An intelligent decision-making method for highway asphalt pavement maintenance

Abstract

The invention discloses an intelligent decision-making method for highway asphalt pavement maintenance, which relates to the technical field of pavement maintenance. Through detailed analysis of road section driving behavior data, such as: average vehicle speed, number of lane changes and lane change reaction time, the maintenance risk of the highway section can be more accurately evaluated, so as to formulate targeted maintenance strategies, evaluate maintenance risks, and ensure that maintenance decisions are based on objective data rather than subjective judgments. By analyzing the maintenance safety evaluation coefficients of maintenance groups at each collection time point, it is possible to timely judge whether the maintenance safety is qualified, and issue an early warning prompt when the safety is unqualified, effectively preventing and reducing safety hazards. Through scientific data analysis, real-time safety monitoring and dynamic resource allocation, the scientific nature of maintenance decisions, the safety of the maintenance process and the utilization efficiency of maintenance resources are significantly improved, and the intelligence and automation level of maintenance management are also improved.

Description

Technical Field

[0001] The invention relates to the technical field of pavement maintenance, and in particular to an intelligent decision-making method for highway asphalt pavement maintenance. Background Art

[0002] The maintenance of asphalt pavement on highways is an important part of ensuring traffic safety, improving driving comfort and extending the service life of the pavement. With the continuous increase in traffic volume and the diversification of vehicle types and driving speeds, the pavement of highways is under increasing pressure, resulting in accelerated damage and aging of the pavement. Therefore, scientific and effective maintenance management is particularly important.

[0003] Prior art, such as an invention patent application with announcement number CN117876182B, discloses a highway maintenance decision-making management method based on multi-source data analysis, comprising: extracting a highway image by frame extraction of monitoring video segments in the highway's own video monitoring system, screening the images to generate a highway image set; splicing the highway image set using an image stitching method to obtain a complete highway image and an original highway complete image; judging the highway maintenance demand through a highway maintenance demand analysis model; obtaining the duration required for highway maintenance based on big data analysis; analyzing the historical traffic flow of the path in the complete image of the highway through the highway's own video monitoring system; judging the impact of the maintenance project on the traffic based on the historical traffic flow of the path and the maintenance project, making reminder decisions on the maintenance time of the highway to avoid the normal operation of highway traffic being affected by maintenance.

[0004] The above scheme has the following technical problems: 1. Existing maintenance decisions often rely on experience and intuition, lack of scientific analysis based on actual road driving behavior data, which easily leads to inaccurate decisions and inability to effectively deal with various maintenance risks. At the same time, during the maintenance process, there is a lack of real-time safety monitoring measures, which makes it difficult to discover potential safety hazards in a timely manner, increasing the risk of accidents, thereby threatening the safety of maintenance personnel and passing drivers. The maintenance standards in different regions are different, and there is a lack of systematic and standardized guidance, resulting in uneven maintenance management levels in different sections, affecting the overall safety and traffic capacity of the highway. The lack of effective data feedback and improvement mechanism makes it impossible to continuously optimize maintenance management based on historical data, affecting the long-term maintenance effect and the service life of the highway.

[0005] 2. Existing methods make it difficult to dynamically adjust resource allocation according to changes in maintenance risks, which may lead to excessive or insufficient allocation of maintenance personnel and equipment, waste of resources or reduced maintenance efficiency. At the same time, traditional manual inspections and data recording methods are inefficient, highly subjective, and prone to errors, resulting in the inability to fully and accurately evaluate maintenance effects. There is usually no perfect early warning mechanism, and it is impossible to issue warnings in time when maintenance safety problems arise, which will pose a potential threat to the safety of the maintenance process. In the event of maintenance safety problems, existing technologies often cannot respond quickly and lack the ability to quickly adjust and optimize maintenance plans, increasing the complexity of maintenance management. Summary of the invention

[0006] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide an intelligent decision-making method for highway asphalt pavement maintenance.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solution: The present invention provides an intelligent decision-making method for highway asphalt pavement maintenance, comprising:

[0008] Step 1: Assessment of maintenance risk: Before the target highway section is maintained, the driving behavior data of the target highway section corresponding to each historical maintenance cycle is obtained. The driving behavior data of the target highway section includes the average vehicle speed, the number of lane changes of each vehicle, and the lane change reaction time. The driving behavior data assessment coefficient of the target highway section is obtained by analysis, and then the maintenance risk corresponding to the target highway section is assessed, and the maintenance execution standard corresponding to the target highway section is analyzed;

[0009] Step 2: Analysis of maintenance conditions: used to obtain the number of vehicles of various types and the number of replacement sections corresponding to the target expressway section, and then analyze the maintenance conditions corresponding to the target expressway section, including full-closed maintenance and semi-closed maintenance;

[0010] Step 3: Determination of maintenance safety: If the target highway section needs semi-closed maintenance, the area of ​​the semi-closed area of ​​the target highway section is analyzed. At the same time, during the maintenance process of the target highway section, several collection time points are set to collect maintenance safety data corresponding to the maintenance group at each collection time point. The maintenance safety data includes the number of each type of safety management equipment, the field of view angle, and the distance between each maintenance personnel and the maintenance boundary. Then, the maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is analyzed to determine whether the maintenance safety of the maintenance group at each collection time point is qualified.

[0011] Step 4: Decision-making on maintenance safety: When the maintenance safety of a maintenance group at a certain collection time point is unqualified, an early warning will be issued, and an analysis of the maintenance safety decision of the maintenance group at that collection time point will be conducted.

[0012] Preferably, the analysis obtains the road section driving behavior data evaluation coefficient corresponding to the target highway section, and the specific analysis process is as follows:

[0013] The average vehicle speed corresponding to each historical maintenance period of the target highway section, the number of lane changes corresponding to each vehicle, and the lane change reaction time are recorded as , and ,in, Indicates the number corresponding to each historical maintenance cycle, , u is a positive integer, Indicates the number corresponding to each vehicle, , m is a positive integer, substitute into the calculation formula The evaluation coefficient of the driving behavior data corresponding to the target highway section is obtained ,in, , , They are the standard average vehicle speed, standard lane change times and standard lane change reaction time corresponding to the set highway section. , , They are the weight factor corresponding to the average speed of vehicles on the set highway section, the weight factor corresponding to the number of lane changes, and the weight factor corresponding to the lane change reaction time. , , They are respectively the difference in the average speed of vehicles allowed on the set highway section, the difference in the standard number of lane changes allowed for vehicles, and the difference in the reaction time of the allowed standard lane changes. e represents a natural constant.

[0014] Preferably, the maintenance execution standard corresponding to the target highway section is analyzed, and the specific analysis process is as follows: A1. If the maintenance risk corresponding to the target highway section is "low risk", the target highway section is marked as "low risk". At the same time, the maintenance execution standard corresponding to "low risk" is extracted from the database. The maintenance execution standard is: 1 to 2 maintenance personnel are assigned to be responsible for daily inspection and maintenance, the number of maintenance equipment required is listed, the frequency of maintenance work is set to conduct a comprehensive inspection and maintenance once a week, and the maintenance execution standard corresponding to the target highway section is sent to the maintenance management platform.

[0015] A2. If the maintenance risk corresponding to the target highway section is "medium risk", the target highway section is marked as "medium risk". At the same time, the maintenance implementation standard corresponding to "medium risk" is extracted from the database. The maintenance implementation standard is: 2 to 3 maintenance personnel are assigned to be responsible for daily inspection and maintenance, and the number of maintenance equipment required is listed. At the same time, the number of specific equipment is increased, and the frequency of maintenance work is set to conduct a comprehensive inspection and maintenance once a month, and the maintenance implementation standard corresponding to the target highway section is sent to the maintenance management platform.

[0016] A3. If the maintenance risk corresponding to the target highway section is "high risk", the target highway section is marked as "high risk". At the same time, the maintenance implementation standard corresponding to "high risk" is extracted from the database. The maintenance implementation standard is: more than 3 maintenance personnel are assigned to be responsible for daily inspection and maintenance, and the number of maintenance equipment required is listed. At the same time, the number of specific equipment and advanced monitoring equipment and repair tools are increased, and the frequency of maintenance work is set to conduct a comprehensive inspection and maintenance once a quarter, and the maintenance implementation standard corresponding to the target highway section is sent to the maintenance management platform.

[0017] Preferably, the maintenance condition corresponding to the target highway section is analyzed, and the specific analysis process is as follows:

[0018] S1. Use traffic monitoring equipment to collect vehicle traffic data on the target highway section, classify and count according to vehicle type, obtain the number of vehicles of each type, use geographic information system software, input the coordinate information of the target highway, search the nearby road network to identify potential replacement sections, determine the data corresponding to the roads parallel to the target section, and record it as the number of replacement sections;

[0019] S2. Compare the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section with the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section. If the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section are both greater than or equal to the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section, it means that the target expressway section can be semi-closed for maintenance. If one of the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section is less than the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section, it means that the target expressway section needs to be fully closed for maintenance.

[0020] Preferably, the area of ​​the semi-enclosed area of ​​the target highway section is analyzed, and the specific analysis process is as follows:

[0021] D1. Use geographic information system software to input the coordinate information of the target road section, obtain the lane length and lane width corresponding to the target highway section, and extract the corresponding traffic volume during the semi-closed maintenance of the historical target highway section from the database;

[0022] D2. Using traffic flow theory, the length and width of the lanes corresponding to the target expressway section are brought into the macro traffic flow model to simulate the changes in traffic flow, evaluate the level of traffic congestion that may occur during the semi-closed maintenance period, and compare the traffic congestion level corresponding to the target expressway section with the traffic congestion level corresponding to the area of ​​each semi-closed area in the database. If the traffic congestion level corresponding to the target expressway section is the same as the traffic congestion level corresponding to a semi-closed area in the database, the area of ​​the semi-closed area in the database will be used as the semi-closed area area of ​​the target expressway section.

[0023] Preferably, the maintenance safety data corresponding to the maintenance group is collected at each collection time point, and the specific collection process is as follows: B1. Use a drone equipped with a high-definition camera or a multi-spectral sensor to take aerial photos of the target highway section, and obtain high-resolution image data corresponding to the maintenance group at each collection time point. Use remote sensing image processing software to interpret the collected high-resolution image data to identify the type and quantity corresponding to each type of safety management equipment.

[0024] B2. Obtain high-resolution image data from drones or other sources, record the specific location coordinates of the device, obtain the digital elevation model of the target area through high-resolution image data to provide terrain information, run the viewshed analysis tool, and calculate the viewshed range of the device according to the set parameters. After the analysis is completed, the GIS software will generate a viewshed map, superimpose the viewshed analysis results with the high-resolution image, and intuitively display the actual viewshed range of the device.

[0025] B3. Equip maintenance personnel with GPS positioning devices or smart phones to record their location data in real time during work, set clear marks at the maintenance boundaries, integrate the GPS data of maintenance personnel with the maintenance boundary data in the GIS database, use GIS software for spatial analysis, calculate the distance between maintenance personnel and the maintenance boundary, obtain accurate distance data through the point-to-line distance calculation function, and use the activity trajectory of maintenance personnel to generate a heat map to obtain the corresponding distance between each maintenance personnel and the maintenance boundary.

[0026] Preferably, the analysis obtains the maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point, and the specific analysis process is as follows:

[0027] The number of each type of safety management equipment in the maintenance group at each collection time point, the field of view angle, and the distance between each maintenance personnel and the maintenance boundary are recorded as , and ,in, Indicates the number corresponding to each collection time point, , p is a positive integer, Indicates the number corresponding to each type of security management equipment. , q is a positive integer, Indicates the number corresponding to each maintenance personnel and maintenance boundary, , n is a positive integer, substitute into the calculation formula The maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is obtained ,in, , , They are the standard number of safety management equipment, standard viewing angle, and standard distance between maintenance personnel and maintenance boundaries. , , They are the weight factor corresponding to the number of safety management devices set, the weight factor corresponding to the field of view angle, and the weight factor corresponding to the distance between the maintenance personnel and the maintenance limit.

[0028] Preferably, the determination of whether the maintenance safety of the maintenance group at each collection time point is qualified is specifically carried out as follows: the maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is compared with the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group. If the maintenance safety assessment coefficient corresponding to the maintenance group at a certain collection time point is within the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group, then it is determined that the maintenance safety of the maintenance group at the collection time point is qualified. If the maintenance safety assessment coefficient corresponding to the maintenance group at a certain collection time point is not within the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group, then it is determined that the maintenance safety of the maintenance group at the collection time point is unqualified. In this way, it is determined whether the maintenance safety of the maintenance group at each collection time point is qualified.

[0029] Preferably, the analysis of the maintenance safety decision of the maintenance group at the collection time point is specifically performed as follows: C1. If the maintenance safety of the maintenance group at a certain collection time point is unqualified, an early warning prompt is given to the maintenance group, the early warning prompt light of the maintenance group at the collection time point emits a yellow alarm, and the maintenance group is notified to conduct a maintenance safety inspection.

[0030] C2. If the maintenance safety of a maintenance group at a certain collection time point is unqualified, and the total number of times the maintenance safety of the maintenance group fails to meet the standards during the maintenance of the target expressway section is counted, if the total number of times the maintenance group fails to meet the standards during the maintenance of the target expressway section is greater than the set number of times the maintenance safety fails to meet the standards during the maintenance of the standard expressway section, an early warning will be issued to the maintenance group, the early warning indicator of the maintenance group at the collection time point will be turned red, and the maintenance group will be notified to conduct a maintenance safety inspection. At the same time, the maintenance group will be recalled and provided with maintenance safety training.

[0031] The beneficial effects of the present invention are as follows: 1. The embodiments of the present invention can more accurately assess the maintenance risk of highway sections through detailed analysis of road section driving behavior data, such as average vehicle speed, number of lane changes, and lane change reaction time, so as to formulate targeted maintenance strategies. By using historical data and set standard parameters, it is possible to generate a road section driving behavior data evaluation coefficient, and then assess the maintenance risk, ensuring that maintenance decisions are based on objective data rather than subjective judgment.

[0032] 2. In the embodiment of the present invention, during the maintenance process, by setting multiple collection time points, the number of safety management equipment of the maintenance group, the field of view angle, and the distance between the maintenance personnel and the maintenance boundary are monitored and evaluated in real time to ensure the maintenance safety at each time point. By analyzing the maintenance safety evaluation coefficient of the maintenance group at each collection time point, it is possible to timely determine whether the maintenance safety is qualified, and issue an early warning prompt when the safety is unqualified, effectively preventing and reducing safety hazards. Through scientific data analysis, real-time safety monitoring and dynamic resource allocation, the scientific nature of maintenance decisions, the safety of the maintenance process and the utilization efficiency of maintenance resources are significantly improved, while also improving the intelligence and automation level of maintenance management.

[0033] 3. The embodiment of the present invention dynamically adjusts the maintenance execution standards, including the number of maintenance personnel, maintenance equipment configuration and maintenance work frequency, according to different maintenance risk levels, such as low risk, medium risk and high risk, to achieve reasonable allocation of resources and improve maintenance efficiency. Through multi-sensor technologies such as drones and high-definition cameras, maintenance safety data can be efficiently collected, manual intervention can be reduced, and the accuracy and efficiency of data collection can be improved.

[0034] 4. The embodiment of the present invention reduces the errors of manual calculation and judgment by automatically calculating and analyzing the maintenance safety assessment coefficients at each acquisition time point, and improves the intelligence and automation level of decision-making. When the maintenance safety is unqualified, an early warning prompt can be automatically issued, and the maintenance safety decision can be analyzed according to the specific situation, which enhances the response speed of maintenance management and the flexibility of decision-making.

[0035] 5. The embodiment of the present invention accurately evaluates the traffic conditions of the target road section. This helps to formulate a scientific maintenance plan, select an appropriate maintenance method (fully closed or semi-closed), ensure the effectiveness and necessity of the maintenance work, and at the same time, reasonably arrange the time for maintenance construction. Choosing to carry out semi-closed maintenance during periods of low traffic flow can reduce the impact on traffic. By analyzing the area of ​​the semi-closed area, reasonable safety warning signs and traffic guidance can be set to ensure a clear division between the construction area and the normal traffic area. This will greatly reduce the risk of accidents during construction, ensure the safety of construction personnel and passing vehicles, optimize maintenance methods and construction arrangements, reduce the impact on user travel, and can improve the public's satisfaction with highway maintenance work and enhance trust with the public. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Embodiments of the present invention Figure 1 As shown, an intelligent decision-making method for highway asphalt pavement maintenance includes: step 1, assessment of maintenance risk: before maintaining a target highway section, obtaining section driving behavior data corresponding to each historical maintenance cycle of the target highway section, the section driving behavior data including the average vehicle speed, the number of lane changes corresponding to each vehicle and the lane change reaction time, so as to analyze and obtain the section driving behavior data assessment coefficient corresponding to the target highway section, and then assess the maintenance risk corresponding to the target highway section, and analyze the maintenance execution standard corresponding to the target highway section.

[0040] The embodiments of the present invention can more accurately assess the maintenance risk of highway sections through detailed analysis of road section driving behavior data, such as average vehicle speed, number of lane changes, and lane change reaction time, so as to formulate targeted maintenance strategies. By using historical data and set standard parameters, it is possible to generate a road section driving behavior data evaluation coefficient, and then assess the maintenance risk, ensuring that maintenance decisions are based on objective data rather than subjective judgment.

[0041] In a specific embodiment, the analysis obtains the road section driving behavior data evaluation coefficient corresponding to the target highway section, and the specific analysis process is as follows:

[0042] The average vehicle speed corresponding to each historical maintenance period of the target highway section, the number of lane changes corresponding to each vehicle, and the lane change reaction time are recorded as , and ,in, Indicates the number corresponding to each historical maintenance cycle, , u is a positive integer, Indicates the number corresponding to each vehicle, , m is a positive integer, substitute into the calculation formula The evaluation coefficient of the driving behavior data corresponding to the target highway section is obtained ,in, , , They are the standard average vehicle speed, standard lane change times and standard lane change reaction time corresponding to the set highway section. , , They are the weight factor corresponding to the average speed of vehicles on the set highway section, the weight factor corresponding to the number of lane changes, and the weight factor corresponding to the lane change reaction time. , , They are respectively the difference in the average speed of vehicles allowed on the set highway section, the difference in the standard number of lane changes allowed for vehicles, and the difference in the reaction time of the allowed standard lane changes. e represents a natural constant.

[0043] It should be noted that , , Both are greater than 0 and less than 1.

[0044] It should also be noted that through the summary of a large amount of research data and experimental data. Professional institutions and research institutions set the standard average vehicle speed corresponding to the highway section, the standard number of lane changes corresponding to the vehicle, and the standard lane change reaction time. At the same time, based on the professional knowledge and research basis of experts in the field, and discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the average vehicle speed of the highway section, the weight factors corresponding to the number of vehicle lane changes, and the weight factors corresponding to the lane change reaction time based on their own experience and knowledge, and set the difference in the average vehicle speed allowed for the highway section, the difference in the standard number of lane changes allowed for the vehicle, and the difference in the standard lane change reaction time allowed.

[0045] It should be noted again that the standard vehicle average driving speed, the standard number of lane changes corresponding to the vehicle, and the standard lane change reaction time are the vehicle average driving speed threshold, the vehicle corresponding lane change number threshold, and the lane change reaction time threshold, respectively. For example, the standard vehicle average driving speed is 40, the standard number of lane changes corresponding to the vehicle is 2, and the standard lane change reaction time is 2.

[0046] It should also be noted that the lane change reaction time is the time from when the driver perceives the maintenance measure traffic sign to when he actually slows down or changes lanes.

[0047] It should also be noted that installing radar speed measuring devices on designated roads can monitor the speed of each vehicle in real time, record historical data, and average the speed of each vehicle to obtain the average vehicle speed. The vehicle driving process is recorded by a high-resolution camera, and the vehicle's lane changing behavior is analyzed using image recognition and machine learning algorithms. When the vehicle approaches a maintenance sign, the sensor on the side of the road triggers the system that records the reaction time. By recording the relative position and time of the vehicle and the traffic sign, the time from the driver seeing the sign to starting to slow down or change lanes is analyzed.

[0048] In another specific embodiment, the maintenance risk corresponding to the target highway section is evaluated, and the specific analysis process is as follows: the section driving behavior data evaluation coefficient corresponding to the target highway section is compared with the section driving behavior data evaluation coefficient interval corresponding to each maintenance risk in the database; if the section driving behavior data evaluation coefficient corresponding to the target highway section is within the section driving behavior data evaluation coefficient interval corresponding to a maintenance risk in the database, then the maintenance risk in the database is recorded as the maintenance risk corresponding to the target highway section.

[0049] It should be noted that various maintenance risks include: low risk, medium risk and high risk.

[0050] In another specific embodiment, the maintenance execution standards corresponding to the target highway section are analyzed, and the specific analysis process is as follows: A1. If the maintenance risk corresponding to the target highway section is "low risk", the target highway section is marked as "low risk". At the same time, the maintenance execution standards corresponding to "low risk" are extracted from the database. The maintenance execution standards are: 1 to 2 maintenance personnel are assigned to be responsible for daily inspection and maintenance, the number of maintenance equipment required is listed, the frequency of maintenance work is set to conduct comprehensive inspection and maintenance once a week, and the maintenance execution standards corresponding to the target highway section are sent to the maintenance management platform.

[0051] A2. If the maintenance risk corresponding to the target highway section is "medium risk", the target highway section is marked as "medium risk". At the same time, the maintenance implementation standard corresponding to "medium risk" is extracted from the database. The maintenance implementation standard is: 2 to 3 maintenance personnel are assigned to be responsible for daily inspection and maintenance, and the number of maintenance equipment required is listed. At the same time, the number of specific equipment is increased, and the frequency of maintenance work is set to conduct a comprehensive inspection and maintenance once a month, and the maintenance implementation standard corresponding to the target highway section is sent to the maintenance management platform.

[0052] A3. If the maintenance risk corresponding to the target highway section is "high risk", the target highway section is marked as "high risk". At the same time, the maintenance implementation standard corresponding to "high risk" is extracted from the database. The maintenance implementation standard is: more than 3 maintenance personnel are assigned to be responsible for daily inspection and maintenance, and the number of maintenance equipment required is listed. At the same time, the number of specific equipment and advanced monitoring equipment and repair tools are increased, and the frequency of maintenance work is set to conduct a comprehensive inspection and maintenance once a quarter, and the maintenance implementation standard corresponding to the target highway section is sent to the maintenance management platform.

[0053] It should be noted that the specific equipment includes: a road condition assessor for road surface testing and a wear repair tool for daily maintenance and emergency repair.

[0054] The embodiment of the present invention dynamically adjusts the maintenance execution standards, including the number of maintenance personnel, maintenance equipment configuration and maintenance work frequency, according to different maintenance risk levels, such as low risk, medium risk and high risk, to achieve reasonable allocation of resources and improve maintenance efficiency. Through multi-sensor technologies such as drones and high-definition cameras, maintenance safety data can be efficiently collected, manual intervention can be reduced, and the accuracy and efficiency of data collection can be improved.

[0055] Step 2: Analysis of maintenance status: used to obtain the number of vehicles of various types and the number of replacement sections corresponding to the target highway section, and then analyze the maintenance status corresponding to the target highway section, which includes fully closed maintenance and semi-closed maintenance.

[0056] In a specific embodiment, the maintenance condition corresponding to the target highway section is analyzed, and the specific analysis process is as follows:

[0057] S1. Use traffic monitoring equipment to collect vehicle traffic data on the target highway section, classify and count according to vehicle type, obtain the number of vehicles of each type, use geographic information system software, input the coordinate information of the target highway, search the nearby road network to identify potential replacement sections, determine the data corresponding to the roads parallel to the target section, and record it as the number of replacement sections;

[0058] S2. Compare the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section with the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section. If the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section are both greater than or equal to the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section, it means that the target expressway section can be semi-closed for maintenance. If one of the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section is less than the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section, it means that the target expressway section needs to be fully closed for maintenance.

[0059] Step 3: Determination of maintenance safety: If the target highway section requires semi-closed maintenance, the area of ​​the semi-closed region of the target highway section is analyzed. At the same time, during the maintenance process of the target highway section, several collection time points are set to collect maintenance safety data corresponding to the maintenance group at each collection time point. The maintenance safety data includes the number of each type of safety management equipment, the field of view angle, and the distance between each maintenance personnel and the maintenance boundary. The maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is then analyzed to determine whether the maintenance safety of the maintenance group at each collection time point is qualified.

[0060] In a specific embodiment, the area of ​​the semi-enclosed area of ​​the target highway section is analyzed, and the specific analysis process is as follows:

[0061] D1. Use geographic information system software to input the coordinate information of the target road section, obtain the lane length and lane width corresponding to the target highway section, and extract the corresponding traffic volume during the semi-closed maintenance of the historical target highway section from the database;

[0062] D2. Using traffic flow theory, the length and width of the lanes corresponding to the target expressway section are brought into the macro traffic flow model to simulate the changes in traffic flow, evaluate the level of traffic congestion that may occur during the semi-closed maintenance period, and compare the traffic congestion level corresponding to the target expressway section with the traffic congestion level corresponding to the area of ​​each semi-closed area in the database. If the traffic congestion level corresponding to the target expressway section is the same as the traffic congestion level corresponding to a semi-closed area in the database, the area of ​​the semi-closed area in the database will be used as the semi-closed area area of ​​the target expressway section.

[0063] In a specific embodiment, the maintenance safety data includes the number of each type of safety management equipment, the field of view angle, and the distance between each maintenance personnel and the maintenance boundary.

[0064] In another specific embodiment, the maintenance safety data corresponding to the maintenance group is collected at each collection time point, and the specific collection process is as follows: B1. Use a drone equipped with a high-definition camera or a multi-spectral sensor to take aerial photos of the target highway section, and obtain high-resolution image data corresponding to the maintenance group at each collection time point. Use remote sensing image processing software to interpret the collected high-resolution image data to identify the type and quantity corresponding to each type of safety management equipment.

[0065] B2. Obtain high-resolution image data from drones or other sources, record the specific location coordinates of the device, obtain the digital elevation model of the target area through high-resolution image data to provide terrain information, run the viewshed analysis tool, and calculate the viewshed range of the device according to the set parameters. After the analysis is completed, the GIS software will generate a viewshed map, superimpose the viewshed analysis results with the high-resolution image, and intuitively display the actual viewshed range of the device.

[0066] B3. Equip maintenance personnel with GPS positioning devices or smart phones to record their location data in real time during work, set clear marks at the maintenance boundaries, integrate the GPS data of maintenance personnel with the maintenance boundary data in the GIS database, use GIS software for spatial analysis, calculate the distance between maintenance personnel and the maintenance boundary, obtain accurate distance data through the point-to-line distance calculation function, and use the activity trajectory of maintenance personnel to generate a heat map to obtain the corresponding distance between each maintenance personnel and the maintenance boundary.

[0067] In the embodiment of the present invention, during the maintenance process, multiple collection time points are set to monitor and evaluate the number of safety management equipment of the maintenance group, the field of view angle, and the distance between the maintenance personnel and the maintenance boundary in real time, so as to ensure the maintenance safety at each time point. By analyzing the maintenance safety evaluation coefficient of the maintenance group at each collection time point, it is possible to timely judge whether the maintenance safety is qualified, and issue an early warning prompt when the safety is unqualified, effectively preventing and reducing safety hazards. Through scientific data analysis, real-time safety monitoring and dynamic resource allocation, the scientific nature of maintenance decisions, the safety of the maintenance process and the utilization efficiency of maintenance resources are significantly improved, and at the same time, the intelligence and automation level of maintenance management are improved.

[0068] In another specific embodiment, the analysis obtains the maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point, and the specific analysis process is as follows:

[0069] The number of each type of safety management equipment in the maintenance group at each collection time point, the field of view angle, and the distance between each maintenance personnel and the maintenance boundary are recorded as , and ,in, Indicates the number corresponding to each collection time point, , p is a positive integer, Indicates the number corresponding to each type of security management equipment. , q is a positive integer, Indicates the number corresponding to each maintenance personnel and maintenance boundary, , n is a positive integer, substitute into the calculation formula The maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is obtained ,in, , , They are the standard number of safety management equipment, standard viewing angle, and standard distance between maintenance personnel and maintenance boundaries. , , They are the weight factor corresponding to the number of safety management devices set, the weight factor corresponding to the field of view angle, and the weight factor corresponding to the distance between the maintenance personnel and the maintenance limit.

[0070] It should be noted that , , Both are greater than 0 and less than 1.

[0071] It should also be noted that through the summary of a large amount of research data and experimental data, the standard number of safety management equipment, the standard field of view angle, and the standard distance between maintenance personnel and maintenance boundaries are set by professional institutions and research institutions. At the same time, based on the professional knowledge and research basis of experts in the field, they are discussed and confirmed with industry organizations or professional institutions. Experts set the weight factors corresponding to the number of safety management equipment, the weight factors corresponding to the field of view angle, and the weight factors corresponding to the distance between maintenance personnel and maintenance boundaries based on their own experience and knowledge.

[0072] It should be noted again that the standard quantity, standard viewing angle, and standard distance between the maintenance personnel and the maintenance boundary are the quantity threshold, viewing angle threshold, and distance threshold between the maintenance personnel and the maintenance boundary, respectively. For example, the standard quantity is 3, the standard viewing angle is 40, and the standard distance between the maintenance personnel and the maintenance boundary is 3.

[0073] In another specific embodiment, the determination of whether the maintenance safety of the maintenance group at each collection time point is qualified is specifically carried out as follows: the maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is compared with the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group. If the maintenance safety assessment coefficient corresponding to the maintenance group at a certain collection time point is within the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group, then the maintenance safety of the maintenance group at the collection time point is determined to be qualified. If the maintenance safety assessment coefficient corresponding to the maintenance group at a certain collection time point is not within the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group, then the maintenance safety of the maintenance group at the collection time point is determined to be unqualified. In this way, it is determined whether the maintenance safety of the maintenance group at each collection time point is qualified.

[0074] Step 3: Maintenance safety decision: When the maintenance safety of a maintenance group at a certain collection time point is not up to standard, an early warning will be issued, and the maintenance safety decision of the maintenance group at that collection time point will be analyzed.

[0075] In a specific embodiment, the analysis of the maintenance safety decision of the maintenance group at the collection time point is performed, and the specific analysis process is as follows: C1. If the maintenance safety of the maintenance group at a certain collection time point is unqualified, an early warning prompt is given to the maintenance group, the early warning prompt light of the maintenance group at the collection time point emits a yellow alarm, and the maintenance group is notified to conduct a maintenance safety inspection.

[0076] C2. If the maintenance safety of a maintenance group at a certain collection time point is unqualified, and the total number of times the maintenance safety of the maintenance group fails to meet the standards during the maintenance of the target expressway section is counted, if the total number of times the maintenance group fails to meet the standards during the maintenance of the target expressway section is greater than the set number of times the maintenance safety fails to meet the standards during the maintenance of the standard expressway section, an early warning will be issued to the maintenance group, the early warning indicator of the maintenance group at the collection time point will be turned red, and the maintenance group will be notified to conduct a maintenance safety inspection. At the same time, the maintenance group will be recalled and provided with maintenance safety training.

[0077] The embodiment of the present invention reduces the errors of manual calculation and judgment by automatically calculating and analyzing the maintenance safety assessment coefficients at each acquisition time point, and improves the intelligence and automation level of decision-making. When the maintenance safety is unqualified, an early warning prompt can be automatically issued, and the maintenance safety decision can be analyzed according to the specific situation, which enhances the response speed of maintenance management and the flexibility of decision-making.

[0078] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent decision-making method for highway asphalt pavement maintenance, characterized in that: include: Step 1: Assessment of maintenance risk: Before the target highway section is maintained, the driving behavior data of the target highway section corresponding to each historical maintenance cycle is obtained. The driving behavior data of the target highway section includes the average vehicle speed, the number of lane changes of each vehicle, and the lane change reaction time. The driving behavior data assessment coefficient of the target highway section is obtained by analysis, and then the maintenance risk corresponding to the target highway section is assessed, and the maintenance execution standard corresponding to the target highway section is analyzed; The analysis obtains the road section driving behavior data evaluation coefficient corresponding to the target highway section. The specific analysis process is as follows: The average vehicle speed corresponding to each historical maintenance period of the target highway section, the number of lane changes corresponding to each vehicle, and the lane change reaction time are recorded as V k , and Among them, k represents the number corresponding to each historical maintenance cycle, k = 1, 2...u, u is a positive integer, h represents the number corresponding to each vehicle, h = 1, 2...m, m is a positive integer, substitute into the calculation formula Obtain the road section driving behavior data evaluation coefficient Ψ corresponding to the target highway section, wherein V′, N′, and T′ are the standard vehicle average speed, the standard lane change number, and the standard lane change reaction time corresponding to the set highway section, respectively; ψ1, ψ2, and ψ3 are the weight factors corresponding to the vehicle average speed, the vehicle lane change number, and the lane change reaction time of the set highway section, respectively; ΔV, ΔN, and ΔT are the difference in the average speed of vehicles allowed on the set highway section, the difference in the standard lane change number, and the difference in the standard lane change reaction time, respectively; and e represents a natural constant; The specific analysis process of the maintenance risk corresponding to the target highway section is as follows: The road section driving behavior data evaluation coefficient corresponding to the target highway section is compared with the road section driving behavior data evaluation coefficient interval corresponding to each maintenance risk in the database. If the road section driving behavior data evaluation coefficient corresponding to the target highway section is within the road section driving behavior data evaluation coefficient interval corresponding to a maintenance risk in the database, the maintenance risk in the database is recorded as the maintenance risk corresponding to the target highway section; Step 2: Analysis of maintenance conditions: used to obtain the number of vehicles of various types and the number of replacement sections corresponding to the target expressway section, and then analyze the maintenance conditions corresponding to the target expressway section, including full-closed maintenance and semi-closed maintenance; The maintenance situation corresponding to the target expressway section is analyzed, and the specific analysis process is as follows: S1. Use traffic monitoring equipment to collect vehicle traffic data on the target highway section, classify and count according to vehicle type, obtain the number of vehicles of each type, use geographic information system software, input the coordinate information of the target highway, search the nearby road network to identify potential replacement sections, determine the data corresponding to the roads parallel to the target section, and record it as the number of replacement sections; S2. Compare the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section with the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section. If the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section are both greater than or equal to the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section, it means that the target expressway section can be semi-closed for maintenance. If one of the number of vehicles of each type and the number of replacement sections corresponding to the target expressway section is less than the number of vehicles of each type and the number of replacement sections corresponding to the set standard expressway section, it means that the target expressway section needs to be fully closed for maintenance. Step 3: Determination of maintenance safety: If the target highway section needs semi-closed maintenance, the area of ​​the semi-closed area of ​​the target highway section is analyzed. At the same time, during the maintenance process of the target highway section, several collection time points are set to collect maintenance safety data corresponding to the maintenance group at each collection time point. The maintenance safety data includes the number of each type of safety management equipment, the field of view angle, and the distance between each maintenance personnel and the maintenance boundary. Then, the maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is analyzed to determine whether the maintenance safety of the maintenance group at each collection time point is qualified. Step 4: Decision-making on maintenance safety: When the maintenance safety of a maintenance group at a certain collection time point is unqualified, an early warning will be issued, and an analysis of the maintenance safety decision of the maintenance group at that collection time point will be conducted.

2. The intelligent decision-making method for highway asphalt pavement maintenance according to claim 1, characterized in that: The maintenance execution standards corresponding to the target expressway section are analyzed, and the specific analysis process is as follows: A1. If the maintenance risk corresponding to the target expressway section is "low risk", the target expressway section is marked as "low risk". At the same time, the maintenance execution standard corresponding to "low risk" is extracted from the database. The maintenance execution standard is: 1 to 2 maintenance personnel are assigned to be responsible for daily inspection and maintenance, the number of maintenance equipment required is listed, the frequency of maintenance work is set to conduct comprehensive inspection and maintenance once a week, and the maintenance execution standard corresponding to the target expressway section is sent to the maintenance management platform; A2. If the maintenance risk corresponding to the target expressway section is "medium risk", the target expressway section is marked as "medium risk". At the same time, the maintenance execution standard corresponding to "medium risk" is extracted from the database. The maintenance execution standard is: 2 to 3 maintenance personnel are assigned to be responsible for daily inspection and maintenance, and the number of maintenance equipment required is listed. At the same time, the frequency of maintenance work is set to conduct a comprehensive inspection and maintenance once a month, and the maintenance execution standard corresponding to the target expressway section is sent to the maintenance management platform; A3. If the maintenance risk corresponding to the target highway section is "high risk", the target highway section is marked as "high risk". At the same time, the maintenance implementation standard corresponding to "high risk" is extracted from the database. The maintenance implementation standard is: more than 3 maintenance personnel are assigned to be responsible for daily inspection and maintenance, and the number of maintenance equipment required is listed. At the same time, monitoring equipment and repair tools are added, and the frequency of maintenance work is set to conduct a comprehensive inspection and maintenance once a quarter, and the maintenance implementation standard corresponding to the target highway section is sent to the maintenance management platform.

3. The intelligent decision-making method for highway asphalt pavement maintenance according to claim 2, characterized in that: The semi-enclosed area of ​​the target highway section is analyzed, and the specific analysis process is as follows: D1. Use geographic information system software to input the coordinate information of the target road section, obtain the lane length and lane width corresponding to the target highway section, and extract the corresponding traffic volume during the semi-closed maintenance of the historical target highway section from the database; D2. Using traffic flow theory, the length and width of the lanes corresponding to the target expressway section are brought into the macro traffic flow model to simulate the changes in traffic flow, evaluate the level of traffic congestion that may occur during the semi-closed maintenance period, and compare the traffic congestion level corresponding to the target expressway section with the traffic congestion level corresponding to the area of ​​each semi-closed area in the database. If the traffic congestion level corresponding to the target expressway section is the same as the traffic congestion level corresponding to a semi-closed area in the database, the area of ​​the semi-closed area in the database will be used as the semi-closed area area of ​​the target expressway section.

4. The intelligent decision-making method for highway asphalt pavement maintenance as claimed in claim 3, characterized in that: The maintenance safety data corresponding to the maintenance group is collected at each collection time point, and the specific collection process is as follows: B1. Use drones equipped with high-definition cameras or multispectral sensors to take aerial photos of target highway sections, obtain high-resolution image data corresponding to the collection and maintenance groups at each collection time point, and use remote sensing image processing software to interpret the collected high-resolution image data to identify the types and quantities of various types of safety management equipment; B2. Obtain high-resolution image data from drones or other sources, record the specific location coordinates of the device, obtain the digital elevation model of the target area through high-resolution image data to provide terrain information, run the viewshed analysis tool, and calculate the viewshed range of the device according to the set parameters. After the analysis is completed, the GIS software will generate a viewshed map, superimpose the viewshed analysis results with the high-resolution image, and intuitively display the actual viewshed range of the device; B3. Equip maintenance personnel with GPS positioning devices or smart phones to record their location data in real time during work, set clear marks at the maintenance boundaries, integrate the GPS data of maintenance personnel with the maintenance boundary data in the GIS database, use GIS software for spatial analysis, calculate the distance between maintenance personnel and the maintenance boundary, obtain accurate distance data through the point-to-line distance calculation function, and use the activity trajectory of maintenance personnel to generate a heat map to obtain the corresponding distance between each maintenance personnel and the maintenance boundary.

5. The intelligent decision-making method for highway asphalt pavement maintenance as claimed in claim 4, characterized in that: The analysis obtains the maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point. The specific analysis process is as follows: The number of each type of safety management equipment in the maintenance group at each collection time point, the field of view angle, and the distance between each maintenance personnel and the maintenance boundary are recorded as and Among them, y represents the number corresponding to each collection time point, y = 1, 2...p, p is a positive integer, f represents the number corresponding to each type of safety management equipment, f = 1, 2...q, q is a positive integer, d represents the number corresponding to each maintenance personnel and maintenance boundary, d = 1, 2...n, n is a positive integer, substitute into the calculation formula The maintenance safety assessment coefficient Θ corresponding to the maintenance group at each collection time point is obtained y , where Z′, M′, and R′ are the standard number of safety management devices, standard field of view angle, and standard distance between maintenance personnel and maintenance boundaries, respectively; ζ1, ζ2, and ζ3 are the weight factors corresponding to the number of safety management devices, the weight factor corresponding to the field of view angle, and the weight factor corresponding to the distance between maintenance personnel and maintenance boundaries, respectively.

6. The intelligent decision-making method for highway asphalt pavement maintenance as claimed in claim 5, characterized in that: The specific judgment process of judging whether the maintenance safety of the maintenance group at each collection time point is qualified is as follows: The maintenance safety assessment coefficient corresponding to the maintenance group at each collection time point is compared with the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group. If the maintenance safety assessment coefficient corresponding to the maintenance group at a certain collection time point is within the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group, then the maintenance safety of the maintenance group at that collection time point is judged to be qualified. If the maintenance safety assessment coefficient corresponding to the maintenance group at a certain collection time point is not within the maintenance safety assessment coefficient interval corresponding to the set standard maintenance group, then the maintenance safety of the maintenance group at that collection time point is judged to be unqualified. In this way, whether the maintenance safety of the maintenance group at each collection time point is qualified is judged.

7. The intelligent decision-making method for highway asphalt pavement maintenance as claimed in claim 6, characterized in that: The specific analysis process of the analysis of the maintenance safety decision-making of the maintenance group at the collection time point is as follows: C1. If the maintenance safety of a maintenance group at a certain collection time point is unqualified, an early warning will be issued to the maintenance group, the early warning indicator of the maintenance group at that collection time point will emit a yellow alarm, and the maintenance group will be notified to conduct a maintenance safety inspection; C2. If the maintenance safety of a maintenance group at a certain collection time point is unqualified, and the total number of times the maintenance safety of the maintenance group fails to meet the standards during the maintenance of the target expressway section is counted, if the total number of times the maintenance group fails to meet the standards during the maintenance of the target expressway section is greater than the set number of times the maintenance safety fails to meet the standards during the maintenance of the standard expressway section, an early warning will be issued to the maintenance group, the early warning indicator of the maintenance group at the collection time point will be turned red, and the maintenance group will be notified to conduct a maintenance safety inspection. At the same time, the maintenance group will be recalled and provided with maintenance safety training.

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