is the weight of the maximum traffic area, ω2 is the weight of the severity level of the road surface defect, and A is the weight of the road surface defect severity level. i is the maximum traffic area of ​​the ith sub-area, D iis the severity level score of the road surface defect in the i-th sub-area.

[0022] Preferably, the method for obtaining the pavement defect detection model is: obtaining a pavement defect dataset, inputting data in the pavement defect dataset into a convolutional neural network model Faster R-CNN, training Faster R-CNN, and obtaining the pavement defect detection model.

[0023] Preferably, the step of acquiring image data of the road section to be maintained, analyzing the image data using a road surface defect detection model, and obtaining identification information of the area to be maintained on the road section to be maintained comprises the following steps:

[0024] Obtain image data of the road section to be maintained through drones;

[0025] The image data is preprocessed using image processing technology, specifically: denoising the image data, enhancing contrast, and adjusting resolution.

[0026] The pre-processed image data is input into the pavement defect detection model, and the image is analyzed by the pavement defect detection model to detect multiple pavement defects in the area to be maintained;

[0027] Classify multiple pavement defects and annotate the defect type, location, and severity.

[0028] The present invention also provides a highway maintenance road construction sequence determination system, comprising:

[0029] A partitioning module is used to obtain image data of the road section to be maintained, analyze the image data using a pavement defect detection model, and obtain identification information of the area to be maintained on the road section to be maintained, wherein the identification information includes the category, location, and severity of multiple pavement defects in the area to be maintained; and partition the area to be maintained according to the category and location of the multiple pavement defects to obtain multiple sub-areas;

[0030] An actual traffic area acquisition module is used to obtain the peak traffic flow and maximum lane capacity of the road section to be maintained in the maintenance area before it is blocked, and determine the actual traffic area of ​​the road section to be maintained in the maintenance area before it is blocked based on the peak traffic flow and maximum lane capacity;

[0031] A maximum traffic area acquisition module is used to set a traffic speed threshold for the road section to be maintained after the maintenance area is blocked, and calculate the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked based on the actual traffic area and the traffic speed threshold;

[0032] The maintenance sequence confirmation module is used to determine the maintenance sequence of multiple sub-areas according to the maximum traffic area and the severity of the road surface defects, and to perform maintenance on the multiple sub-areas one by one according to the maintenance sequence.

[0033] The present invention also provides a computer device, characterized in that it includes a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the highway maintenance road construction sequence determination method.

[0034] The present invention also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the highway maintenance road construction sequence determination method.

[0035] The method, system, equipment and medium for determining the construction sequence of highway maintenance provided by the present invention have the following beneficial effects:

[0036] The present invention can detect the categories, locations and severity of multiple road surface defects by using a road surface defect detection model; the maintenance area of ​​the road section to be maintained can be divided into multiple sub-areas according to the categories and locations of the multiple road surface defects; this process can decompose the overall area to be maintained into multiple small areas, thereby reducing the area of ​​each blockage and reducing traffic congestion; the maximum traffic area corresponding to the sub-area blocked each time can be determined by the actual traffic area and the traffic speed threshold; the maintenance order of multiple sub-areas can be determined by the maximum traffic area and the severity of the road surface defects. Through this process, different sub-areas can be maintained one by one, and traffic congestion can be improved while ensuring traffic safety, so that vehicles can pass smoothly during peak traffic hours. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] To more clearly illustrate the embodiments of the present invention and its design, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort.

[0038] Figure 1 This is a flow chart of a method for determining a road construction sequence for highway maintenance according to an embodiment of the present invention;

[0039] Figure 2 This is a status diagram of the overall blocking of the area to be maintained;

[0040] Figure 3 A state diagram for dividing the area to be maintained into multiple sub-areas;

[0041] Figure 4This is the status diagram of the first maintenance sub-area. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the technical solution of the present invention and to be able to implement it, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention and are not intended to limit the scope of protection of the present invention.

[0043] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the technical solutions of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0044] In addition, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance. In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "connected" and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meaning of the above terms in the present invention can be understood according to the specific circumstances. In the description of the present invention, unless otherwise specified, "plurality" means two or more, which will not be described in detail here.

[0045] Example

[0046] The present invention provides a method for determining the construction sequence of a highway maintenance road, specifically as follows Figure 1 As shown, the following steps are included:

[0047] Step 1: Obtain image data of the road section to be maintained, analyze the image data using a pavement defect detection model, and obtain identification information of the area to be maintained on the road section. The identification information includes the category, location, and severity of multiple pavement defects in the area to be maintained; partition the area to be maintained according to the category and location of the multiple pavement defects to obtain multiple sub-areas. The specific steps include the following:

[0048] (1) Obtain a pavement defect dataset, input the data in the pavement defect dataset into the convolutional neural network model Faster R-CNN, train Faster R-CNN, and obtain a pavement defect detection model.

[0049] The road surface defect dataset of the present invention is a Kaggle dataset on a public data platform.

[0050] Faster R-CNN (Region-based Convolutional Neural Network) is a convolutional neural network model for object detection that can simultaneously localize (i.e., detect the location of an object) and classify (i.e., identify the category of an object). Faster R-CNN is developed based on R-CNN (Region-based CNN) and Fast R-CNN. Its main innovation is the introduction of the Region Proposal Network (RPN) to replace the traditional manual selection of region proposals, thereby significantly improving detection efficiency. The Region Proposal Network (RPN) is one of the key innovations of Faster R-CNN. The RPN scans the feature map of the entire image using a sliding window to generate a set of candidate regions (i.e., region proposals). These region proposals are areas that may contain objects. The RPN generates candidate regions by classifying these regions as foreground / background. After generating the candidate regions, the ROI pooling (Region of Interest Pooling) operation converts these candidate regions into fixed-sized feature maps, which are then further processed by fully connected layers for classification and regression. On the feature map after ROI pooling, the classifier is responsible for identifying the object category in each candidate region, and the regressor is responsible for adjusting the position of the candidate box so that it surrounds the object more accurately.

[0051] This method divides the road surface defect dataset into a training set and a validation set in a 9:1 ratio. Faster R-CNN is trained by inputting the image data from the training set into it. The loss function used in Faster R-CNN training typically consists of two parts: a classification loss (classification accuracy) and a regression loss (box prediction accuracy). After training, the validation set is used to evaluate the model's accuracy. This method uses the evaluation metrics mAP (mean Average Precision), Precision, and Recall to evaluate the model's accuracy, ultimately resulting in a road surface defect detection model.

[0052] (2) Obtain image data of the road section to be maintained, analyze the image data using a pavement defect detection model, and obtain identification information of the area to be maintained on the road section to be maintained. The identification information includes the category, location, and severity of multiple pavement defects in the area to be maintained. The specific steps include:

[0053] Image data of the road section to be maintained is obtained through equipment such as drones or cameras. The obtained image data needs to have sufficient resolution and clarity so that the subsequent defect detection model can accurately identify problems on the road surface.

[0054] After acquiring image data, image preprocessing techniques (such as OpenCV and image enhancement algorithms) are required. These include denoising, contrast enhancement, cropping regions of interest, and resolution adjustment to ensure the image data is suitable for input into the defect detection model. Image preprocessing can improve image quality, remove unnecessary background or noise, and maintain or enhance the visibility of road surface defects in the image.

[0055] The pre-processed image data is input into the pavement defect detection model. The model analyzes the image and detects multiple pavement defects in the area to be maintained. The detected pavement defects are classified (such as cracks, potholes, potholes, etc.) and the location (such as coordinate boxes) and severity of the defects are marked. The output of the pavement defect detection model is used to obtain the specific defect type, location, and severity of the road section to be maintained. During the highway maintenance process, different types of defects have different corresponding treatment methods. The following are different highway defects and their corresponding maintenance measures:

[0056] Rutting: Rutting is the subsidence or plastic deformation of the road surface caused by traffic loads, typically occurring on roads with high traffic volumes and speeds. Rutting typically requires repair by excavation, milling, or adding a new layer, and cold or hot regeneration as necessary.

[0057] Cracks: Cracks in pavement are caused by factors such as temperature fluctuations, loads, and material aging. Common crack types include transverse cracks, longitudinal cracks, and web cracks. Crack repair can be achieved through techniques such as caulking (filling) and adding a sealing layer.

[0058] Potholes: Potholes are localized damage caused by factors such as damaged pavement base, water infiltration, and climatic factors (such as freeze-thaw). They often form depressions of varying depths on the roadway. Pothole repair methods include milling and cleaning the damaged area, adding new material and compacting it, and repairing the base. Potholes generally require immediate attention upon discovery. Failure to repair them promptly can lead to further deterioration of the road surface, compromising traffic safety.

[0059] Warping or uneven road surface: Uneven or warping road surface deformation is usually caused by poor construction quality, excessive temperatures, traffic loads, etc. Warping and unevenness usually require milling and resurfacing, or local cold recycling and overlay.

[0060] Surface loosening: Surface loosening refers to the loss of particles due to wear and tear on cement concrete or asphalt pavement, and is common in asphalt pavements. Repair methods include resurfacing, deep sealing, and spraying adhesives.

[0061] Settlement: Settlement refers to the phenomenon of partial sinking of the road surface due to uneven foundation settlement, improper construction, etc. It is usually repaired by strengthening the roadbed, repairing the base layer, and adding pavement.

[0062] The present invention can detect the specific type and specific location of the road surface defects in the road section to be maintained by inputting the acquired image data of the road section to be maintained into a road surface defect detection model.

[0063] (3) The maintenance area of ​​the road section to be maintained is divided into multiple sub-areas according to various road surface defects.

[0064] After the specific type and specific location of the road surface defects of the road section to be maintained are detected, the area to be maintained of the road section to be maintained is divided into multiple sub-areas. Figure 2 A is the overall blocking area) is divided into multiple sub-areas. Figure 3 (1), (2), (3), (4) and (5) shown in Figure 2 The A region in the image is divided into multiple sub-regions, wherein non-adjacent sub-regions can be of the same type, for example, Figure 3 (1) and (5) in .

[0065] Step 2: Obtain the peak traffic flow and maximum lane capacity of the road section in the maintenance area before it is blocked, and determine the actual traffic area of ​​the road section in the maintenance area before it is blocked based on the peak traffic flow and maximum lane capacity. This specifically includes the following steps:

[0066] Obtain the road length of the maintenance area of ​​the road section before being blocked; calculate the lane occupancy coefficient based on the road length of the maintenance area of ​​the road section before being blocked, traffic flow, and maximum lane capacity.

[0067] Use distance measuring tools or GPS tracking systems to accurately measure the road length corresponding to the maintenance area of ​​the road section to be maintained.

[0068] Traffic flow data is an important indicator of road traffic conditions. It typically refers to the number of vehicles passing through a lane per unit time, typically expressed in vehicles per hour (vph). Specifically, traffic flow data for the length of road corresponding to the maintenance area can be collected using traffic monitoring system sensors (such as pavement sensors, video surveillance, and traffic radar) to monitor traffic flow in real time.

[0069] Maximum lane capacity refers to the maximum number of vehicles a lane can accommodate under ideal conditions. It is typically determined based on factors such as lane width, lane type, and speed. For a single lane on a regular highway, the maximum lane capacity is generally between 2,000 and 2,500 vehicles per hour (capacity varies depending on the region and road section).

[0070] The lane occupancy factor is a factor used to adjust the traffic area, typically representing the actual proportion of lane occupancy. Calculating the lane occupancy factor helps traffic management departments assess the traffic density of a road section, allowing them to make appropriate maintenance or improvement decisions to ensure smooth and safe traffic flow. The specific calculation formula is as follows:

[0071] Lane occupancy factor = traffic flow ÷ maximum lane capacity;

[0072] (2) Obtain the road width, road length, and lane occupancy coefficient of the road section to be maintained before blocking to determine the actual traffic area of ​​the road section to be maintained before blocking. The calculation formula for the actual traffic area is as follows:

[0073] S = H × L × α;

[0074] Where H and L are the total road width and total road length of the road section to be maintained before blocking, respectively, and α is the lane occupancy coefficient.

[0075] Step 3: Set the speed threshold for the road section to be maintained after it is blocked. Combined with the actual traffic area and the speed threshold, calculate the maximum traffic area of ​​each sub-area after blocking.

[0076] In traffic management and maintenance projects, the speed threshold after a road closure is a key factor used to determine whether traffic can maintain a relatively smooth flow after a road closure or partial closure. This threshold is usually determined based on factors such as the road design type, traffic volume, degree of closure, and surrounding environment. The speed threshold settings for different road types are as follows:

[0077] Urban roads: For main arterial roads or busy roads in cities, smooth traffic is expected after they are blocked. A speed threshold of 20-30 km / h is usually set. This speed can ensure that vehicles can flow smoothly even in heavy traffic.

[0078] Suburban roads or secondary roads: For secondary roads or suburban roads with low traffic volume, the speed threshold can usually be set at 30-40 km / h, which can ensure that traffic is not too congested in most cases.

[0079] Expressways or arterial roads: For blockages on expressways or arterial roads, the speed threshold is typically set at 60 km / h or higher. This blockage has minimal impact on traffic, allowing vehicles to maintain higher speeds.

[0080] Calculating the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked includes the following steps:

[0081] The area of ​​the sub-region is obtained, and the traffic area of ​​the road section to be maintained after blocking is determined based on the difference between the actual traffic area of ​​the road section to be maintained before blocking and the area of ​​the sub-region.

[0082] Obtain the traffic capacity saturation corresponding to the traffic area of ​​the road section to be maintained after blocking, and determine the maximum traffic area corresponding to the traffic speed threshold after blocking the sub-area based on the traffic area of ​​the road section to be maintained after blocking and the corresponding traffic capacity saturation.

[0083] Traffic capacity saturation refers to the ratio of the actual traffic flow after the maintenance section is blocked to the theoretical maximum traffic flow. Assuming that the traffic capacity of the maintenance section is proportional to the traffic area after the blockage, the maximum traffic area is calculated as follows:

[0084]

[0085] Where A i is the maximum traffic area of ​​the ith sub-area, S i is the maximum traffic area of ​​the ith sub-area, V i is the traffic capacity saturation of the ith sub-area, v u is the speed threshold, and n is the number of lanes.

[0086] Step 4: Determine the maintenance order of multiple sub-areas based on the maximum traffic area and the severity of the road surface defects, and perform maintenance on each sub-area according to the maintenance order. The specific steps include:

[0087] (1) The severity of the road surface defects in each sub-area is graded in order from heavy to light, and each grade is assigned a value, as shown in Table 1. As can be seen from Table 1, each type of defect is divided into 5 grades, from 1 (lightest) to 5 (heaviest), indicating the severity of the defect. The higher the grade, the greater the impact of the defect on traffic safety, driving comfort and road surface structure. According to the severity of the defect, each grade is assigned a corresponding value (0-5). The larger the value, the more serious the defect and the greater the impact. According to the grade of each defect type in each sub-area, combined with the degree of impact of each defect type, it can be obtained

[0088] Table 1 Pavement defect grade classification

[0089]

[0090] This grading system generates an overall pavement defect score, providing a scientific basis for road maintenance and repair, determining whether immediate repair, priority repair, or routine maintenance is required. This grading system allows for quantitative assessment of pavement defects across different road sections and sub-areas, providing intuitive and systematic data support for maintenance management.

[0091] (2) The weighted sum of the maximum traffic area and the severity level of the road surface defects after blocking each sub-area is calculated using the following formula:

[0092] Q=ω1A i +ω2D i ;

[0093] Where Q is the weighted sum value; ω1 is the weight of the maximum traffic area, ω2 is the weight of the severity level of the road surface defect, and A is the weight of the road surface defect severity level. i is the maximum traffic area of ​​the ith sub-area, D i is the severity level score of the road surface defect in the i-th sub-area.

[0094] (3) Sort the weighted sum values ​​from large to small, and maintain each sub-area in this order.

[0095] For example, Figure 3 After calculating the sub-areas in the above calculation method, the obtained plugging maintenance order is: (1), (3), (2), (5) and (4), among which the plugging status of sub-area (1) is shown in Figure 4 .

[0096] The present invention also provides a highway maintenance road construction sequence determination system, comprising:

[0097] A partitioning module is used to obtain image data of the road section to be maintained, analyze the image data using a pavement defect detection model, and obtain identification information of the area to be maintained on the road section to be maintained. The identification information includes the category, location, and severity of multiple pavement defects in the area to be maintained; and partition the area to be maintained according to the category and location of the multiple pavement defects to obtain multiple sub-areas.

[0098] The actual traffic area acquisition module is used to obtain the peak traffic flow and maximum lane capacity of the road section to be maintained in the maintenance area before it is blocked, and determine the actual traffic area of ​​the road section to be maintained in the maintenance area before it is blocked based on the peak traffic flow and maximum lane capacity;

[0099] The maximum traffic area acquisition module is used to set the traffic speed threshold of the road section to be maintained after the maintenance area is blocked, and calculate the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked based on the actual traffic area and the traffic speed threshold;

[0100] The maintenance sequence confirmation module is used to determine the maintenance sequence of multiple sub-areas according to the maximum traffic area and the severity of road defects, and to maintain the multiple sub-areas one by one according to the maintenance sequence.

[0101] The present invention also provides a computer device, characterized in that it includes a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the method for determining the road construction sequence of highway maintenance.

[0102] The present invention also provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the method for determining the road construction sequence of highway maintenance.

[0103] As can be seen from the above description, the present invention can detect the categories, locations and severity of multiple pavement defects by using a pavement defect detection model; the maintenance area of ​​the maintenance section can be partitioned according to the categories and locations of multiple pavement defects to obtain multiple sub-areas; this process can decompose the overall area to be maintained into multiple small areas, thereby reducing the area of ​​each blockage and reducing traffic congestion; the maximum traffic area corresponding to the sub-area blocked each time can be determined by the actual traffic area and the traffic speed threshold; the maintenance order of multiple sub-areas can be determined by the maximum traffic area and the severity of the pavement defects. Through this process, different sub-areas can be maintained one by one, and traffic congestion can be improved while ensuring traffic safety, so that vehicles can pass smoothly during peak traffic hours.

[0104] The above embodiments are only preferred specific implementation methods of the present invention, and the protection scope of the present invention is not limited thereto. Any simple changes or equivalent replacements of the technical solutions that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention fall within the protection scope of the present invention.

Claims

1. A method for determining the construction sequence of a highway maintenance road, characterized in that: The steps include: Acquire image data of the road section to be maintained, analyze the image data using a pavement defect detection model, and obtain identification information of the area to be maintained on the road section to be maintained, wherein the identification information includes the type, location, and severity of multiple pavement defects in the area to be maintained; The maintenance area is divided into multiple sub-areas according to the types and locations of multiple pavement defects; Obtaining the peak traffic flow and maximum lane capacity of the road section to be maintained in the maintenance area before the road section is blocked, and determining the actual traffic area of ​​the road section to be maintained in the maintenance area before the road section is blocked based on the peak traffic flow and maximum lane capacity; Setting a speed threshold for the road section to be maintained after the maintenance area is blocked, and calculating the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked, based on the actual traffic area and the speed threshold; The maintenance sequence of the multiple sub-areas is determined according to the maximum traffic area and the severity of the road surface defects, and the multiple sub-areas are maintained area by area according to the maintenance sequence.

2. The method for determining the construction sequence of a highway maintenance road according to claim 1, characterized in that: Determining the actual traffic area of ​​the road section to be maintained before blocking the maintenance area based on the peak traffic flow and the maximum lane capacity includes the following steps: Obtain the road length of the road section to be maintained before it is blocked; calculate the lane occupancy coefficient based on the road length of the road section to be maintained before it is blocked, traffic flow, and maximum lane capacity; Obtain the road width, road length and lane occupancy coefficient of the road section to be maintained before it is blocked to determine the actual traffic area of ​​the road section to be maintained before it is blocked.

3. The method for determining the construction sequence of a highway maintenance road according to claim 2, characterized in that: The calculation of the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked comprises the following steps: Obtain the area of ​​the sub-region, and determine the traffic area of ​​the road section to be maintained after blocking based on the difference between the actual traffic area of ​​the road section to be maintained before blocking and the area of ​​the sub-region; The traffic capacity saturation corresponding to the traffic area of ​​the road section to be maintained after blocking is obtained, and the maximum traffic area corresponding to the traffic speed threshold after blocking of the sub-area is determined according to the traffic area of ​​the road section to be maintained after blocking and the corresponding traffic capacity saturation.

4. The method for determining the construction sequence of a highway maintenance project according to claim 3, wherein: The step of determining the maintenance order of the plurality of sub-areas according to the maximum traffic area and the severity of the road surface defects comprises the following steps: The severity of the road surface defects in each sub-area is graded from severe to mild, and a value is assigned to each grade; Perform a weighted summation of the maximum traffic area and road surface defect severity level after blocking each sub-area; The weighted sum values ​​are sorted from largest to smallest, and each sub-region is maintained in this order.

5. The method for determining the construction sequence of highway maintenance according to claim 4, characterized in that: The weighted sum of the maximum traffic area and the severity level of the road surface defects after blocking each sub-area is performed using the following formula: Q6ω1A i +ω2D i 100. Where Q is the weighted sum value; ω1 is the weight of the maximum traffic area, ω2 is the weight of the severity level of the road surface defect, and A is the weight of the road surface defect severity level. i is the maximum traffic area of ​​the ith sub-area, D i is the severity level score of the road surface defect in the i-th sub-area.

6. The method for determining the construction sequence of a highway maintenance project according to claim 1, wherein: The method for obtaining the pavement defect detection model comprises: obtaining a pavement defect dataset, inputting data in the pavement defect dataset into a convolutional neural network model FasterR-CNN, training FasterR-CNN, and obtaining the pavement defect detection model.

7. The method for determining the construction sequence of a highway maintenance project according to claim 1, wherein: The method of acquiring image data of the road section to be maintained and analyzing the image data using a road surface defect detection model to obtain identification information of the area to be maintained on the road section to be maintained includes the following steps: Obtain image data of the road section to be maintained through drones; Preprocessing the image data using image processing technology, specifically: removing noise from the image data, enhancing contrast, and adjusting resolution; The pre-processed image data is input into the pavement defect detection model, and the image is analyzed by the pavement defect detection model to detect multiple pavement defects in the area to be maintained; Classify multiple pavement defects and annotate the defect type, location, and severity.

8. A highway maintenance road construction sequence determination system, characterized in that: include: A partitioning module is used to obtain image data of the road section to be maintained, analyze the image data using a pavement defect detection model, and obtain identification information of the area to be maintained on the road section to be maintained, wherein the identification information includes the type, location, and severity of multiple pavement defects in the area to be maintained; The maintenance area is divided into multiple sub-areas according to the types and locations of multiple pavement defects; An actual traffic area acquisition module is used to obtain the peak traffic flow and maximum lane capacity of the road section to be maintained in the maintenance area before it is blocked, and determine the actual traffic area of ​​the road section to be maintained in the maintenance area before it is blocked based on the peak traffic flow and maximum lane capacity; A maximum traffic area acquisition module is used to set a traffic speed threshold for the road section to be maintained after the maintenance area is blocked, and calculate the maximum traffic area of ​​the road section to be maintained after each sub-area is blocked based on the actual traffic area and the traffic speed threshold; The maintenance sequence confirmation module is used to determine the maintenance sequence of multiple sub-areas according to the maximum traffic area and the severity of the road surface defects, and to maintain the multiple sub-areas one by one according to the maintenance sequence.

9. A computer device, characterized in that: It comprises a memory and a processor; the memory stores a computer program, and the processor is used to run the computer program in the memory to execute the method for determining the road construction sequence of highway maintenance according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor to execute the method for determining the road construction sequence of highway maintenance according to any one of claims 1 to 7.

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