Urban road intelligent maintenance system

Abstract

The invention relates to the technical field of road maintenance, in particular to an urban road intelligent maintenance system which comprises a data acquisition module. The abnormal road judgment module is used for determining an abnormal road area according to the single body activity data in the preset duration and the road inspection data acquired by the intelligent inspection robot; the abnormal road detection module is used for determining the detection priority of the abnormal road area according to the distance between the abnormal road area and the key monitoring area and the quantity of the single activity data with abnormal fluctuation in the abnormal road area; the abnormal road maintenance module is used for determining the maintenance resource scheduling amount according to the detection priority of the abnormal road area and the repeated occurrence frequency of the abnormal road area; the adjusting module is used for determining and adjusting the road maintenance process according to the utilization rate of the maintenance resources, and the road maintenance efficiency is improved by improving the accuracy of road data analysis in the marathon event process.

Description

Technical Field

[0001] The present invention relates to the technical field of road maintenance, and particularly to an intelligent urban road maintenance system. Background Art

[0002] In the urban infrastructure management system, the maintenance work of urban roads is of crucial importance. It not only relates to the smooth operation of urban traffic but also directly affects the travel experience of residents and the overall image of the city. As a large-scale urban event, a marathon places extremely high demands on the road conditions of the hosting city. Traditional urban road maintenance methods often rely on manual inspections and experience-based judgments, which are inefficient and inaccurate. In the face of large-scale and highly concerned events like marathons, it is difficult to meet the requirements for rapid and precise road maintenance during event preparation and hosting. During the event preparation stage, manual inspections may not be able to promptly detect some minor road problems that may affect the runners' race experience, such as tiny cracks and uneven road surfaces. During the event, in case of sudden road conditions, such as debris falling and temporary road damage, traditional maintenance methods are difficult to respond and handle in a timely manner, thus affecting the smooth progress of the event.

[0003] Chinese Patent Application Publication No.: CN118608117A discloses a smart city road maintenance management method and system, which relates to the technical field of road maintenance management. The invention includes road maintenance screening, road surface information monitoring, marking status monitoring, marking performance analysis, road maintenance analysis, and early warning prompts. By screening each road in the city, the target roads in the city are obtained. By analyzing the overall evaluation coefficient of the markings corresponding to each target road in the city and making a judgment, the maintenance roads in the city are obtained, and the maintenance demand corresponding to each maintenance road in the city is calculated, so as to realize the road maintenance management guarantee for each maintenance road in the smart city, conduct timely prevention and road maintenance of urban roads, improve the convenience and efficiency of travel on urban roads, reduce the blindness and slowness of urban road maintenance work, and make the required maintenance resources for urban road maintenance be allocated more accurately and reasonably.

[0004] However, the existing technology has the problem that the analysis of road data during the marathon event process is not accurate enough, resulting in the inability to promptly detect emergencies and causing low efficiency in urban road maintenance. Summary of the Invention

[0005] Therefore, the present invention provides an intelligent urban road maintenance system to overcome the problem in the existing technology that the analysis of road data during the marathon event process is not accurate enough, resulting in the inability to promptly detect emergencies and causing low efficiency in urban road maintenance.

[0006] To achieve the above object, the present invention provides an intelligent urban road maintenance system, including:

[0007] A data acquisition module, which includes a data acquisition unit for acquiring marathon event data, event venue data, and individual event data, and an intelligent inspection robot for inspecting the marathon event road;

[0008] An abnormal road judgment module, which is connected to the data acquisition module and is used to determine the abnormal road area according to the individual event data within a preset time period and the road inspection data obtained by the intelligent inspection robot;

[0009] An abnormal road detection module, which is connected to the abnormal road judgment module and is used to determine the detection priority of the abnormal road area according to the distance between the abnormal road area and the key monitoring area and the number of individual event data with abnormal fluctuations in the abnormal road area;

[0010] An abnormal road maintenance module, which is connected to the abnormal road detection module and is used to determine the maintenance resource scheduling amount of the abnormal road area according to the detection priority of the abnormal road area and the recurrence frequency of the abnormal road area;

[0011] An adjustment module, which is respectively connected to the abnormal road judgment module and the abnormal road maintenance module, and is used to determine the adjustment of the preset time period and update the maintenance resource scheduling amount calculation model according to the utilization rate of the maintenance resources.

[0012] Further, the abnormal road judgment module determines the abnormal road area including:

[0013] When there is an abnormal fluctuation in the individual event data within a preset time period or the road inspection data is abnormal, determine the road area as an abnormal road area;

[0014] Or, when there is no abnormal fluctuation in the individual event data within a preset time period or the road inspection data is normal, determine the road area as a normal road area.

[0015] Further, the abnormal road judgment module determines that there is an abnormal fluctuation in the individual event data within a preset time period based on the judgment result that the change range of the individual event data within a preset time period is greater than the preset change range or the change trend of the individual event data within a preset time period is irregular.

[0016] Further, the abnormal road judgment module determines that the road inspection data is abnormal based on the image data of road damage or road debris existing in the road inspection data.

[0017] Further, the abnormal road detection module determines the detection priority of the abnormal road area including:

[0018] Under the condition that the distance between the abnormal road area and the key monitoring area is less than the preset distance or the number of abnormal fluctuation individual activity data in the abnormal road area is greater than the preset number, it is determined that the detection priority of the abnormal road area is increased by one level.

[0019] Further, the preset number is determined according to the total number of individual participants in the marathon event.

[0020] Further, the key monitoring areas include the starting area, the ending area and the supply area of the marathon event venue.

[0021] Further, the abnormal road maintenance module determines the maintenance resource scheduling quantity of the abnormal road area, including:

[0022] The maintenance resource scheduling quantity of the abnormal road area is the sum of the product of the detection priority of the abnormal road area and the detection priority weight and the product of the recurrence frequency of the abnormal road area and the recurrence frequency weight.

[0023] Further, the adjustment module determines to adjust the preset duration and update the maintenance resource scheduling quantity calculation model, including determining to adjust the preset duration and update the maintenance resource scheduling quantity calculation model under the condition that the utilization rate of the maintenance resources is less than the preset utilization rate.

[0024] Further, the adjustment amount of the preset duration is positively correlated with the utilization rate of the maintenance resources.

[0025] Compared with the prior art, the beneficial effects of the present invention are as follows. By the change amplitude and change trend of individual activity data within the preset duration, combined with the road inspection data obtained by the intelligent inspection robot, the present invention can more accurately judge whether there is an abnormality on the road. For example, when the change amplitude of the athlete's speed exceeds the preset value (such as 0.5 m / s), it indicates that the individual's speed changes greatly due to road damage, or when the speed change trend presents a concave or convex shape, it indicates that the individual needs to decelerate first and then accelerate due to road obstacles, and it can be judged that there is an abnormality in this area. Through the above method, the accuracy of road data analysis during the marathon event is improved, and thus the urban road maintenance efficiency is improved.

[0026] Furthermore, by setting a preset distance and a preset quantity, the system of the present invention can accurately identify which abnormal road areas need to be processed preferentially. For example, abnormal areas closer to the starting point, the ending point, or the supply area, or areas with a larger quantity of abnormal single-activity data, will be detected and processed preferentially. The detection priority is dynamically adjusted according to the actual situation, avoiding waste and inefficient allocation of resources. For example, when an abnormal area is closer to the key monitoring area and has a large amount of abnormal data, the priority will be increased, thus ensuring that key areas receive timely attention. Through the above method, the accuracy of road data analysis during the marathon event process is improved, and thus the urban road maintenance efficiency is enhanced.

[0027] Furthermore, through the quantitative analysis of the detection priority and the recurrence frequency, the system of the present invention can accurately calculate the amount of maintenance resource scheduling for each abnormal road area. This data-based decision-making method avoids the blindness of resource allocation, ensuring that resources are reasonably allocated to the areas most in need. According to the actual situation of the abnormal road area, the system can dynamically adjust the amount of resource scheduling to flexibly respond to different situations. For example, areas with high priority and frequent abnormalities will receive more resource support. For areas with high detection priority and high recurrence frequency, the system will allocate more resources to ensure that these areas can be quickly processed. Through the clear mapping relationship between the amount of resource scheduling and resource allocation, the maintenance team can quickly understand the urgency of the task and the required resources, thereby optimizing the maintenance process and improving work efficiency. By accurately calculating the amount of maintenance resource scheduling, the system avoids over-investment in low-priority areas and reduces unnecessary resource waste.

[0028] Furthermore, by comparing the actual utilization rate of the maintenance resources with the preset utilization rate, the system of the present invention can dynamically adjust the preset duration. If the utilization rate is lower than the preset value, it indicates that the current preset duration may lead to unreasonable resource allocation. The system will shorten the preset duration through an adjustment coefficient, thereby improving the response speed and utilization efficiency of the resources. When the utilization rate is lower than the preset value, the system will also re-determine the weights of the detection priority and the recurrence frequency, and update the calculation model of the maintenance resource scheduling amount. This dynamic update mechanism can ensure more scientific resource allocation and avoid resource waste. Through the above method, the accuracy of road data analysis during the marathon event process is improved, and thus the urban road maintenance efficiency is enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic structural diagram of the urban road intelligent maintenance system according to an embodiment of the present invention;

[0030] Figure 2 It is a schematic structural diagram of the data acquisition module in the urban road intelligent maintenance system according to an embodiment of the present invention;

[0031] Figure 3This is the flowchart of the adjustment module in the urban road intelligent maintenance system according to the embodiments of the present invention. Detailed implementation manners

[0032] In order to make the objectives and advantages of the present invention clearer, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0033] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0034] In addition, it should be noted that in the description of the present invention, unless otherwise clearly defined and limited, the term "connected" should be understood in a broad sense. For example, it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two components. For those skilled in the art, the specific meaning of the above terms in the present invention can be understood according to specific circumstances.

[0035] Please refer to Figures 1 - 3 as shown Figure 1 This is the structural schematic diagram of the urban road intelligent maintenance system according to the embodiments of the present invention; Figure 2 This is the structural schematic diagram of the data acquisition module in the urban road intelligent maintenance system according to the embodiments of the present invention; Figure 3 This is the flowchart of the adjustment module in the urban road intelligent maintenance system according to the embodiments of the present invention.

[0036] The urban road intelligent maintenance system according to the embodiments of the present invention includes:

[0037] A data acquisition module, which includes a data acquisition unit for acquiring marathon activity data and individual activity data, and an intelligent inspection robot for inspecting the roads of marathon activities;

[0038] An abnormal road judgment module, which is connected to the data acquisition module and is used to determine the abnormal road area according to the individual activity data within a preset time period and the road inspection data acquired by the intelligent inspection robot;

[0039] An abnormal road detection module, which is connected to the abnormal road judgment module and is used to determine the detection priority of the abnormal road area according to the distance between the abnormal road area and the key monitoring area and the number of individual activity data with abnormal fluctuations in the abnormal road area;

[0040] An abnormal road maintenance module, which is connected to the abnormal road detection module and is used to determine the maintenance resource scheduling amount of the abnormal road area according to the detection priority of the abnormal road area and the recurrence frequency of the abnormal road area;

[0041] An adjustment module, which is respectively connected to the abnormal road judgment module and the abnormal road maintenance module, is used to determine an adjusted preset duration and update a calculation model for dispatching the amount of maintenance resources according to the utilization rate of the maintenance resources.

[0042] In the embodiments of the present invention, the marathon activity data includes, but is not limited to, "marathon activity venue data, marathon participant data, and marathon activity time data", the individual activity data includes, but is not limited to, "individual real-time speed data, individual real-time position data, and individual real-time action trajectory data", the road inspection data includes, but is not limited to, "road image data and road position data", and the individual activity data includes, but is not limited to, "individual real-time speed data and individual real-time heart rate data". Preferably, the individual real-time speed data is used in the present invention.

[0043] Specifically, under the condition of determining the abnormal road area, the abnormal road judgment module determines the abnormal road area according to the individual activity data within the preset duration and the road inspection data obtained by the intelligent inspection robot.

[0044] If there is an abnormal fluctuation in the individual activity data within the preset duration or the road inspection data is abnormal, the abnormal road judgment module determines that the road area is an abnormal road area.

[0045] If there is no abnormal fluctuation in the individual activity data within the preset duration or the road inspection data is normal, the abnormal road judgment module determines that the road area is a normal road area.

[0046] In the embodiments of the present invention, the preset duration is set to 2 minutes. The abnormal road judgment module determines that there is an abnormal fluctuation in the individual activity data within the preset duration based on the judgment result that the change amplitude of the individual activity data within the preset duration is greater than the preset change amplitude or the change trend of the individual activity data within the preset duration is irregular. The preset change amplitude is one-tenth of the individual activity data before the change. The change trend of the individual activity data within the preset duration being irregular includes that the change trend of the individual real-time speed presents a concave or convex area in the time-speed dimension. The abnormal road judgment module determines that the road inspection data is abnormal based on the image data of road damage or debris on the road in the road inspection data. However, the above values are not limited to this, and those skilled in the art can also adjust these values according to actual needs.

[0047] In an embodiment of the present invention, it is assumed that the speed of an athlete at a certain moment is 5 m / s, then the preset change range is 0.5 m / s. Within the preset duration of 2 minutes, if the speed change range of the athlete exceeds 0.5 m / s, or the speed change trend presents concave or convex regions in the time-speed dimension, it is considered that there are abnormal fluctuations in the single-entity activity data of the athlete. During the inspection process, the intelligent inspection robot analyzes the captured road images through image recognition technology. If it is found that there are damages such as cracks and potholes on the road, or there are sundries (such as branches and garbage) on the road, it is determined that the road inspection data is abnormal. If within the preset duration, there are abnormal fluctuations in the single-entity activity data corresponding to a certain section of the road, or the road inspection data of this section of the road is abnormal, then this section of the road is determined as an abnormal road area; on the contrary, if there are no abnormal fluctuations in the single-entity activity data and the road inspection data is normal, it is determined that this section of the road is a normal road area. For example, when the game reaches the 30th minute, the intelligent inspection robot finds a crack about 20 cm long on a 500-meter-long road. At the same time, the speed change ranges of 3 athletes near this section of the road exceed the preset change range within 2 minutes, and the speed change trend presents a concave region. According to the rules of the abnormal road judgment module, this 500-meter section of the road is determined as an abnormal road area.

[0048] The present invention can more accurately judge whether there are abnormalities in the road by the change range and change trend of the single-entity activity data within the preset duration, combined with the road inspection data obtained by the intelligent inspection robot. For example, when the speed change range of the athlete exceeds the preset value (such as 0.5 m / s), it indicates that the single entity has a large speed change range due to road damage, or when the speed change trend presents concave or convex shapes, it indicates that the single entity needs to decelerate first and then accelerate due to road obstacles, then it can be judged that there are abnormalities in this area. By the above method, the accuracy of road data analysis in the process of marathon event activities is improved, and thus the urban road maintenance efficiency is improved.

[0049] Specifically, under the condition of determining the detection priority of the abnormal road area, the abnormal road detection module determines the detection priority of the abnormal road area according to the distance between the abnormal road area and the key monitoring area and the number of single-entity activity data with abnormal fluctuations in the abnormal road area;

[0050] If the distance between the abnormal road area and the key monitoring area is less than the preset distance or the number of single-entity activity data with abnormal fluctuations in the abnormal road area is greater than the preset number, the abnormal road detection module determines that the detection priority of the abnormal road area is increased by one level;

[0051] If the distance between the abnormal road area and the key monitoring area is greater than or equal to the preset distance or the number of single activity data with abnormal fluctuations in the abnormal road area is less than the preset number, the abnormal road detection module determines that the detection priority of the abnormal road area remains unchanged.

[0052] In the embodiment of the present invention, the preset distance is set to 100 meters, the preset number is one-tenth of the total number of single participants in the marathon activity, the key monitoring area includes the starting area, the ending area and the supply area of the marathon activity venue, and the basic detection priority of the abnormal road area is all level one. The distance from this abnormal road area to the nearest supply station is only 70 meters, less than the preset 100 meters. At the same time, the number of single activity data with abnormal fluctuations in this area is 10 people, greater than the preset number of 8 people. According to the rules of the abnormal road detection module, it meets the condition of "the distance between the abnormal road area and the key monitoring area is less than the preset distance or the number of single activity data with abnormal fluctuations in the abnormal road area is greater than the preset number", so the priority of this abnormal road area is increased from level one to level three; on the track near the ecological park, the system also detects an abnormal road area. The distance from this area to the nearest key monitoring area (medical aid point) is 150 meters, greater than the preset distance of 100 meters, and the number of single activity data with abnormal fluctuations in this area is 10 people, less than the preset number of 8 people. According to the rules, it meets the condition of "the distance between the abnormal road area and the key monitoring area is greater than or equal to the preset distance or the number of single activity data with abnormal fluctuations in the abnormal road area is greater than the preset number", so the detection priority of this abnormal road area is increased from level one to level two.

[0053] By setting the preset distance and the preset number in the present invention, the system can accurately identify which abnormal road areas need to be processed preferentially. For example, abnormal areas closer to the starting point, ending point or supply area, or areas with a larger number of abnormal single activity data, will be detected and processed preferentially. The detection priority is dynamically adjusted according to the actual situation, avoiding waste and inefficient allocation of resources. For example, when an abnormal area is closer to the key monitoring area and has more abnormal data, the priority will be increased, so as to ensure that key areas are promptly concerned. Through the above method, the accuracy of road data analysis during the marathon event process is improved, and thus the urban road maintenance efficiency is improved.

[0054] Specifically, under the condition of determining the maintenance resource scheduling amount of the abnormal road area, the abnormal road maintenance module determines the maintenance resource scheduling amount of the abnormal road area according to the detection priority of the abnormal road area and the recurrence frequency of the abnormal road area;

[0055] The maintenance resource scheduling quantity of the abnormal road area is the sum of the product of the detection priority and the detection priority weight of the abnormal road area and the product of the recurrence frequency and the recurrence frequency weight of the abnormal road area.

[0056] In the embodiments of the present invention, the detection priority weight and the recurrence frequency weight can be determined by methods such as the Analytic Hierarchy Process (AHP) and the Improved Weighted Random Forest Algorithm (IWRF), through expert investigation and model verification, to determine the detection priority weight and the recurrence frequency weight.

[0057] In the embodiments of the present invention, the system needs to preset a mapping relationship between the resource scheduling volume and resource allocation in advance, match the calculated scheduling volume value with specific maintenance resources (such as personnel, vehicles, materials, etc.). This mapping relationship can be adjusted according to historical data and actual requirements. For example: High scheduling volume (such as 1.8 and above): corresponding to high-priority areas, requiring quick response and a large amount of resource investment. Personnel configuration: 5 professional maintenance personnel, vehicle configuration: 2 maintenance vehicles; Medium scheduling volume (such as 1.2 - 1.8): corresponding to medium-priority areas, requiring moderate response and an appropriate amount of resource investment. Personnel configuration: 3 maintenance personnel, vehicle configuration: 1 maintenance vehicle; Low scheduling volume (such as below 1.2): corresponding to low-priority areas, requiring regular response and less resource investment. Personnel configuration: 2 maintenance personnel, vehicle configuration: 1 maintenance vehicle (or adjusted according to actual situation). Assuming the detection priority weight is set to 0.6 and the repeated occurrence frequency weight is set to 0.4. If the detection priority of the abnormal road area is level three, and assuming that in the past 2 hours, the frequency of repeated abnormalities in this area is 0.3 (that is, there are 3 abnormalities, detected once every 20 minutes for a total of 6 detections). According to the formula, the maintenance resource scheduling volume = the detection priority of the abnormal road area × the detection priority weight + the repeated occurrence frequency of the abnormal road area × the repeated occurrence frequency weight, that is, the maintenance resource scheduling volume = 3 × 0.6 + 0.3 × 0.4 = 1.8 + 0.12 = 1.92. Based on this value, the system arranged a maintenance team consisting of 5 professional maintenance personnel, 2 maintenance vehicles, and sufficient filling materials and cleaning tools to go to this area. After arriving at the scene, the staff quickly cleared the stones on the road surface, filled and leveled the small potholes using professional equipment to ensure that the contestants could pass through this section safely and smoothly; If the detection priority of the abnormal road area is level two, and assuming that in the past 2 hours, the frequency of repeated abnormalities in this area is 0.2 (that is, there are 2 abnormalities), then the maintenance resource scheduling volume = 2 × 0.6 + 0.2 × 0.4 = 1.2 + 0.08 = 1.28. The system dispatched a team consisting of 3 maintenance personnel and 1 maintenance vehicle, carrying corresponding basic maintenance tools to this area. After the staff arrived, they first conducted a detailed assessment of the road surface conditions, immediately dealt with some small problems, and marked the more serious problems for comprehensive repair after the event ended.

[0058] Through the quantitative analysis of the detection priority and recurrence frequency, the system of the present invention can accurately calculate the amount of maintenance resource scheduling for each abnormal road area. This data-based decision-making method avoids the blindness of resource allocation, ensures that resources are reasonably allocated to the areas most in need, and can dynamically adjust the amount of resource scheduling according to the actual conditions of the abnormal road areas to flexibly respond to different situations. For example, areas with high priority and frequent abnormalities will receive more resource support. For areas with high detection priority and high recurrence frequency, the system will allocate more resources to ensure that these areas can be quickly processed. Through the clear mapping relationship between the amount of resource scheduling and resource allocation, the maintenance team can quickly understand the urgency of tasks and the required resources, thereby optimizing the maintenance process and improving work efficiency. By accurately calculating the amount of maintenance resource scheduling, the system avoids over-investment in low-priority areas and reduces unnecessary resource waste.

[0059] Specifically, under the condition of determining whether to adjust the preset duration and whether to update the calculation model of the maintenance resource scheduling amount, the adjustment module determines whether to adjust the preset duration and update the calculation model of the maintenance resource scheduling amount according to the comparison result between the utilization rate of the maintenance resources and the preset utilization rate;

[0060] If the utilization rate of the maintenance resources is less than the preset utilization rate, the adjustment module determines to adjust the preset duration and update the calculation model of the maintenance resource scheduling amount;

[0061] If the utilization rate of the maintenance resources is greater than or equal to the preset utilization rate, the adjustment module determines that there is no need to adjust the preset duration and there is no need to update the calculation model of the maintenance resource scheduling amount.

[0062] In the embodiment of the present invention, updating the calculation model of the maintenance resource scheduling amount includes re-determining the detection priority weight and the recurrence frequency weight. The utilization rate of the maintenance resources is the ratio of the actually used maintenance resources to the allocated maintenance resources. The value range of the preset utilization rate is set to 0.75 - 0.95, and the preferred value of the preset utilization rate is 0.82, but the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0063] Specifically, under the condition of determining to adjust the preset duration and update the calculation model of the maintenance resource scheduling amount, the adjustment module determines to adjust the preset duration with an adjustment coefficient.

[0064] In the embodiment of the present invention, the value range of the adjustment coefficient is set to 0.83 - 0.96, the preferred value of the adjustment coefficient is 0.91, and the adjustment amount of the preset duration is positively correlated with the utilization rate of the maintenance resources, but the above values are not limited to this, and those skilled in the art can also adjust the values according to actual needs.

[0065] In the present invention, by comparing the actual utilization rate of maintenance resources with the preset utilization rate, the system can dynamically adjust the preset duration. If the utilization rate is lower than the preset value, it indicates that the current preset duration may lead to unreasonable resource allocation. The system will shorten the preset duration through an adjustment coefficient, thereby improving the response speed and utilization efficiency of resources. When the utilization rate is lower than the preset value, the system will also re-determine the detection priority weight and the repeated occurrence frequency weight, and update the calculation model of the maintenance resource scheduling volume. This dynamic update mechanism can ensure more scientific resource allocation and avoid resource waste. Through the above method, the accuracy of road data analysis during the marathon event process is improved, and thus the urban road maintenance efficiency is enhanced.

[0066] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

Claims

1. An intelligent urban road maintenance system, characterized in that: include: A data acquisition module, which includes a data acquisition unit for acquiring marathon event data, event venue data, and single-unit event data, and an intelligent inspection robot for conducting road inspections on marathon event roads; An abnormal road judgment module, which is connected to the data acquisition module and is used to determine the abnormal road area based on the single-unit activity data within a preset time period and the road inspection data acquired by the intelligent inspection robot; An abnormal road detection module, which is connected to the abnormal road judgment module, is used to determine the detection priority of the abnormal road area according to the distance between the abnormal road area and the key monitoring area and the number of monomer activity data with abnormal fluctuations in the abnormal road area; An abnormal road maintenance module, which is connected to the abnormal road detection module and is used to determine the maintenance resource scheduling amount of the abnormal road area according to the detection priority of the abnormal road area and the recurrence frequency of the abnormal road area; An adjustment module is respectively connected to the abnormal road judgment module and the abnormal road maintenance module, and is used to determine the adjustment preset time and update the maintenance resource scheduling calculation model according to the utilization rate of the maintenance resources.

2. The urban road intelligent maintenance system according to claim 1, characterized in that: The abnormal road judgment module determines the abnormal road area including: Under the condition that there is abnormal fluctuation in the single-unit activity data within a preset time period or the road inspection data is abnormal, determining the road area as an abnormal road area; Alternatively, under the condition that there is no abnormal fluctuation in the single-unit activity data within a preset time period or the road inspection data is normal, the road area is determined to be a normal road area.

3. The urban road intelligent maintenance system according to claim 2 is characterized in that: The abnormal road judgment module determines that there is abnormal fluctuation in the monomer activity data within the preset time period based on the judgment result that the change range of the monomer activity data within the preset time period is greater than the preset change range or the change trend of the monomer activity data within the preset time period is irregular.

4. The urban road intelligent maintenance system according to claim 2, characterized in that: The abnormal road judgment module determines that the road inspection data is abnormal based on image data showing road damage or debris in the road inspection data.

5. The urban road intelligent maintenance system according to claim 4, characterized in that: The abnormal road detection module determines the detection priority of the abnormal road area including: Under the condition that the distance between the abnormal road area and the key monitoring area is less than a preset distance or the number of single-unit activity data with abnormal fluctuations in the abnormal road area is greater than a preset number, it is determined that the detection priority of the abnormal road area is increased by one level.

6. The urban road intelligent maintenance system according to claim 5, characterized in that: The preset number is determined based on the total number of participants in the marathon event.

7. The urban road intelligent maintenance system according to claim 6, characterized in that: The key monitoring areas include the starting area, the finishing area and the supply area of ​​the marathon venue.

8. The urban road intelligent maintenance system according to claim 7, characterized in that: The abnormal road maintenance module determines the maintenance resource scheduling amount of the abnormal road area including: The maintenance resource scheduling amount of the abnormal road area is the sum of the product of the detection priority of the abnormal road area and the detection priority weight and the product of the recurrence frequency of the abnormal road area and the recurrence frequency weight.

9. The urban road intelligent maintenance system according to claim 8, characterized in that: The adjustment module determines to adjust the preset time and update the maintenance resource scheduling calculation model, including determining to adjust the preset time and update the maintenance resource scheduling calculation model under the condition that the utilization rate of the maintenance resources is less than the preset utilization rate.

10. The urban road intelligent maintenance system according to claim 9, characterized in that: The adjustment amount of the preset duration is positively correlated with the utilization rate of the maintenance resources.

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