Road surface flatness repairing method based on scheduling algorithm and related equipment

Abstract

The invention provides a road surface flatness repairing method based on a scheduling algorithm and related equipment, and relates to the field of road engineering. The method comprises the following steps: acquiring image data of a target road, dividing a road surface region and sub-road surface regions according to a preset region size, and coding the sub-road surface regions; and then obtaining point cloud data of the sub-pavement areas, dividing the point cloud data into horizontal and vertical column point clouds according to the width and length of the sub-pavement areas, and determining the pavement flatness according to the difference value of the key point coordinates of each column of point clouds. And determining the sub-pavement area with the flatness lower than the first threshold value or higher than the second threshold value as a to-be-repaired position, and determining a to-be-repaired category. And then candidate maintenance vehicles with the distances to the to-be-repaired position being within a set range and matched with the to-be-repaired position in category are determined, a target maintenance vehicle is determined from the candidate maintenance vehicles in combination with a departure time priority algorithm, and finally a maintenance task is sent to the target maintenance vehicle. Accurate detection and efficient repair of the pavement flatness are realized.

Description

Technical Field

[0001] The present application relates to the field of road engineering, and in particular to a road surface smoothness repair method based on a scheduling algorithm and related equipment. Background Art

[0002] With the rapid development of the transportation industry, the smoothness of roads is crucial to driving safety and comfort. Ensuring that roads maintain good smoothness has become a key task in road maintenance.

[0003] At present, the detection and maintenance of road surface flatness mostly rely on manual inspections and regular maintenance. We can also use cameras installed on patrol vehicles to collect road images, and use simple image processing technology to detect obvious damage such as road cracks and potholes. With the help of image information, we can judge the road condition, classify and locate the road surface, so as to repair the uneven areas in a targeted manner.

[0004] However, manual inspections require a lot of time and manpower, and it is difficult to fully cover all road areas. Using image information to judge road conditions makes it difficult to quantitatively analyze the tiny surface deformations of the road surface, and it is difficult to quantify refined indicators such as road surface smoothness. Summary of the invention

[0005] The present application provides a road surface smoothness repair method and related equipment based on a scheduling algorithm, which is used to realize automatic, accurate detection and efficient repair of road surface smoothness, while the road surface smoothness detection and maintenance in the prior art rely on manual inspections and regular repairs, which have problems such as low efficiency, limited accuracy, and lack of scientificity and timeliness in maintenance scheduling.

[0006] In a first aspect, the present application provides a method for repairing road surface flatness based on a scheduling algorithm, which is applied to a server. The method includes: after acquiring image data of a target road through an image acquisition device, dividing the target road into a plurality of road surface areas according to a preset area size according to the image data, and dividing each road surface area into a plurality of sub-road surface areas; encoding the plurality of sub-road surface areas according to a preset encoding rule to obtain a road surface code; after acquiring point cloud data corresponding to each sub-road surface area, dividing the point cloud data into a plurality of horizontal point clouds and vertical point clouds according to the width and length of the sub-road surface area; based on each horizontal point cloud, The road surface flatness of the sub-road surface area is determined by the difference in key point coordinates of the cloud and the vertical row point cloud; the sub-road surface area with a road surface flatness lower than a first threshold or higher than a second threshold is determined as a position to be repaired, and the second threshold is greater than the first threshold; the category to be repaired that matches the position to be repaired is determined according to the road surface flatness; a candidate maintenance vehicle that is within a set range of the distance from the position to be repaired and matches the category to be repaired is determined; after determining a target maintenance vehicle from the candidate maintenance vehicles in combination with an algorithm that prioritizes vehicle departure time, a maintenance task is sent to the target maintenance vehicle so that the target maintenance vehicle goes to the corresponding sub-road surface area for repair.

[0007] By adopting the above technical solution, the road surface area and sub-road surface area are divided according to the image data and encoded, which helps to accurately identify and locate the location that needs to be repaired. Then, the road surface flatness is determined by the difference between the point cloud data and the coordinates of the key points, and the location to be repaired is accurately found. Next, the category to be repaired is determined according to the road surface flatness, and then the matching candidate repair vehicle is determined. Finally, the target repair vehicle is determined and the repair task is sent, which realizes accurate repair scheduling, improves the repair efficiency and quality, and ensures that the road surface flatness is effectively improved.

[0008] In combination with some embodiments of the first aspect, in some embodiments, after obtaining the point cloud data corresponding to each sub-road surface area, after the step of dividing the point cloud data into multiple horizontal column point clouds and vertical column point clouds according to the width and length of the sub-road surface area, it also includes: after extracting the elevation coordinate values ​​of all the point cloud data points in the vertical column point cloud, using the variance of the corresponding elevation coordinate values ​​as the vertical column elevation variance; after extracting the elevation coordinate values ​​of all the point cloud data points in the horizontal column point cloud, using the variance of the corresponding elevation coordinate values ​​as the horizontal column elevation variance; averaging all the vertical column elevation variances as the first average elevation variance of the road surface of the sub-road surface area in the length direction; averaging all the horizontal column elevation variances as the second average elevation variance of the road surface of the sub-road surface area in the width direction; and determining the road surface flatness based on the first average elevation variance and the second average elevation variance.

[0009] By adopting the above technical solution, the variance of the elevation coordinate values ​​of the vertical and horizontal point clouds is extracted, and the average is calculated to obtain the first and second average elevation variances. Based on this, the road surface flatness is determined, which can more comprehensively consider the ups and downs of the road surface in the length and width directions. This multi-dimensional analysis method improves the accuracy of road surface flatness assessment, provides a more reliable basis for subsequent repair work, helps to carry out repairs more accurately, and improves the overall quality of the road surface.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after determining the target maintenance vehicle from the candidate maintenance vehicles in combination with an algorithm that prioritizes vehicle departure time, after the step of sending a maintenance task to the target maintenance vehicle, it also includes: predicting whether the amount of available maintenance materials of the target maintenance vehicle meets the maintenance demand corresponding to the sub-road area, where the maintenance demand is determined by the area of ​​the location to be repaired and a preset material consumption per unit area; if the amount of available maintenance materials is insufficient, determining an alternative supply point, and determining maintenance route information that allows the target maintenance vehicle to pass through the alternative supply point; and sending the maintenance route information via the alternative supply point to the target maintenance vehicle.

[0011] By adopting the above technical solution, the amount of available maintenance materials of the target maintenance vehicle is predicted. If it is insufficient, the alternative supply point and maintenance route information are determined and sent to the vehicle, avoiding maintenance delays caused by insufficient materials. In this way, preparations can be made in advance to ensure that the maintenance vehicle has sufficient materials when performing the task, and the repair work is completed in time, which improves maintenance efficiency and reduces the impact on road use.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the category to be repaired that matches the position to be repaired according to the road surface flatness, it also includes: obtaining environmental monitoring data within a set range near the position to be repaired through a monitoring device; judging whether the environmental conditions of the position to be repaired meet the construction conditions corresponding to the category to be repaired according to the environmental monitoring data; if the environmental conditions do not meet the construction conditions, determining an alternative category to be repaired for the position to be repaired; and determining a candidate maintenance vehicle whose distance from the position to be repaired is within the set range and whose category matches the category according to the alternative category to be repaired.

[0013] By adopting the above technical solution, environmental monitoring data is obtained to determine the construction conditions. If the conditions are not met, the alternative repair category and the corresponding candidate repair vehicle are determined, thus avoiding repair delays or quality problems caused by unsuitable environmental conditions. This method of adjusting the repair plan according to the environment can better adapt to the actual situation, ensure that the repair work is carried out under appropriate conditions, and improve the success rate and effect of the repair.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining, based on the alternative category to be repaired, a candidate maintenance vehicle whose distance from the location to be repaired is within a set range and whose category matches the category, it also includes: sending maintenance task information to a new target maintenance vehicle among the candidate maintenance vehicles; after receiving the maintenance completion information reported by the new target maintenance vehicle, judging, based on the maintenance completion information, whether the maintenance quality data in the maintenance completion information meets a preset quality threshold; if the quality threshold is not met, determining the quality defect type and the corresponding supplementary maintenance plan; re-planning a supplementary maintenance route for the new target maintenance vehicle, and issuing a supplementary maintenance instruction.

[0015] By adopting the above technical solution, the repair completion information is received and the repair quality is judged. If the quality threshold is not met, a supplementary repair plan is determined, the route is replanned, and instructions are issued to timely discover and solve quality problems. This can ensure that the road repair meets the expected standards, improve the safety and comfort of the road, and extend the service life of the road.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after determining the target maintenance vehicle from the candidate maintenance vehicles in combination with an algorithm that prioritizes vehicle departure time, after the step of sending a maintenance task to the target maintenance vehicle, it also includes: receiving feedback information from the target maintenance vehicle, the feedback information including the actual maintenance end time; determining the standard maintenance time of the sub-road area according to a preset maintenance time standard; comparing the time difference between the actual maintenance end time fed back and the standard maintenance time; if the time difference exceeds a set threshold, confirming the abnormal factor causing the timeout, and recording the fault information of the target maintenance vehicle, the fault information including the number of fault occurrences; when the number of fault occurrences exceeds the set number, sending a prompt message to the management end, the prompt message is used to prompt the management personnel to check the target maintenance vehicle.

[0017] By adopting the above technical solution, feedback information is received and the actual maintenance time is compared with the standard time. If it exceeds the time limit, the abnormal factors are confirmed and the fault information is recorded. When the number of faults exceeds the set number, a prompt message is sent. This helps managers to understand the status of the maintenance vehicle in a timely manner and take corresponding measures to improve the reliability and maintenance efficiency of the maintenance vehicle.

[0018] In combination with some embodiments of the first aspect, in some embodiments, the step of encoding multiple sub-pavement areas according to a preset coding rule to obtain a pavement code specifically includes: according to a preset position coding rule, encoding the position of the target road, the position of the pavement area and the position of the sub-pavement area in turn to generate a unique position number; forming a comprehensive code based on the pavement material type, the category to be repaired and the pavement flatness parameters of the sub-pavement area; and determining the pavement code based on the position number and the comprehensive code.

[0019] By adopting the above technical solution, the road surface code is determined according to the position coding rules and comprehensive coding, so that each sub-road surface area has a unique identification, which is convenient for accurately recording and tracking its related information, such as road surface material, type to be repaired and flatness, etc. This helps to improve the refinement of road surface management, provides a more accurate basis for subsequent maintenance and repair work, and improves the efficiency and quality of road management.

[0020] In a second aspect, the present application provides a server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0021] In a third aspect, the present application provides a computer-readable storage medium comprising instructions, which, when executed on a server, causes the server to execute the method described in the first aspect and any possible implementation of the first aspect.

[0022] In a fourth aspect, the present application provides a computer program product, which, when executed on a server, enables the server to execute the method described in the first aspect and any possible implementation manner of the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Due to the adoption of technical means of dividing road surface areas and sub-road surface areas according to image data, encoding, and accurately determining the location to be repaired and dispatching maintenance vehicles, the problems of inaccurate and inefficient road surface flatness repair in the existing technology are effectively solved, thereby achieving accurate repair of road surface flatness, improving repair efficiency and quality, and ensuring the technical effect of effectively improving road surface flatness.

[0024] 2. The use of technical means to predict the amount of available maintenance materials for the target maintenance vehicle, determine alternative supply points and maintenance route information and send them to the vehicle effectively solves the problem of maintenance delays caused by insufficient maintenance materials in the existing technology, thereby achieving the technical effect of ensuring the smooth progress of maintenance tasks, completing repair work in a timely manner, improving maintenance efficiency and reducing the impact on road use.

[0025] 3. Due to the adoption of technical means for receiving maintenance completion information and judging maintenance quality, determining supplementary maintenance plans, re-planning routes and issuing instructions, the problem of difficult to ensure road repair quality in existing technologies has been effectively solved, thereby achieving the technical effect of ensuring repair quality, ensuring that road repairs meet expected standards, improving road safety and comfort, and extending road service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a road surface smoothness repair method based on a scheduling algorithm in an embodiment of the present application; Figure 2 is another flow chart of the road surface flatness repair method based on the scheduling algorithm in the embodiment of the present application; Figure 3 It is a schematic diagram of a physical device structure of a server in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to and includes any or all possible combinations of one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, an application scenario of the relevant method is introduced below.

[0030] Xiao Wang is responsible for the daily inspection and maintenance of municipal roads. Every day, he needs to drive a patrol car and repeatedly patrol along the planned route of the road he is responsible for, visually checking whether the road surface is damaged. If he finds cracks or potholes on the road, he will record the location and go back to fill out a repair application. However, it is difficult to detect subtle deformations of the road surface through visual inspection. If the quality of the road surface continues to deteriorate, it may take time for the vehicle to bump around while running to notice it. Sometimes, after the potholes are formed, even Xiao Wang himself does not know how long they have been there. Later, a camera system installed on the patrol car was used, which could automatically take pictures of the road when Xiao Wang was patrolling, and then use simple image processing methods to find cracks and potholes in the pictures. This is very helpful for Xiao Wang to check the road conditions, and it can remind him to pay attention to some problems that may be missed when visually inspecting. However, it is still difficult to judge the flatness and slight deformation of the road surface.

[0031] It can be seen that the methods in the related art rely on manual visual inspection of road conditions, which is inefficient, cannot fully cover all roads, and are prone to missing some subtle deformations. Using simple image processing to find cracks and potholes is also limited, and it is impossible to perform quantitative analysis to determine refined indicators such as road surface flatness.

[0032] By adopting the technical means in the embodiments of the present application, such as dividing the road surface area and coding according to image data, analyzing the flatness using point cloud data, determining the category to be repaired, and dispatching matching maintenance vehicles for repair, the road surface can be effectively repaired by accurately locating the position of the road surface that needs to be repaired, scientifically evaluating the road surface flatness, and reasonably planning the repair plan and vehicles. This can not only improve the accuracy and efficiency of road surface repair, but also deploy different types of repair vehicles according to the actual situation of the road surface, so as to achieve accurate matching of repair methods.

[0033] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above content. Figure 1 , which is a flow chart of a road surface flatness repair method based on a scheduling algorithm in an embodiment of the present application.

[0034] S101, after acquiring image data of a target road through an image acquisition device, dividing the target road into a plurality of road surface areas according to preset area sizes based on the image data, and dividing each of the road surface areas into a plurality of sub-road surface areas; The image acquisition device can be a high-definition camera installed on the roadside or on a patrol vehicle, which can shoot the target road and obtain road image data. The server is connected to multiple image acquisition devices in communication, receives their image data, and is also connected to the management end in communication, sending target maintenance vehicle information and maintenance instructions to the management end. The server needs to process and analyze the captured images, specifically including: first, detecting the location information of the road area in the image, and dividing multiple road areas according to the preset size requirements (such as dividing each 50-meter length into a road area), and determining the range of each road area. Second, in each road area, continue to divide multiple sub-road areas according to the preset size (such as 5 meters × 5 meters), and determine the location coordinate information of each sub-road area. For example, if a section of road is 500 meters long, it can be divided into 10 road areas, each of which is 50 meters long. Then each road area can be further divided. If a sub-area size of 5 meters × 5 meters is used, each road area can be divided into 100 sub-road areas, and the road section has a total of 10×100=1000 sub-road areas. In this way, the image data is subdivided into multiple levels of regional divisions, which facilitates the subsequent management of road locations of different granularities. This division method can flexibly adjust the region size to adapt to different road conditions and processing accuracy requirements.

[0035] S102, encoding the plurality of sub-road surface areas according to a preset encoding rule to obtain a road surface code; After the sub-pavement areas are divided, the server needs to assign a unique identification code to each area for accurate identification and management. The coding rules can be pre-set according to a certain format. For example, the code can include the target road ID, pavement area number, and sub-pavement area number. Specifically, it can be designed as follows: pavement code = target road ID + pavement area number + sub-pavement area number, where the target road ID can use the road management number, and the pavement area number and sub-pavement area number are serial numbers assigned in sequence when dividing. For example, if the target road ID is G123, a pavement area number is 08, and one of the sub-pavement areas is numbered 056, then the code of the sub-pavement area is G12308-056. In this way, by combining the target road information and the two-level area number, a code that uniquely identifies each sub-pavement area can be generated.

[0036] The coding format can also add extended content such as checksums to improve the correctness of the coding. After the sub-road area division and coding are completed, the server can establish a database of regional location information corresponding to the road coding, so as to quickly find the corresponding road area through the coding. This provides a basis for subsequent road condition detection and maintenance.

[0037] In some embodiments, specifically, according to the preset position coding rules, the server can sequentially encode the position of the target road, the position of the pavement area, and the position of the sub-pavement area to generate a unique position number. For example, the encoding structure of "road ID-pavement area serial number-sub-pavement area serial number" can be adopted. Taking highway G1 as an example, G1 can be set as the road ID; the highway is divided into multiple pavement areas of 1 km in length, and each pavement area is assigned a four-digit number, such as 0001, 0002, etc.; each pavement area is further divided into multiple sub-pavement areas, and each sub-area is encoded with a four-digit number in row order, such as 0101, 0102, etc. Then the position number of a sub-pavement area can be G1-0056-0215, representing the 215th sub-pavement area in the 56th pavement area of ​​highway G1. Then, the server forms a comprehensive code based on the road material type, the type of repair to be repaired, and the road surface smoothness parameters of the sub-road surface area. For example, for the sub-road surface area G1-0056-0215 ​​just now, if its road surface material is asphalt concrete, the type of repair to be repaired is partial breaking and resurfacing, and the smoothness is assessed as average, a comprehensive code "BH-1-2" can be formed, indicating asphalt concrete material, partial breaking and resurfacing type, and average smoothness. Combining the above position number G1-0056-0215 ​​and the comprehensive code BH-1-2, the final road surface code of the sub-road surface area can be generated as "G1-0056-0215-BH-1-2".

[0038] In this way, by combining the target road information, area number and road attribute parameters, a unique road code can be assigned to each sub-road area, which is convenient for subsequent identification and management of road conditions. The coding structure can be adjusted and expanded according to actual needs, making road management more refined.

[0039] S103, after acquiring the point cloud data corresponding to each of the sub-road surface areas, dividing the point cloud data into a plurality of horizontal and vertical point clouds according to the width and length of the sub-road surface area; After the image data is divided and encoded, the server needs to collect point cloud data for each sub-road area to accurately reflect the road condition. Sensors such as laser radar installed on patrol cars can be used to obtain road point cloud information. For each sub-road area, the server extracts the point cloud data within its range to form a point cloud set of the sub-area. The server then processes this point cloud set, first detecting the length and width of the sub-area, and then dividing the point cloud into multiple columns of longitudinal point clouds according to the length, and into multiple rows of transverse point clouds according to the width. For example, if a sub-road area is 5 meters long and 5 meters wide, the point cloud data of the sub-road area can be divided into 10 columns of longitudinal point clouds, each with a width of 0.5 meters; and divided into 10 rows of transverse point clouds, each with a length of 0.5 meters. In this way, a neatly divided point cloud grid is obtained, which is convenient for calculating the statistical characteristics of each point cloud for evaluating the flatness of the road surface.

[0040] S104, determining the road surface flatness of the sub-road surface area based on the key point coordinate differences of each of the horizontal row point cloud and the vertical row point cloud; After dividing the point cloud data, the server will extract the key points of each horizontal and vertical point cloud. The key points can be selected from the point cloud points or the middle points. Then calculate the coordinate elevation values ​​of these key points. If the road surface is relatively flat, the elevation values ​​of the key points in the same direction will not change much. If the road surface is uneven, the elevation values ​​will change greatly. The server can statistically calculate the difference range of the coordinates of each key point in each direction. If the elevation difference of the key points in most directions is small, it can be determined that the flatness of the sub-road area is good; if there are many key points with large differences, the flatness is poor. Specifically, the flatness evaluation level can be set. If more than 50% of the key points in the direction have a difference value within 10 cm, it is judged to be good flatness; if 30%-50% of the key points in the direction have a difference value between 10-20 cm, it is judged to be average flatness; if more than 30% of the key points in the direction have a difference value of more than 20 cm, it is judged to be poor flatness. The above standards can be adjusted according to actual needs and are not limited here. By combining the statistical results from multiple directions, the overall road surface roughness level of the sub-road surface area can be obtained.

[0041] In some embodiments, after dividing the point cloud data into horizontal and vertical columns, the server can extract the elevation coordinate values ​​of all point cloud data points in the vertical column point cloud, and then calculate the variance of these elevation coordinate values, and use it as the elevation variance of the vertical column point cloud. Similarly, for the horizontal column point cloud, the server also extracts the elevation coordinate values ​​of all point cloud data points, and calculates the variance of the elevation coordinate values ​​as the elevation variance of the horizontal column. For example, assuming that a sub-road surface area is divided into 10 vertical columns of point clouds, each column of point clouds has 100 data points, then the server extracts the elevation values ​​of 100 points in each vertical column, statistically calculates its variance, and obtains the elevation variance of 10 vertical columns of point clouds. Similarly, the elevation variance of 10 horizontal column point clouds can be obtained. Then, the server averages the elevation variance values ​​of all 10 vertical columns of point clouds, and calculates the first average elevation variance of the sub-road surface area in the length direction, which can reflect the overall undulation of the road surface in the length direction.

[0042] Similarly, averaging the elevation variances of 10 horizontal point clouds can obtain the second average elevation variance of the sub-road area in the width direction, reflecting the lateral road surface undulation. Finally, the server can evaluate the road surface flatness of the sub-road area based on the first average elevation variance and the second average elevation variance. If the first and second average elevation variances are relatively small, it means that the road surface undulates relatively evenly in length and width, and it can be determined that the flatness of the sub-area is good. If the average elevation variance in either or both directions is large, it means that there is obvious unevenness in the road surface, and the flatness is poor.

[0043] Through this multi-dimensional analysis of extracted point cloud elevation information, the flatness of the road surface can be evaluated more comprehensively and accurately, making the flatness grading results more precise, providing a reliable basis for subsequent road maintenance.

[0044] S105, determining the sub-road surface area whose road surface flatness is lower than a first threshold or higher than a second threshold as a location to be repaired, wherein the second threshold is greater than the first threshold; After obtaining the road surface flatness of each sub-road area, the server needs to determine the location that needs to be repaired first based on the flatness results. For example, the area with a flatness grade of "poor" can be set as the primary location to be repaired, and the area with a flatness grade of "average" can be set as the secondary location to be repaired.

[0045] Threshold setting can also be combined with road use needs and maintenance resource conditions. If resources are sufficient, higher standards can be set, such as including areas with average flatness in the primary repair scope; if resources are limited, the threshold standards can be appropriately relaxed to repair the road sections with the worst flatness first. Setting two levels of thresholds can distinguish priorities and determine the repair areas in order from severe to minor to optimize resource allocation and ensure road quality. The server can establish a table of locations to be repaired to record the code, location coordinates, flatness evaluation results, and priority of repair for each sub-road area determined to be repaired. This can help the server remember the sections that need to be repaired and facilitate subsequent scheduling and allocation. For example, the sub-road area coded "G12311-215" has a flatness evaluation of "poor" and is determined as the primary location to be repaired; the sub-road area coded "G12311-222" is evaluated as "average" and is determined as a secondary location to be repaired.

[0046] In some embodiments, the first threshold and the second threshold in this step may also refer to the road surface depression in the road surface flatness being lower than when the depression depth in the sub-road surface area is lower than the first threshold, it is determined as a position to be repaired. The first threshold here can be set to the maximum allowable value of the depression depth, which can be a negative threshold, such as the first threshold is set to -10 cm; when the protrusion height in the sub-road surface area is higher than the second threshold, it is determined as a position to be repaired. The second threshold here can be set to the maximum allowable value of the protrusion height, such as the second threshold can be set to 20 cm. The protrusion height and depression depth here can be realized by the coordinate elevation value of the key point in the previous step, and the difference in the elevation value of the key point of each adjacent horizontal and longitudinal point cloud can be counted to obtain the protrusion height and depression depth in the column.

[0047] S106, determining a category to be repaired that matches the location to be repaired according to the road surface flatness; After determining the sub-pavement area to be repaired, the server needs to analyze the specific flatness parameters of the road surface to determine the repair category corresponding to the location to be repaired. Road surfaces with different flatness levels have different types and severity of damage, and different repair measures need to be taken. The server can automatically determine the repair category that matches each sub-pavement area to be repaired based on statistical flatness evaluation indicators, road surface height difference range, cracking rate and other information, and refer to the preset repair category standards. For example, if the flatness is evaluated as "good", it matches simple repair, and if the flatness is evaluated as "poor", it matches road reconstruction, etc. The classification standards can be adjusted and optimized according to actual experience data.

[0048] For example, the following matching rules can be set: if the flatness is evaluated as "good", the height difference is less than 3 cm, and the cracking rate is less than 10%, it matches the "micro-repair" category; if the flatness is evaluated as "average", the height difference is 3-5 cm, and the cracking rate is 10%-30%, it matches "partial break-up and re-paving"; if the height difference is evaluated as "poor", the height difference is greater than 5 cm, and the cracking rate exceeds 30%, it matches "large-scale reconstruction". According to the above rules, the specific repair measure category required for the location to be repaired can be determined.

[0049] S107, determining a candidate maintenance vehicle whose distance from the location to be repaired is within a set range and matches the category to be repaired; After obtaining the specific location of the sub-road surface area to be repaired and the matching repair category, the server needs to determine the candidate maintenance vehicles nearby that can perform the corresponding category of maintenance tasks. The location information of each maintenance vehicle recorded on the server can be queried to select candidate vehicles within the range of the sub-road surface area to be repaired. Then, based on the technical parameters and configuration information of the vehicle, determine whether it can meet the repair category requirements of the location to be repaired. For example, if the location to be repaired requires the road surface to be broken up and repaved, it is necessary to query maintenance vehicles equipped with heavy machinery such as stone crushing equipment and rollers as candidates; if it is just road repair, a small maintenance vehicle equipped with corresponding materials and equipment can be selected.

[0050] The server can set a search range, such as all repair vehicles that meet the repair category requirements within a 5-kilometer radius around the location to be repaired, and determine them as candidate vehicles. If no matching vehicle is found within the range, the search range can be gradually expanded until a suitable candidate vehicle is determined.

[0051] S108. After determining a target maintenance vehicle from the candidate maintenance vehicles in combination with an algorithm that prioritizes vehicle departure time, sending a maintenance task to the target maintenance vehicle so that the target maintenance vehicle goes to a corresponding sub-road surface area for repair.

[0052] When determining the final target maintenance vehicle from the candidate vehicles, the server needs to consider the vehicle's departure time and the time from the location to be repaired. For a quick response, vehicles that can arrive at the site in the shortest time should be given priority. The server can use a departure time priority algorithm: query the current geographic location of each candidate vehicle; calculate the estimated driving time required for the candidate vehicle to reach the sub-road area to be repaired; sort the driving time, and determine the vehicle with the shortest departure time as the target maintenance vehicle. After the target vehicle is determined, the server sends the coordinates of the location to be repaired, the repair category, the task requirements and other information to the central control system of the target vehicle, issues a specific maintenance task, and guides it to perform the road repair work. After the task is completed, the target vehicle will feedback the repair status and quality data. The server can record this data to confirm the completion of the task and subsequent quality assessment.

[0053] In some embodiments, the server can predict whether the amount of available maintenance materials of the target maintenance vehicle meets the maintenance demand of the corresponding sub-pavement area to be repaired. The maintenance demand can be calculated by the area of ​​the location to be repaired and the preset material consumption per unit area. For example, if the area of ​​the road section to be repaired is 1,000 square meters and the preset asphalt concrete material consumption is 15 kg / square meter, the maintenance material demand at this location is 1,000 * 15 = 15,000 kg. The server can query its current material inventory information through the connection with the maintenance vehicle to determine whether the amount of available materials is sufficient to meet the above calculated demand. If the amount of available maintenance materials is insufficient, the requirements of this task cannot be met. At this time, the server needs to determine an alternative supply point to replenish the necessary materials for the maintenance vehicle.

[0054] The server can find the material sites near the target maintenance vehicle based on the geographic location information, and determine the sites with the required materials as the alternative supply points. At the same time, considering the material reserve information of each site, give priority to sites with sufficient inventory. Then, the server needs to determine a maintenance route information through the selected alternative supply point and send it to the target maintenance vehicle, instructing it to go to the supply point to replenish materials first, and then go to the repair location to start the operation. For example, if the alternative supply point A is more convenient, the server can plan a route to go to site A for replenishment first, and then go to the section C to be repaired; if the route of another supply point B is shorter, a route to site B and then to point C can be planned. The server will use the path planning algorithm to determine a supply route with the best total route based on information such as vehicle location and site location. In this way, by predicting material needs in advance and replenishing the required materials in a timely manner, it can ensure that the maintenance vehicle has sufficient material preparation when it arrives at the work site, avoiding operation delays or failures due to material shortages, and improving the guarantee of task completion.

[0055] In the embodiments of the present application, the road surface condition is comprehensively and accurately detected and analyzed, and reasonable planning and deployment of repair resources are achieved, which effectively solves the problem of lack of refined management of road surface assessment and maintenance scheduling in the prior art.

[0056] In some embodiments, the server receives the maintenance end feedback information sent back by the target maintenance vehicle, and the feedback information includes the actual maintenance end time. For example, the actual end time of a repair task is 10:35 on June 12, 2022. Then, the server determines a standard estimated repair time based on the area of ​​the sub-pavement area to be repaired, the maintenance category, etc., and combined with historical statistical data. For example, according to the construction rate standard of the repair category, the repair time for 500 square meters of this type of pavement is 2.5 hours. The standard maintenance time for the task is determined to be 10:30 (start time 8:00 + 2.5 hours). Then, the server compares the actual end time 10:35 fed back with the calculated standard end time 10:30, and obtains a time difference of 0:05 (5 minutes). If the time difference exceeds the tolerable threshold, such as more than 10 minutes, it indicates that there is an abnormal delay in this maintenance.

[0057] When an abnormal delay is detected, the server will try to confirm the specific cause of the timeout, such as vehicle failure, improper operation by personnel, material supply problems, etc., and record the abnormal factors. At the same time, the fault information is combined with the historical fault records of the target maintenance vehicle to determine whether the cumulative number of faults exceeds the set threshold, such as more than 10 times. When the cumulative number of faults exceeds the standard, it indicates that there may be systemic problems with the maintenance vehicle. At this time, the server will send a prompt message to the manager, suggesting that the target maintenance vehicle be checked to avoid the problem from continuing to affect the operation. Through such maintenance time monitoring and exception handling mechanisms, the transparency of the repair process can be improved, and vehicle failures can be discovered in a timely manner, prompting managers to take measures to improve system reliability.

[0058] In some embodiments, after determining the location to be repaired and the corresponding repair category, the server needs to obtain environmental monitoring data near the location to be repaired, such as temperature, humidity, wind speed, etc., because road construction also needs to consider specific environmental factors. If it is determined that the environmental conditions do not meet the construction requirements of the category to be repaired, for example, it is not suitable to spray in windy weather, or it is not suitable to break the road surface in rainy days, the original plan needs to be adjusted. At this time, the server will re-determine an alternative repair category that is more suitable under the current environmental conditions, such as changing to a simple measure such as short-term oil replenishment.

[0059] After combining the above scenarios, the following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the road surface smoothness repair method based on the scheduling algorithm in an embodiment of the present application.

[0060] S201, obtaining environmental monitoring data within a set range near the location to be repaired through monitoring equipment; After determining the specific location to be repaired and the matching repair category, since road construction also needs to consider the environmental conditions at the time, the server needs to obtain real-time environmental monitoring data near the location to be repaired.

[0061] The server can be connected to various environmental monitoring devices, such as temperature and humidity sensors, anemometers, noise meters, etc. These monitoring devices can be arranged on the roadside or on lamp poles. The server can select monitoring equipment within a certain range around the location to be repaired, such as equipment within 100 meters of the location to be repaired, and obtain their real-time monitoring data, such as temperature, humidity, wind force, noise and other information. These data can determine whether the noon environmental conditions at the location are suitable for repair operations. For example, if the temperature is too low, it will affect the construction conditions of certain materials; if the wind is too strong, the construction will be difficult and affect the effect; if the noise is too high, it will affect the efficiency of the operators. Therefore, the server needs to summarize various environmental data for comprehensive judgment to avoid construction quality problems or safety accidents caused by certain environmental factors.

[0062] S202: judging whether the environmental conditions of the location to be repaired meet the construction conditions of the category to be repaired according to the environmental monitoring data; After obtaining the environmental monitoring data, the server needs to determine whether the monitoring data meets the construction requirements of the repair category of the location to be repaired based on the preset construction condition standards. Different repair categories have different requirements for temperature, humidity, wind and other conditions. The server can compare item by item based on the category to be repaired and the obtained monitoring data to determine whether the construction conditions are met. For example, the simple repair category has lower environmental requirements, but the road surface breaking and resurfacing has certain requirements for temperature, humidity, and wind. If the monitoring data shows that the current wind force is too strong, the construction conditions of the resurfacing category are not met.

[0063] If the environmental conditions meet the construction conditions corresponding to the type to be repaired, step S203 is executed; If the environmental conditions do not meet the construction conditions corresponding to the category to be repaired, step S204 is executed.

[0064] S203, determining candidate maintenance vehicles whose distance from the location to be repaired is within a set range and whose categories match; This step is similar to step S107 and will not be described again here.

[0065] S204, determining an alternative category of the location to be repaired; If the environmental conditions do not meet the construction requirements of the corresponding repair category, the server will re-evaluate and determine an alternative repair category that is more suitable for the current environmental conditions. For example, the original category is pavement breaking and resurfacing, but the environmental monitoring data shows that the wind is too strong and not suitable for this construction work. The server can refer to the category and environment adaptation rules to determine another simple repair category that is more suitable in a windy environment, such as small area gravel repair or spray sealing.

[0066] The determination of the alternative category will take into account environmental constraints, maintenance urgency, required repair intensity, etc. Ensure that a feasible and simple repair plan is selected under the current environmental conditions to achieve the purpose of emergency repair.

[0067] S205, determining, according to the alternative to-be-repaired category, a candidate repair vehicle whose distance from the to-be-repaired location is within a set range and whose category matches the to-be-repaired location; After determining the alternative category to be repaired, the server needs to re-screen candidate repair vehicles that match the category. The server first queries all the surrounding repair vehicle information within a preset distance range based on the coordinates of the location to be repaired, such as vehicles within a range of 5 kilometers. The server then retrieves the category parameters of these vehicles one by one to determine whether they meet the requirements of the alternative category to be repaired. For example, if the original category to be repaired is "partial breakage and resurfacing", but it is changed to "simple refueling" due to strong winds, it is necessary to retrieve refueling vehicles equipped with asphalt concrete mixing systems, spraying equipment, etc.

[0068] The server can establish a vehicle matching matrix, list the functional configurations of each candidate vehicle, and compare them item by item with the requirements of the alternative category to be repaired. When the configuration of the candidate vehicle fully meets the category requirements, it is confirmed as a matching vehicle. If a vehicle configuration lacks a certain function, it can also be evaluated according to the set importance weight, and if it meets the threshold requirements, it can be considered a basic match.

[0069] Finally, the server will generate a new list of candidate vehicles after screening, which only contains the vehicle information of the matching category, including vehicle number, vehicle category, location, physical distance from the location to be repaired, etc. This information will be used to determine the best target repair vehicle in the future. For example, the simple refueling vehicle numbered V1101, located in the construction site next to the location to be repaired, will be the best candidate vehicle.

[0070] S206, sending maintenance task information to the candidate maintenance vehicle; After selecting candidate repair vehicles with matching categories, the server needs to issue specific repair task instructions to the final target vehicle. The server will connect to the on-board system of the target repair vehicle and issue repair task information, which includes: the code, location coordinates, damage type, environmental conditions and alternative repair categories of the sub-road area to be repaired, as well as repair requirements, such as the use of specific materials, oil filling thickness and other parameters.

[0071] The server can use the control command protocol supported by the vehicle system to organize the above information in a format that can be read by the device, and send it to the target maintenance vehicle through a secure and reliable communication link. After receiving it, the vehicle system will automatically parse it and display it to the vehicle operator, guiding him to carry out the repair work according to the specified requirements. If the task is more complicated, the server can also determine whether it is necessary to issue a specific operation process and step plan. If multiple vehicles are arranged to work together, a fleet collaboration instruction will also be issued to specify the scope and order of each vehicle's operation to improve efficiency.

[0072] The server can also set up a command confirmation mechanism. The vehicle system needs to reply with a confirmation message after receiving the command, so that it can verify whether the vehicle has received it correctly and keep track of the task progress in time. If the confirmation is not received within the set time, the server will retry to send it until it confirms that the command has been delivered.

[0073] S207, after receiving the maintenance completion information reported by the candidate maintenance vehicle, judging whether the maintenance quality data in the maintenance completion information meets a preset quality threshold according to the maintenance completion information; After the maintenance vehicle completes the designated road refueling task, the relevant personnel can collect the maintenance results through the corresponding equipment and upload them to the vehicle system, which will send the maintenance completion report to the server. The report can include the quality inspection data of the refueling section.

[0074] After receiving the report, the server will extract the quality data and check it item by item. For example, for the thickness of the asphalt concrete layer, the measured values ​​of each measuring point will be extracted, and the deviation from the designed thickness will be calculated. If the deviation is greater than ±10%, it will be judged as unqualified. For the flatness of the road surface, the flatness parameters will be extracted. If it is lower than the specification requirements, it will be unqualified. The compaction, roughness and other indicators are also compared item by item. The server will automatically judge the extracted quality data according to the quality threshold requirements preset in the refueling specification. If all indicators meet the threshold requirements, the quality is confirmed to be qualified, the maintenance acceptance is completed, and the results are recorded. If there are unqualified items, it is judged as a quality problem and the acceptance cannot be completed. For example, if the thickness deviation and flatness are qualified, but the compaction is not up to standard, the quality is judged to be unqualified and unacceptable. The server will start the quality problem handling mechanism for subsequent quality confirmation and rework guidance.

[0075] If the maintenance quality data meets the preset quality threshold, step S208 is executed; If the maintenance quality data does not meet the preset quality threshold, step S208 is executed.

[0076] S208, recording the completion status of the maintenance task, and marking the sub-road surface area as repaired; For tasks that meet the quality standards, the server will confirm the completion of the task and mark the repair status of the corresponding road section as "repaired". This includes recording the repair time, repair vehicle, quality assessment results and other information to confirm the repair status of the road section. Task completion information can be entered into the road repair history database for query, analysis and summary, which can be used to continuously improve quality control and construction guidance. Marking the repair status of the road section also facilitates subsequent inspections and maintenance.

[0077] S209, determine the quality defect type and the corresponding supplementary maintenance plan; In the case of unqualified quality, the server needs to determine the specific quality problems and remedial measures. Based on the unqualified items and data in the quality inspection report, combined with the quality knowledge base in the field of road repair, the server will automatically analyze and determine the specific types of quality defects, such as insufficient concrete density, excessive road surface flatness deviation, etc. Then, according to the type of quality defect, query the repair plan template in the knowledge base to determine the corresponding supplementary repair measures. For example, if the compaction degree does not meet the standard, the rework plan is to add compaction; if the flatness exceeds the standard, the re-paving layer needs to be polished, etc. The knowledge base will preset the preferred rework template plan for different defect types.

[0078] The server finally forms a formal rework implementation plan, including the quality management measures taken, the estimated repair time, the required equipment and personnel, etc. It is then sent to the corresponding maintenance vehicle, requiring additional repairs to eliminate quality defects and ensure compliance.

[0079] By automatically determining the type of quality problem and matching supplementary solutions, rework guidance can be carried out quickly and efficiently to ensure the quality of repair. The knowledge base can continuously accumulate maintenance experience and continuously improve the quality management template to improve the analysis effect.

[0080] S210: re-plan a supplementary maintenance route for the candidate maintenance vehicle and issue a supplementary maintenance instruction.

[0081] After issuing the quality rework plan, the server also needs to plan a specific route for the maintenance vehicle to carry out supplementary work.

[0082] First, the server will re-query the current geographic location information of the vehicle, and obtain the resource information such as equipment and materials on the vehicle through the set channels. Then, the server will search for relevant facilities such as nearby material sites and waste disposal points to avoid insufficient equipment and materials on the vehicle, and query road traffic data, including information such as restrictions and congestion that may affect driving. After comprehensively considering the above information, the server will use an intelligent path planning algorithm to re-plan a route plan for supplementary maintenance for the maintenance vehicle. At the same time, a route report will be generated, marking the points passed by the route, the location of the facilities, the length of the path and the time consumed.

[0083] Finally, the server sends the supplementary maintenance route plan to the vehicle-mounted system of the maintenance vehicle in the form of a task instruction. The instruction contains detailed route report information and requires the vehicle to rework according to the route. The vehicle system will receive the instruction and execute the route plan in the vehicle navigation system to guide the driver to accurately reach the quality problem section for supplementary construction.

[0084] In the embodiment of the present application, the above technical means are adopted to achieve the technical effect of dynamically adjusting and optimizing the repair plan and vehicle scheduling according to different environmental conditions and quality feedback results. This can achieve the environmental adaptability of the repair plan, ensure construction under appropriate conditions, and continuously improve through closed-loop quality control, thereby improving the success rate and quality of road repair, making the entire maintenance scheduling and implementation process more intelligent and dynamic.

[0085] The following describes the server in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a server in an embodiment of the present application.

[0086] It should be noted that Figure 3 The structure of the server shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0087] like Figure 3As shown, the server includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In RAM 303, various programs and data required for system operation are also stored. CPU 301, ROM 302 and RAM 303 are connected to each other through bus 304. Input / output (I / O) interface 305 is also connected to bus 304.

[0088] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0089] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0090] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0091] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0092] Specifically, the server of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the road surface flatness repair method based on the scheduling algorithm provided in the above embodiment is implemented.

[0093] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the server described in the above embodiment; or may exist independently without being assembled into the server. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the server, the server implements the road surface smoothness repair method based on the scheduling algorithm provided in the above embodiment.

[0094] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0095] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0096] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A road surface smoothness repair method based on a scheduling algorithm, applied to a server, characterized in that: The method comprises: After acquiring image data of a target road through an image acquisition device, dividing the target road into a plurality of road surface areas according to preset area sizes based on the image data, and dividing each of the road surface areas into a plurality of sub-road surface areas; Encoding the plurality of sub-road surface areas according to a preset encoding rule to obtain a road surface code; After acquiring the point cloud data corresponding to each of the sub-road surface areas, dividing the point cloud data into a plurality of horizontal and vertical point clouds according to the width and length of the sub-road surface area; Determine the road surface flatness of the sub-road surface area based on the key point coordinate difference between each of the horizontal and vertical point clouds; Determine a sub-road surface area whose road surface flatness is lower than a first threshold or higher than a second threshold as a location to be repaired, wherein the second threshold is greater than the first threshold; Determining a repair category that matches the repair location according to the road surface flatness; Determine a candidate repair vehicle that is within a set distance from the location to be repaired and matches the category to be repaired; After determining a target maintenance vehicle from the candidate maintenance vehicles in combination with an algorithm that prioritizes vehicle departure time, a maintenance task is sent to the target maintenance vehicle, so that the target maintenance vehicle goes to a corresponding sub-road surface area for repair.

2. The method according to claim 1, characterized in that After acquiring the point cloud data corresponding to each of the sub-road surface areas, the step of dividing the point cloud data into a plurality of horizontal and vertical point clouds according to the width and length of the sub-road surface area further includes: After extracting the elevation coordinate values ​​of all the point cloud data points in the vertical column point cloud, the variance of the corresponding elevation coordinate values ​​is used as the vertical column elevation variance; After extracting the elevation coordinate values ​​of all the point cloud data points in the horizontal point cloud, the variance of the corresponding elevation coordinate values ​​is used as the horizontal elevation variance; The average of all the vertical column elevation variances is taken as the first average elevation variance of the road surface in the length direction of the sub-road surface area; the average of all the horizontal column elevation variances is taken as the second average elevation variance of the road surface in the width direction of the sub-road surface area; The road surface roughness is determined according to the first average elevation variance and the second average elevation variance.

3. The method according to claim 1, characterized in that After determining a target maintenance vehicle from the candidate maintenance vehicles in combination with an algorithm prioritizing vehicle departure time, after the step of sending a maintenance task to the target maintenance vehicle, the method further includes: Predicting whether the quantity of available repair materials of the target repair vehicle meets the repair demand of the corresponding sub-road area, wherein the repair demand is determined by the area of ​​the location to be repaired and a preset material consumption per unit area; If the available maintenance materials are insufficient, determining an alternative supply point, and determining maintenance route information that allows the target maintenance vehicle to pass through the alternative supply point; The maintenance route information passing through the candidate replenishment point is sent to the target maintenance vehicle.

4. The method according to claim 1, characterized in that: After the step of determining the category to be repaired that matches the position to be repaired according to the road surface flatness, the method further includes: Acquiring environmental monitoring data within a set range near the location to be repaired through monitoring equipment; Determining whether the environmental conditions of the location to be repaired meet the construction conditions of the corresponding category to be repaired according to the environmental monitoring data; If the environmental conditions do not meet the construction conditions, determining an alternative category of the location to be repaired; A candidate repair vehicle whose distance from the location to be repaired is within a set range and whose category matches the location to be repaired is determined according to the alternative category to be repaired.

5. The method according to claim 4, characterized in that After the step of determining, according to the alternative to-be-repaired category, a candidate repair vehicle whose distance from the to-be-repaired location is within a set range and whose category matches the to-be-repaired location, the method further includes: Sending maintenance task information to a new target maintenance vehicle among the candidate maintenance vehicles; After receiving the maintenance completion information reported by the new target maintenance vehicle, judging whether the maintenance quality data in the maintenance completion information meets a preset quality threshold according to the maintenance completion information; If the quality threshold is not met, determining the quality defect type and the corresponding supplementary maintenance plan; A supplementary maintenance route is replanned for the new target maintenance vehicle, and a supplementary maintenance instruction is issued.

6. The method according to claim 1, characterized in that After determining a target maintenance vehicle from the candidate maintenance vehicles in combination with an algorithm prioritizing vehicle departure time, after the step of sending a maintenance task to the target maintenance vehicle, the method further includes: Receiving feedback information sent by the target maintenance vehicle, wherein the feedback information includes an actual maintenance completion time; Determine the standard maintenance time for the sub-pavement area according to the preset maintenance time standard; Compare the time difference between the actual maintenance completion time fed back and the standard maintenance time; If the time difference exceeds a set threshold, the abnormal factor causing the timeout is confirmed, and the fault information of the target maintenance vehicle is recorded, wherein the fault information includes the number of times the fault occurs; When the cumulative number of failure occurrences exceeds a set number, a prompt message is sent to the management end, and the prompt message is used to prompt the management staff to check the target maintenance vehicle.

7. The method according to claim 1, characterized in that The step of encoding the plurality of sub-road surface areas according to a preset encoding rule to obtain a road surface code specifically includes: According to a preset position coding rule, the position of the target road, the position of the road surface area and the position of the sub-road surface area are sequentially coded to generate a unique position number; Combining the road surface material type, the category to be repaired and the road surface flatness parameter of the sub-road surface area to form a comprehensive code; The road surface code is determined according to the position number and the comprehensive code.

8. A server, characterized in that: The server includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the server to execute the method described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a server, the server is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a server, the server is caused to execute the method according to any one of claims 1 to 7.

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