Method, system and equipment for determining pre-maintenance opportunity of asphalt pavement

Abstract

The invention discloses a method, a system and equipment for determining the pre-maintenance opportunity of an asphalt pavement, and belongs to the technical field of pavement pre-maintenance opportunity analysis processing, and the method comprises the steps: analyzing a pavement state comprehensive evaluation value, obtaining a pavement state comprehensive fluctuation index value, carrying out the adaptive optimization of a sampling interval, and determining an optimal pre-maintenance time window; and performing pre-maintenance operation based on the optimal pre-maintenance time window, performing feedback adjustment on pre-maintenance operation parameters, obtaining a first dynamic correction factor, analyzing a second dynamic correction factor, and analyzing the optimized pre-maintenance reference index. According to the method, the scientificity, accuracy and efficiency of asphalt pavement maintenance can be improved from multiple aspects, resource waste is reduced, the long-term performance of the pavement is practically guaranteed, scientific and efficient development of road maintenance work is promoted, and the service life of the asphalt pavement is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of pavement pre-curing opportunity analysis and processing, and in particular to a method, system and equipment for determining asphalt pavement pre-curing opportunity. Background Art

[0002] In highway engineering, asphalt pavement is widely used due to its advantages such as excellent driving comfort, skid resistance, and durability. Preventive maintenance is a proactive maintenance strategy aimed at preemptively identifying and repairing potential or incipient pavement damage, thereby preventing serious road problems and extending the pavement's service life. This type of maintenance relies on regular pavement inspections and assessments to detect and address problems promptly. For example, small cracks in the pavement are sealed to prevent further damage to the pavement structure from moisture infiltration and freeze-thaw cycles.

[0003] For example, the invention patent announcement with announcement number: CN107798177B discloses a method for determining the optimal pavement maintenance timing based on pavement performance models before and after maintenance. The method determines two pavement performance decay models before and after maintenance based on historical performance test indicators of actual pavement maintenance projects, such as the roughness index IRI and rutting depth RD. Then, a connection is established between the two model parameters, and the pavement performance model before maintenance is used to predict the pavement performance model after maintenance. The maintenance benefit is defined as the envelope area between the performance curves before and after maintenance and the maintenance threshold, and is expressed as a function of the parameters of the performance model before maintenance. Finally, the value of the corresponding parameter when the maintenance benefit is maximized is determined to determine the optimal maintenance timing.

[0004] For example, the invention patent publication with announcement number CN118797978B discloses a method for estimating the remaining life of asphalt pavements and making decisions on pre-maintenance. The method includes: assessing the pavement condition starting from the opening of the expressway to traffic, before any defects occur on the asphalt pavement; using the PQI and its sub-indicators to determine if a road section requires preventive maintenance; when 96>PQI≥90, a preventive maintenance judgment is introduced, core sampling is performed on typical sections of the road section, and a series of semicircular bending tests are conducted to measure the dynamic modulus, fatigue life, and flexural tensile strength. Combined with traffic volume surveys, the remaining life of the asphalt pavement is estimated, maintenance timing is determined, and maintenance decisions are made.

[0005] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0006] Existing methods for determining the timing of pre-maintenance maintenance for asphalt pavements mostly focus on determining the timing for already-traveled sections. For untraveled sections, the timing relies solely on experience, lacking scientific evidence and accurate judgment. Furthermore, these methods are unable to dynamically adjust maintenance strategies based on actual conditions, making it difficult to achieve optimal pre-maintenance results. This can lead to resource waste and inability to fully guarantee the long-term performance of the pavement, hindering the efficient implementation of road maintenance. Summary of the Invention

[0007] A first aspect of the present invention provides a method for determining the timing of pre-curing an asphalt pavement, comprising the following steps:

[0008] S1, collecting road surface condition data of a target road section based on a preset sampling interval, and analyzing a comprehensive evaluation value of the road surface condition.

[0009] S2, obtain the environmental prediction data of the target road section, obtain the comprehensive fluctuation index value of the road surface state, and generate the dynamic adjustment coefficient of the sampling period.

[0010] S3, based on the dynamic adjustment coefficient of the sampling period, the sampling interval is adaptively optimized, and an evolution trajectory model of the time-pavement condition comprehensive evaluation value is constructed. The evolution trajectory model is trend-matched with the pre-maintenance benchmark indicators to determine the optimal pre-maintenance time window.

[0011] S4, performing pre-curing operations based on the optimal pre-curing time window, collecting the spraying uniformity coefficient in real time, and performing feedback adjustment on the pre-curing operation parameters, thereby obtaining a first dynamic correction factor.

[0012] S5, after the pre-maintenance is completed, the uniformity parameters are obtained, the second dynamic correction factor is analyzed, the pavement condition data is collected, and the optimized pre-maintenance benchmark index is analyzed by combining the first dynamic correction factor, the second dynamic correction factor and the number of historical errors.

[0013] A second aspect of the present invention provides a system for determining the timing of pre-curing asphalt pavement maintenance, comprising:

[0014] The pavement condition evaluation module is used to collect pavement condition data of the target road section based on a preset sampling interval and analyze the comprehensive evaluation value of the pavement condition.

[0015] The sampling period dynamic adjustment module is used to obtain the environmental prediction data of the target road section and the comprehensive fluctuation index value of the road surface state, thereby generating the sampling period dynamic adjustment coefficient.

[0016] The optimal pre-maintenance time window determination module is used to adaptively optimize the sampling interval based on the dynamic adjustment coefficient of the sampling period, construct an evolution trajectory model of the time-pavement condition comprehensive evaluation value, match the evolution trajectory model with the pre-maintenance benchmark indicators, and determine the optimal pre-maintenance time window.

[0017] The pre-curing operation parameter feedback adjustment module is used to perform pre-curing operations based on the optimal pre-curing time window, collect the spraying uniformity coefficient in real time, and thereby perform feedback adjustment on the pre-curing operation parameters to obtain the first dynamic correction factor.

[0018] The pre-maintenance benchmark index optimization module is used to obtain uniformity parameters after pre-maintenance is completed, analyze the second dynamic correction factor, collect pavement condition data, and analyze the optimized pre-maintenance benchmark index by combining the first dynamic correction factor, the second dynamic correction factor and the number of historical errors.

[0019] A third aspect of the present invention provides a device for determining the timing of asphalt pavement pre-curing, comprising: a processor, a memory for storing processor-executable instructions; when the processor is configured to execute instructions, the electronic device implements the above-mentioned method for determining the timing of asphalt pavement pre-curing.

[0020] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0021] 1. The method for determining the timing of asphalt pavement pre-maintenance provided by the present invention can improve the scientificity, accuracy, and efficiency of asphalt pavement maintenance in many aspects. In terms of timing determination, by collecting pavement status data and environmental prediction data to generate a dynamic adjustment coefficient for the sampling period, adaptively optimizing the sampling interval, building an evolution trajectory model to match the pre-maintenance benchmark indicators, and accurately locking the optimal pre-maintenance time window, it avoids the blindness of traditional reliance on experience and judgment. During the maintenance process, the spraying uniformity coefficient is collected in real time to feedback and adjust the operation parameters to obtain the first dynamic correction factor. While ensuring the quality of the operation and optimizing resource utilization, it also provides a data basis for accurately evaluating the pavement status. After the maintenance is completed, the uniformity parameter is obtained to analyze the second dynamic correction factor. The pre-maintenance benchmark indicators are optimized in combination with the historical error number. This can fully consider the actual maintenance effect and various influencing factors, dynamically adjust the maintenance strategy, reduce resource waste, effectively ensure the long-term performance of the pavement, promote the scientific and efficient implementation of road maintenance work, and extend the service life of the asphalt pavement.

[0022] 2. The present invention can adaptively optimize the sampling interval according to environmental changes and road surface condition fluctuations by generating a dynamic adjustment coefficient for the sampling period. When the environment has a significant impact on the road surface condition and the road surface condition fluctuations are complex, the coefficient will adjust the sampling interval accordingly to ensure that the collected data is more consistent with the actual changes in the road surface. This makes the subsequent evolution trajectory model of the time-road surface condition comprehensive evaluation value more accurate and more accurately matches the trend of the pre-maintenance benchmark indicators, thereby determining a more reasonable optimal pre-maintenance time window, effectively avoiding resource waste and pre-maintenance timing deviations caused by untimely or excessive sampling, ensuring that the asphalt pavement can be maintained at the most appropriate time, improving the maintenance effect, and extending the service life of the road surface.

[0023] 3. The present invention can improve the quality of pre-maintenance operations, optimize resource utilization, and ensure pavement maintenance effects by performing feedback adjustment on pre-maintenance operation parameters, ensuring that all parts of the pavement receive sufficient pre-maintenance materials. At the same time, by traversing the adjustment parameters and calculating the average adjustment amount to obtain the first dynamic correction factor, the pavement condition evaluation results can be calibrated more accurately, providing a data basis for the subsequent optimization of pre-maintenance benchmark indicators, thereby improving the scientific nature and effectiveness of the overall pre-maintenance operation.

[0024] 4. This invention improves the accuracy and effectiveness of asphalt pavement pre-maintenance operations by analyzing and optimizing pre-maintenance benchmark indicators. After pre-maintenance operations are completed, pavement condition data is recollected and combined with the first and second dynamic correction factors to generate a revised pavement condition evaluation value to optimize the pre-maintenance benchmark indicators. By incorporating various practical factors in pre-maintenance operations, such as external interference and the uniformity coefficient of the maintenance operation, the accuracy and rationality of the optimization of pre-maintenance benchmark indicators are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a method for determining the timing of asphalt pavement pre-curing provided in an embodiment of the present application;

[0026] Figure 2 A schematic diagram of the structure of a system for determining the timing of asphalt pavement pre-curing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] Reference Figure 1 As shown, the first aspect of the present invention provides a method for determining the timing of pre-curing of an asphalt pavement, comprising the following steps:

[0029] S1, collecting road surface condition data of a target road section based on a preset sampling interval, and analyzing a comprehensive evaluation value of the road surface condition.

[0030] In this embodiment, the road surface condition data of the target road section is collected based on the preset sampling interval, and the comprehensive evaluation value of the road surface condition is analyzed. The specific analysis method is as follows:

[0031] The specific steps for collecting the pavement condition data of the target road section are as follows: Use an infrared thermal imager to perform multi-point scanning along the longitudinal direction of the road surface to obtain the temperature distribution data of the surface layer and the preset depths. The temperature gradient is generated by calculating the temperature difference between adjacent measuring points. The specific process is: Use the infrared thermal imager to scan along the longitudinal direction of the target road section in a serpentine path, set up parallel detection bands in the transverse direction, collect data based on the preset spacing and longitudinal sampling interval in the database, and simultaneously collect the temperature distribution data of the road surface nominal and the preset depth. In this way, the surface temperature, the temperature corresponding to the preset depth point, and the depth difference are obtained. Therefore, the temperature gradient is expressed as follows: Where ΔT is the temperature gradient, T surface is the surface temperature, T depth is the temperature corresponding to the preset depth point, and d is the depth difference.

[0032] An ultrasonic pulse reflectometer is used to transmit high-frequency sound waves to the asphalt-aggregate interface. The bond strength is obtained based on the attenuation characteristics of the reflected wave signal. The specific process is as follows: Using the ultrasonic pulse reflectometer, the transmitting probe and the receiving probe are arranged at a preset angle at the asphalt-aggregate interface. The probe spacing is dynamically adjusted according to the preset rules in the database. The incident wave amplitude and the reflected wave amplitude are obtained, and the bond strength is calculated based on the attenuation characteristics of the reflected wave signal. Specifically, Among them, S b is the bonding strength, A incident is the incident wave amplitude, A reflected is the reflected wave amplitude, and k is the material property calibration coefficient stored in the database.

[0033] Electromagnetic waves are emitted by ground-penetrating radar, and a density inversion model is established based on the correlation between the time-domain amplitude of the echo signal and the dielectric constant to output the density. The specific process is: electromagnetic waves are emitted to the road surface by ground-penetrating radar, the time-domain amplitude and propagation delay of the echo signal are recorded, and the density is inverted based on the correlation model between the dielectric constant and the density. Specifically, Among them, ρ is the density, α and β are the calibration parameters preset in the database, A0 is the reference amplitude preset in the database, A(t) is the time domain amplitude, t peak is the propagation delay, ε r The dielectric constant preset in the database.

[0034] The temperature gradient, bond strength and density are combined as the pavement condition data of the target road section.

[0035] Extract the reference temperature gradient, reference bonding strength and reference density preset in the database.

[0036] The temperature gradient weights, bonding strength weights and density weights stored in the database are extracted, and their value ranges are all between 0 and 1. When used, the pre-set values can be directly extracted from the database. The specific extraction method is, for example: a mapping set is constructed one by one for the temperature gradient, bonding strength and density and the corresponding weights. When used, the temperature gradient, bonding strength and density obtained in real time are input one by one into the corresponding mapping set, thereby extracting the corresponding temperature gradient weights, bonding strength weights and density weights.

[0037] The comprehensive evaluation value of the road surface condition is obtained based on the analysis and processing of the road surface condition data of the target road section.

[0038] It should be noted that the comprehensive pavement condition evaluation derived from analyzing and processing pavement condition data for a target road section takes into account the interplay between these parameters. For example, temperature gradients can affect bond strength. Large temperature gradients cause pavement materials to expand and contract, generating stress at the material interface and weakening the bond. For example, high temperature gradients can loosen the asphalt-aggregate interface, reducing bond strength. Temperature gradients also affect density. Temperature changes cause moisture and gas migration within the material, leading to expansion and contraction, and altering the compact structure. For example, rising temperatures cause gas expansion within the material, loosening the structure and reducing density. There is also a correlation between bond strength and density. Good bond strength helps maintain a tight bond between materials, ensuring high density and making the pavement structure more stable. Highly compacted pavements, on the other hand, have closer contact between materials, increasing the bond area, thereby increasing bond strength and enhancing the overall pavement performance.

[0039] The comprehensive evaluation value of pavement condition is a quantitative data that represents the overall pavement condition assessment of the target road section based on temperature gradient, bond strength, and density. The specific analysis process is: differential analysis of the pavement condition data and the corresponding reference pavement condition data is performed, and then the comprehensive evaluation value of the pavement condition is obtained by combining the coupling influence of the corresponding weights.

[0040] In a specific embodiment, the comprehensive evaluation value of the road surface condition is specifically expressed as follows:

[0041]

[0042] Among them, PCV is the comprehensive evaluation value of road surface condition, ΔT is the temperature gradient of the target road section, S b is the bonding strength of the target road section, ρ is the density of the target road section, ΔT vef is the reference temperature gradient, is the reference bond strength, ρ vef is the reference density, x1 is the temperature gradient weight, x2 is the bond strength weight, and x3 is the density weight.

[0043] S2, obtain the environmental prediction data of the target road section, obtain the comprehensive fluctuation index value of the road surface state, and generate the dynamic adjustment coefficient of the sampling period.

[0044] In this embodiment, the dynamic adjustment coefficient of the sampling period is generated accordingly, and the specific analysis process is as follows:

[0045] The environmental prediction data of the target road section includes the predicted temperature change rate, predicted cumulative precipitation and predicted cumulative ultraviolet radiation intensity of the target road section during the expected collection period.

[0046] It should be noted that the expected collection cycle refers to the next collection cycle of the target road section, and the environmental prediction data refers to the weather station prediction data.

[0047] The environmental prediction index value of the target road section is obtained by analyzing and processing the environmental prediction data of the target road section.

[0048] The environmental prediction index value of the target road section is a quantitative result of the degree of influence of the predicted temperature change rate, predicted cumulative precipitation and predicted cumulative ultraviolet radiation intensity on the environmental state of the target road section. It is specifically expressed by coupling the influence of the corresponding weights based on the differentiation results of the predicted temperature change rate, predicted cumulative precipitation and predicted cumulative ultraviolet radiation intensity with the corresponding reference predicted temperature change rate, reference predicted cumulative precipitation and reference predicted cumulative ultraviolet radiation intensity, so as to analyze and obtain the environmental prediction index value of the target road section.

[0049] It should be noted that the environmental prediction index values for target road sections, derived through analysis and processing of environmental prediction data for the target road section, take into account the interplay between these parameters. For example, the temperature change rate affects the evaporation and condensation processes of water. A higher temperature change rate may accelerate evaporation. If the accumulated precipitation is low at this time, the road surface may dry out due to lack of water, thus affecting its structural stability. Furthermore, when the predicted accumulated UV radiation intensity is high, dry roads are more susceptible to UV erosion, accelerating road aging. Conversely, high accumulated precipitation can cause the road surface to remain wet for extended periods. When the temperature change rate is high, this can lead to structural damage from alternating dry and wet conditions. Furthermore, a wet road surface exposed to UV radiation may alter its reflection and absorption properties, affecting the road surface's temperature distribution and further influencing the temperature change rate.

[0050] The reference environmental prediction data stored in the database are extracted, including the reference predicted temperature change rate, the reference predicted cumulative precipitation, and the reference predicted cumulative ultraviolet radiation intensity.

[0051] Extract the predicted temperature change rate weight, predicted cumulative precipitation weight and predicted cumulative ultraviolet radiation intensity weight preset in the database.

[0052] In a specific embodiment, the environmental prediction index value of the target road section is specifically expressed as follows:

[0053]

[0054] Among them, ENP is the environmental prediction index value of the target section, TCR is the predicted temperature change rate of the target section during the expected collection period, PCP is the predicted cumulative precipitation of the target section during the expected collection period, PCU is the predicted cumulative ultraviolet radiation intensity of the target section during the expected collection period, TCR vef For reference, the predicted temperature change rate, PCP vef For reference to the predicted cumulative precipitation, PCU vef For reference, the predicted cumulative ultraviolet radiation intensity is used, y1 is the weight of the predicted temperature change rate, y2 is the weight of the predicted cumulative precipitation, and y3 is the weight of the predicted cumulative ultraviolet radiation intensity.

[0055] It should be noted that the value ranges of the predicted temperature change rate weight, the predicted cumulative precipitation weight, and the predicted ultraviolet cumulative radiation intensity weight are all between 0 and 1. When used, the pre-set values can be directly extracted from the database. The specific extraction method is, for example: a one-to-one mapping set is constructed for the predicted temperature change rate, the predicted cumulative precipitation, and the predicted ultraviolet cumulative radiation intensity with the corresponding predicted temperature change rate weight, the predicted cumulative precipitation weight, and the predicted ultraviolet cumulative radiation intensity weight respectively. When used, the real-time predicted temperature change rate, predicted cumulative precipitation, and predicted ultraviolet cumulative radiation intensity are respectively input into the corresponding mapping set to obtain the predicted temperature change rate weight, the predicted cumulative precipitation weight, and the predicted ultraviolet cumulative radiation intensity weight.

[0056] The environmental correction coefficient corresponding to each environmental prediction index value interval stored in the database is extracted, and the environmental correction coefficient corresponding to the environmental prediction index value interval of the target road section is mapped and extracted, and recorded as the environmental correction coefficient of the target road section.

[0057] It should be understood that the larger the environmental prediction index value, the greater the degree of impact of the environment on the road surface condition. In order to more accurately adjust the sampling period so that it can better fit the actual changes in road conditions, the corresponding extracted environmental correction coefficient will be larger.

[0058] The number of detected cycles preset in the database is extracted, and the corresponding comprehensive evaluation value of the road surface condition is extracted based on the number of detected cycles, which is recorded as the comprehensive evaluation value of the road surface condition of each detected cycle.

[0059] Extract the comprehensive evaluation threshold of road condition preset in the database.

[0060] The road surface condition comprehensive evaluation value of each detected cycle is subtracted from the absolute value of the road surface condition comprehensive evaluation threshold, and then the average is processed to obtain the road surface condition comprehensive fluctuation index value.

[0061] The comprehensive fluctuation index value of the road surface condition is multiplied by the environmental correction coefficient of the target section to obtain the fluctuation factor of the target section.

[0062] Extract the verification fluctuation factor preset in the database, subtract the verification fluctuation factor from the fluctuation factor of the target section, and obtain the fluctuation deviation factor of the target section.

[0063] The periodic adjustment coefficient corresponding to each fluctuation deviation factor interval stored in the database is extracted, and the periodic adjustment coefficient corresponding to the interval where the fluctuation deviation factor of the target road section is located is mapped and extracted, and recorded as the sampling period dynamic adjustment coefficient.

[0064] It's important to understand that the larger the absolute value of the target road section's fluctuation deviation factor, the greater the difference between the target road section's fluctuation factor and the preset verification fluctuation factor. This indicates that the road surface condition fluctuations are more complex due to environmental influences and the degree of data fluctuation is higher. To more effectively adapt to these complex and unexpected road surface condition fluctuations, the sampling interval is adjusted more precisely so that the collected data better reflects actual road surface changes. The larger the absolute value of the extracted period adjustment coefficient, the more significant the adjustment of the sampling period, ensuring more reasonable and accurate determination of subsequent road surface condition monitoring and pre-maintenance timing.

[0065] It is also necessary to understand that if the fluctuation deviation factor of the target section is greater than zero, it means that the fluctuation factor of the target section is greater than the verification fluctuation factor preset in the database, that is, the actual fluctuation of the road surface exceeds expectations. In order to capture the changes in road surface conditions more timely, the corresponding extracted period adjustment coefficient is a positive value, and the sampling period will be increased at this time; if the fluctuation deviation factor of the target section is less than zero, it means that the fluctuation factor of the target section is less than the verification fluctuation factor, and the actual fluctuation of the road surface is less than expected. In order to avoid waste of resources due to too frequent sampling, the corresponding extracted period adjustment coefficient is a negative value, that is, the sampling period will be reduced.

[0066] S3, based on the dynamic adjustment coefficient of the sampling period, the sampling interval is adaptively optimized, and an evolution trajectory model of the time-pavement condition comprehensive evaluation value is constructed. The evolution trajectory model is trend-matched with the pre-maintenance benchmark indicators to determine the optimal pre-maintenance time window.

[0067] In this embodiment, the optimal pre-curing time window is determined, and the specific process is as follows:

[0068] The sampling interval is adaptively optimized based on the dynamic adjustment coefficient of the sampling period, thereby obtaining the comprehensive evaluation value of the road surface condition within each optimized sampling interval.

[0069] In a specific embodiment, the sampling interval is adaptively optimized based on the dynamic adjustment coefficient of the sampling period, specifically: the current sampling period is obtained, the current sampling period is multiplied by the dynamic adjustment coefficient of the sampling period to obtain the adjusted sampling period, sampling is performed based on the adjusted sampling period, and the entire sampling process is traversed in sequence, thereby adaptively optimizing the sampling interval.

[0070] Based on the comprehensive evaluation value of the road surface condition within each optimized sampling interval and each optimized sampling interval, an evolution trajectory model of the time-road surface condition comprehensive evaluation value is constructed.

[0071] In a specific embodiment, the sampling interval is optimized to represent time, which is used as the horizontal coordinate, and the comprehensive evaluation value of the pavement condition is used as the vertical coordinate, thereby establishing a rectangular coordinate system. Each set of data is marked as a scattered point in the coordinate system. These scattered points reflect the comprehensive evaluation of the pavement condition at different time points. According to the distribution characteristics of the scattered points, a suitable fitting algorithm is selected, such as linear fitting, polynomial fitting, etc., to fit a curve. This curve is the evolution trajectory model of the time-pavement condition comprehensive evaluation value, which shows the changing trend of the pavement condition over time and provides a data basis for determining the optimal pre-maintenance time window.

[0072] Extract the pre-maintenance benchmark indicators preset in the database.

[0073] The evolution trajectory model is trend matched with the pre-maintenance benchmark indicators to determine the optimal pre-maintenance time window.

[0074] In a specific embodiment, the evolution trajectory model is trend-matched with pre-maintenance benchmark indicators to determine the optimal pre-maintenance time window. The specific process is as follows: the constructed evolution trajectory model of the time-pavement condition comprehensive evaluation value is compared and analyzed with the pre-maintenance benchmark indicators extracted from the database. By observing the direction, rate of change, and other characteristics of the evolution trajectory, the degree of fit with the ideal state or standard trend represented by the pre-maintenance benchmark indicators is evaluated. The time period in the evolution trajectory is identified when the pavement condition comprehensive evaluation value reaches or approaches the pre-maintenance benchmark indicator requirements, and the subsequent trend indicates that the pavement condition will deteriorate rapidly if pre-maintenance is not performed. This time period is the optimal pre-maintenance time window. Implementing pre-maintenance operations within this window can effectively maintain the performance of the asphalt pavement and extend the pavement service life. In this embodiment, a dynamic time warping algorithm can be used to determine the optimal pre-maintenance time window.

[0075] S4, performing pre-curing operations based on the optimal pre-curing time window, collecting the spraying uniformity coefficient in real time, and performing feedback adjustment on the pre-curing operation parameters, thereby obtaining a first dynamic correction factor.

[0076] In this embodiment, feedback adjustment is performed on the pre-curing operation parameters. The specific analysis process is as follows:

[0077] During the pre-maintenance operation, the spraying uniformity coefficient is collected at a preset collection period, thereby obtaining each spraying uniformity coefficient.

[0078] It should be noted that the spraying uniformity coefficient can be obtained by obtaining the temperature distribution of the spraying area through infrared scanning, and the inverse of the temperature difference is used as the numerical result of the spraying uniformity coefficient.

[0079] Extract the preset spray uniformity coefficient verification value in the database.

[0080] The deviation value of each spraying uniformity coefficient is obtained by performing difference processing on each spraying uniformity coefficient and the spraying uniformity coefficient verification value.

[0081] Difference processing refers to subtracting the spraying uniformity coefficient verification value from each spraying uniformity coefficient. The result of the difference processing can be greater than zero, less than zero, or equal to zero.

[0082] The adjustment parameters corresponding to each spraying uniformity coefficient deviation value interval stored in the database are extracted, and the adjustment parameters corresponding to the interval in which the spraying uniformity coefficient deviation value is located are mapped and recorded as the pre-maintenance operation adjustment parameters.

[0083] The pre-maintenance operation adjustment parameters include the spraying volume adjustment value and the spraying speed adjustment value of the sprayer.

[0084] It is important to understand that when the spray uniformity coefficient deviation value is less than the preset deviation interval lower limit and the absolute value is larger, the actual spray uniformity coefficient is low and the difference from the ideal state is increasing, which means that the current spraying operation is insufficient or there is too little spraying in some areas. In order to return the spray uniformity coefficient to the ideal state, the corresponding extracted sprayer spray volume should be a positive value. Because the current spraying is insufficient, increasing the spray volume can supplement the pre-curing material, so that all parts of the road surface are adequately covered by the material, and improve the spray uniformity coefficient. If the spray speed is negative, slowing down the spraying speed can allow more pre-curing material to be received per unit area, avoiding the problem of insufficient local spraying caused by excessive speed, resulting in an increased spray volume that still cannot improve the problem, thereby improving the spray uniformity coefficient.

[0085] If the deviation value of the spraying uniformity coefficient is greater than the upper limit of the preset deviation interval, and the larger the deviation value, the higher the actual spraying uniformity coefficient, in this case, in order to avoid wasting resources and maintain a suitable spraying effect, the corresponding extracted spraying volume of the sprayer should be a negative value. Because the actual spraying uniformity coefficient is already high, if the current spraying volume is maintained or increased, it may cause waste of pre-curing materials. Therefore, the spraying volume should be reduced, that is, the spraying volume is negative, so as to optimize resource utilization, and the spraying speed should be positive, that is, the spraying speed should be appropriately increased. Since the actual spraying uniformity coefficient is good, speeding up the spraying speed can improve the efficiency of the pre-curing operation while ensuring uniform spraying. While reducing the spraying volume, the faster speed can be used to make the spraying area wider, ensuring that all areas of the road surface are properly covered with pre-curing materials, and avoiding excessive local spraying due to slow spraying speed.

[0086] It should be noted that, in this embodiment, the pre-maintenance equipment includes but is not limited to a road roller, an intelligent asphalt spreader, etc.

[0087] Extract current pre-maintenance operation parameters.

[0088] Based on the current pre-maintenance operation parameters and the pre-maintenance operation adjustment parameters, the pre-maintenance operation parameters are feedback-adjusted.

[0089] In a specific embodiment, assume that during a pre-maintenance operation, the sprayer is operating at a fixed spraying volume and speed. If the spraying uniformity coefficient deviation is found to be less than a preset deviation range, the corresponding pre-maintenance operation adjustment parameters are extracted. For example, if analysis indicates that the spraying volume needs to be increased and the spraying speed needs to be reduced for more uniform spraying, the sprayer's spraying volume and spraying speed adjustment values are extracted. Based on these values, the current spraying volume is increased and the spraying speed is reduced accordingly.

[0090] In this embodiment, the first dynamic correction factor is obtained, and the specific analysis process is as follows:

[0091] The entire pre-maintenance operation cycle is traversed in sequence, and the pre-maintenance operation adjustment parameters corresponding to each cycle are extracted and counted, and recorded as each pre-maintenance operation adjustment parameter.

[0092] The absolute adjustment amounts of each pre-curing operation corresponding to each pre-curing operation adjustment parameter are averaged to obtain the average adjustment amount of the pre-curing operation.

[0093] The correction factor corresponding to each pre-maintenance operation average adjustment amount interval stored in the database is extracted, and the correction factor corresponding to the interval in which the pre-maintenance operation average adjustment amount is located is mapped and extracted, and recorded as the first dynamic correction factor.

[0094] It should be understood that the larger the average adjustment amount, the greater the adjustment range of the pre-maintenance operation parameters during the pre-maintenance operation. This shows that the actual operation process is strongly interfered by many external factors, making it difficult to maintain the operation in an ideal state stably. The role of the first dynamic correction factor is to correct the measured pavement condition deviation index to calibrate the pavement condition evaluation results. When the average adjustment amount is large, it means that the fluctuations in the operation process are mainly caused by external interference, and the pavement condition deviation is more likely to be caused by temporary external interference, and the possibility of incorrect determination of the pre-maintenance timing is small. Therefore, the corresponding extracted first dynamic correction factor is also smaller at this time, so as to reasonably correct the deviation caused by external interference, ensure a more accurate assessment of the pavement condition, and provide a numerical basis for the subsequent optimization of the pre-maintenance benchmark indicators.

[0095] S5, after the pre-maintenance is completed, the uniformity parameters are obtained, the second dynamic correction factor is analyzed, the pavement condition data is collected, and the optimized pre-maintenance benchmark index is analyzed by combining the first dynamic correction factor, the second dynamic correction factor and the number of historical errors.

[0096] In this embodiment, the second dynamic correction factor is analyzed, and the specific analysis process is as follows:

[0097] After the pre-curing operation is completed, the uniformity parameters are obtained, which include the average spraying uniformity coefficient, the penetration uniformity coefficient, the curing uniformity coefficient and the rolling path uniformity coefficient.

[0098] It should be noted that the intensity change value of the ultrasonic signal is collected by the ultrasonic sensor, the standard deviation of the intensity change of the ultrasonic signal is obtained, and the numerical result of the inverse of the standard deviation of the intensity change of the ultrasonic signal is used as the permeability uniformity coefficient.

[0099] The hardness of the road surface after curing is measured by hardness testing equipment at the preset collection points, thereby obtaining the hardness standard deviation, and the numerical result of the inverse of the hardness standard deviation is used as the quantitative result of the curing uniformity coefficient.

[0100] The rolling path data is collected by the path tracking sensor to obtain the overlapping rate of the rolling path, which is used as the numerical result of the rolling path uniformity coefficient.

[0101] The reference uniformity parameters stored in the database are extracted, including the reference average spray uniformity coefficient, the reference penetration uniformity coefficient, the reference curing uniformity coefficient, and the reference rolling path uniformity coefficient.

[0102] The average spraying uniformity coefficient weight, penetration uniformity coefficient weight, solidification uniformity coefficient weight and rolling path uniformity coefficient weight preset in the database are extracted.

[0103] It should be understood that the average spraying uniformity coefficient weight, the penetration uniformity coefficient weight, the solidification uniformity coefficient weight and the rolling path uniformity coefficient weight all have value ranges between 0 and 1. When used, the pre-set values can be directly extracted from the database. The specific extraction method is, for example: the average spraying uniformity coefficient, the penetration uniformity coefficient, the solidification uniformity coefficient and the rolling path uniformity coefficient are respectively constructed into a mapping set with the corresponding average spraying uniformity coefficient weight, the penetration uniformity coefficient weight, the solidification uniformity coefficient weight and the rolling path uniformity coefficient weight. When used, the average spraying uniformity coefficient, the penetration uniformity coefficient, the solidification uniformity coefficient and the rolling path uniformity coefficient obtained in real time are respectively input into the corresponding mapping set, so as to extract the average spraying uniformity coefficient weight, the penetration uniformity coefficient weight, the solidification uniformity coefficient weight and the rolling path uniformity coefficient weight.

[0104] The uniformity coefficient index of the pre-curing operation is obtained based on the uniformity parameter analysis.

[0105] The uniformity coefficient index of the pre-curing operation is used to characterize the uniformity of material spraying, penetration, solidification and rolling path during the pre-curing operation. Specifically, it is expressed by comparing the average spraying uniformity coefficient, penetration uniformity coefficient, solidification uniformity coefficient and rolling path uniformity coefficient with the corresponding reference values, coupling the influence of the corresponding weights, and thus analyzing the uniformity coefficient index of the pre-curing operation.

[0106] In a specific embodiment, the uniformity coefficient index of the pre-curing operation is specifically expressed as follows:

[0107]

[0108] Among them, Uni is the uniformity coefficient index of pre-curing operation, is the average spraying uniformity coefficient, U p is the permeability uniformity coefficient, U c is the curing uniformity coefficient, U r is the rolling path uniformity coefficient, is the reference average spray uniformity coefficient, is the reference permeability uniformity coefficient, is the reference curing uniformity coefficient, is the reference rolling path uniformity coefficient, z1 is the average spraying uniformity coefficient weight, z2 is the penetration uniformity coefficient weight, z3 is the solidification uniformity coefficient weight, and z4 is the rolling path uniformity coefficient weight.

[0109] It should be noted that the average spraying uniformity coefficient, penetration uniformity coefficient, curing uniformity coefficient, and rolling path uniformity coefficient are interrelated. A good average spraying uniformity coefficient can ensure a more even distribution of pre-curing materials, which helps improve the penetration uniformity coefficient and ensure consistent material penetration. Uniform spraying and penetration help improve the curing uniformity coefficient, making the pavement curing state more stable. The rolling path uniformity coefficient affects the overall compaction effect of the pavement. A uniform rolling path ensures that the uniformity of spraying, penetration, and curing is maintained, which together guarantee the quality of pre-curing operations and pavement performance.

[0110] The correction factors corresponding to the uniformity coefficient index intervals stored in the database are extracted, and the correction factors corresponding to the uniformity coefficient index of the pre-maintenance operation are mapped and extracted, which are recorded as the second dynamic correction factors.

[0111] It should be understood that the larger the uniformity coefficient index, the less affected the spraying, penetration, curing, rolling and other links of the material in the maintenance process are by factors such as temperature difference, bonding strength and density. The maintenance process is relatively stable and closer to the ideal state. When the uniformity coefficient index is large, if the comprehensive value of the measured pavement condition is also large, then the large comprehensive value of the measured pavement condition is probably not caused by fluctuations in these internal factors during the maintenance process, but rather by a high possibility of improper selection of the pre-maintenance timing. In order to more accurately correct the deviation caused by the possible incorrect pre-maintenance timing, the corresponding extracted second dynamic correction factor needs to be larger. Through greater correction strength, the comprehensive value of the measured pavement condition can be adjusted more significantly, so that the final pavement condition assessment result is more in line with reality.

[0112] In this embodiment, the optimized pre-curing benchmark indicators are analyzed, and the specific analysis steps are as follows:

[0113] After the pre-maintenance work is completed, the pavement condition data is collected again and analyzed to obtain a comprehensive evaluation value of the pavement condition.

[0114] The comprehensive evaluation value of the road surface condition, the first dynamic correction factor and the second dynamic correction factor are coupled to obtain the road surface condition evaluation correction value. In a specific embodiment, the comprehensive evaluation value of the road surface condition, the first dynamic correction factor and the second dynamic correction factor can be multiplied to obtain the road surface condition evaluation correction value.

[0115] The pavement condition evaluation correction value is subtracted from the pre-maintenance benchmark index to obtain the pavement condition measured deviation index.

[0116] Extract the historical error count stored in the database.

[0117] The allowable range of the measured deviation index is extracted based on the number of historical errors, including the allowable upper limit value of the measured deviation index and the allowable lower limit value of the measured deviation index.

[0118] It should be added that the specific method for extracting the allowable interval of the measured deviation index is: extract the allowable interval of the deviation index corresponding to each historical error number interval stored in the database, and map the area of the allowable interval of the deviation index corresponding to the interval in which the historical error number is extracted to the allowable interval of the measured deviation index.

[0119] If the measured deviation index of the pavement condition is within the allowable range of the measured deviation index, the preset pre-maintenance benchmark index will be used as the optimized pre-maintenance benchmark index.

[0120] If the measured deviation index of the pavement condition exceeds the allowable range of the measured deviation index, the degree of excess of the measured deviation index of the pavement condition is obtained and recorded as the deviation value of the measured deviation index of the pavement condition. The specific acquisition method is as follows: If the measured deviation index of the pavement condition is greater than the allowable upper limit value of the measured deviation index, the numerical result of subtracting the allowable upper limit value of the measured deviation index from the measured deviation index of the pavement condition is recorded as the deviation value of the measured deviation index of the pavement condition; if the measured deviation index of the pavement condition is less than the allowable lower limit value of the measured deviation index, the numerical result of subtracting the allowable lower limit value of the measured deviation index from the measured deviation index of the pavement condition is recorded as the deviation value of the measured deviation index of the pavement condition.

[0121] Extract the pre-maintenance benchmark index correction value corresponding to the deviation value of the actual road condition deviation index stored in the database.

[0122] It should be understood that the greater the deviation value of the measured pavement condition deviation index, the more significant the gap between the actual pavement condition and the pre-set ideal pavement condition after correcting the interference of various factors, and the greater the corresponding correction value of the extracted pre-maintenance benchmark index. In this way, the pre-maintenance operation plan can be optimized to achieve better pre-maintenance results.

[0123] The optimized pre-maintenance benchmark index is obtained based on the analysis of the pre-maintenance benchmark index and the pre-maintenance benchmark index correction value.

[0124] In a specific embodiment, the optimized pre-curing benchmark index is expressed as the pre-curing benchmark index multiplied by the pre-curing benchmark index correction value.

[0125] See Figure 2 As shown, the second aspect of the present invention provides a system for determining the timing of pre-curing of asphalt pavement, comprising:

[0126] The pavement condition evaluation module is used to collect pavement condition data of the target road section based on a preset sampling interval and analyze the comprehensive evaluation value of the pavement condition.

[0127] The sampling period dynamic adjustment module is used to obtain the environmental prediction data of the target road section and the comprehensive fluctuation index value of the road surface state, thereby generating the sampling period dynamic adjustment coefficient.

[0128] The optimal pre-maintenance time window determination module is used to adaptively optimize the sampling interval based on the dynamic adjustment coefficient of the sampling period, construct an evolution trajectory model of the time-pavement condition comprehensive evaluation value, match the evolution trajectory model with the pre-maintenance benchmark indicators, and determine the optimal pre-maintenance time window.

[0129] The pre-curing operation parameter feedback adjustment module is used to perform pre-curing operations based on the optimal pre-curing time window, collect the spraying uniformity coefficient in real time, and thereby perform feedback adjustment on the pre-curing operation parameters to obtain the first dynamic correction factor.

[0130] The pre-maintenance benchmark index optimization module is used to obtain uniformity parameters after pre-maintenance is completed, analyze the second dynamic correction factor, collect pavement condition data, and analyze the optimized pre-maintenance benchmark index by combining the first dynamic correction factor, the second dynamic correction factor and the number of historical errors.

[0131] A third aspect of the present invention provides a device for determining the timing of asphalt pavement pre-curing, comprising: a processor, a memory for storing processor-executable instructions; when the processor is configured to execute instructions, the electronic device implements the above-mentioned method for determining the timing of asphalt pavement pre-curing.

[0132] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0133] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0134] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0135] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0136] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0137] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for determining the timing of pre-curing asphalt pavement, characterized in that: The following steps are involved: S1, collecting road surface condition data of the target road section based on a preset sampling interval and analyzing the comprehensive evaluation value of the road surface condition; S2, obtaining environmental prediction data of the target road section and the comprehensive fluctuation index value of the road surface state, thereby generating a dynamic adjustment coefficient of the sampling period; S3: Adaptively optimize the sampling interval based on the dynamic adjustment coefficient of the sampling period, construct an evolution trajectory model of the time-pavement condition comprehensive evaluation value, match the evolution trajectory model with the pre-maintenance benchmark index, and determine the optimal pre-maintenance time window; S4, performing pre-curing operations based on the optimal pre-curing time window, collecting the spraying uniformity coefficient in real time, and performing feedback adjustment on the pre-curing operation parameters to obtain a first dynamic correction factor; S5, after the pre-maintenance is completed, the uniformity parameters are obtained, the second dynamic correction factor is analyzed, the pavement condition data is collected, and the optimized pre-maintenance benchmark index is analyzed by combining the first dynamic correction factor, the second dynamic correction factor and the number of historical errors.

2. The method for determining the timing of asphalt pavement pre-curing according to claim 1, characterized in that: The road surface condition data of the target road section is collected based on the preset sampling interval, and the comprehensive evaluation value of the road surface condition is analyzed. The specific analysis method is as follows: The infrared thermal imager is used to scan multiple points along the longitudinal direction of the road surface to obtain the temperature distribution data of the surface layer and the preset depths, and the temperature gradient is generated by calculating the temperature difference between adjacent measuring points; Using an ultrasonic pulse reflectometer, high-frequency sound waves are emitted to the asphalt-aggregate interface, and the bond strength is obtained based on the attenuation characteristics of the reflected wave signal. Electromagnetic waves are emitted by ground penetrating radar, and a density inversion model is established based on the correlation between the time domain amplitude of the echo signal and the dielectric constant to output the density; The temperature gradient, bonding strength and density are combined as the pavement condition data of the target road section; Obtaining a comprehensive evaluation value of the road surface condition based on the analysis and processing of the road surface condition data of the target road section; The comprehensive evaluation value of pavement condition is quantitative data representing the overall pavement condition evaluation of the target section based on temperature gradient, bonding strength and density. The specific analysis process is: differential analysis of pavement condition data and corresponding reference pavement condition data is performed, and then the comprehensive evaluation value of pavement condition is obtained by combining the coupling influence of corresponding weights.

3. The method for determining the timing of asphalt pavement pre-curing according to claim 1, characterized in that: The dynamic adjustment coefficient of the sampling period is generated from this, and the specific analysis process is as follows: The environmental prediction data of the target road section includes the predicted temperature change rate, predicted cumulative precipitation and predicted cumulative ultraviolet radiation intensity of the target road section during the expected collection period; Obtaining an environmental prediction index value for the target road section based on analysis and processing of the environmental prediction data for the target road section; The environmental prediction index value of the target road section is a quantitative result of the degree of influence of the predicted temperature change rate, the predicted cumulative precipitation, and the predicted cumulative ultraviolet radiation intensity on the environmental state of the target road section, specifically expressed as coupling the influence of the corresponding weights based on the difference results of the predicted temperature change rate, the predicted cumulative precipitation, and the predicted cumulative ultraviolet radiation intensity with the corresponding reference predicted temperature change rate, the reference predicted cumulative precipitation, and the reference predicted cumulative ultraviolet radiation intensity, thereby analyzing and obtaining the environmental prediction index value of the target road section; Extracting the environmental correction coefficient corresponding to each environmental prediction index value interval stored in the database, and mapping and extracting the environmental correction coefficient corresponding to the environmental prediction index value interval of the target road section, and recording it as the environmental correction coefficient of the target road section; Extracting the number of detected cycles preset in the database, extracting the corresponding comprehensive evaluation value of the road surface condition based on the number of detected cycles, and recording it as the comprehensive evaluation value of the road surface condition for each detected cycle; Extracting the comprehensive evaluation threshold of road condition preset in the database; The road condition comprehensive evaluation value of each detected cycle is subtracted from the absolute value of the road condition comprehensive evaluation threshold, and then the average is processed to obtain the road condition comprehensive fluctuation index value; The comprehensive fluctuation index value of the road surface condition is multiplied by the environmental correction coefficient of the target road section to obtain the fluctuation factor of the target road section; Extract the verification fluctuation factor preset in the database, subtract the verification fluctuation factor from the fluctuation factor of the target section to obtain the fluctuation deviation factor of the target section; The periodic adjustment coefficient corresponding to each fluctuation deviation factor interval stored in the database is extracted, and the periodic adjustment coefficient corresponding to the interval where the fluctuation deviation factor of the target road section is located is mapped and extracted, and recorded as the sampling period dynamic adjustment coefficient.

4. The method for determining the timing of asphalt pavement pre-curing according to claim 1, wherein: The specific process of determining the optimal pre-curing time window is as follows: Adaptively optimize the sampling interval based on the dynamic adjustment coefficient of the sampling period, thereby obtaining a comprehensive evaluation value of the road surface condition within each optimized sampling interval; Based on the comprehensive evaluation value of the road surface condition within each optimized sampling interval and each optimized sampling interval, an evolution trajectory model of the time-road surface condition comprehensive evaluation value is constructed; Extract the pre-maintenance benchmark indicators preset in the database; The evolution trajectory model is trend matched with the pre-maintenance benchmark indicators to determine the optimal pre-maintenance time window.

5. The method for determining the timing of asphalt pavement pre-curing according to claim 1, wherein: The feedback adjustment of the pre-curing operation parameters is carried out, and the specific analysis process is as follows: During the pre-maintenance operation, the spraying uniformity coefficient is collected at a preset collection period, thereby obtaining each spraying uniformity coefficient; Extract the spraying uniformity coefficient verification value preset in the database; Perform difference processing on each spraying uniformity coefficient and the spraying uniformity coefficient verification value to obtain the deviation value of each spraying uniformity coefficient; Extract the adjustment parameters corresponding to each spraying uniformity coefficient deviation value interval stored in the database, and map the adjustment parameters corresponding to the interval in which the spraying uniformity coefficient deviation value is extracted, and record them as pre-maintenance operation adjustment parameters; Extract current pre-maintenance operation parameters; Based on the current pre-maintenance operation parameters and the pre-maintenance operation adjustment parameters, the pre-maintenance operation parameters are feedback-adjusted.

6. The method for determining the timing of asphalt pavement pre-curing according to claim 1, wherein: The first dynamic correction factor is obtained in this way, and the specific analysis process is as follows: Traverse the entire pre-maintenance operation cycle in sequence, extract and count the pre-maintenance operation adjustment parameters corresponding to each cycle, and record them as each pre-maintenance operation adjustment parameter; The absolute adjustment amount of each pre-curing operation corresponding to each pre-curing operation adjustment parameter is averaged to obtain the average adjustment amount of the pre-curing operation; The correction factor corresponding to each pre-maintenance operation average adjustment amount interval stored in the database is extracted, and the correction factor corresponding to the interval in which the pre-maintenance operation average adjustment amount is located is mapped and extracted, and recorded as the first dynamic correction factor.

7. The method for determining the timing of asphalt pavement pre-curing according to claim 1, wherein: The specific analysis process of analyzing the second dynamic correction factor is as follows: After the pre-curing operation is completed, uniformity parameters are obtained, including average spraying uniformity coefficient, penetration uniformity coefficient, curing uniformity coefficient and rolling path uniformity coefficient; The uniformity coefficient index of the pre-curing operation is obtained based on the uniformity parameter analysis; The uniformity coefficient index of the pre-curing operation is used to characterize the uniformity of material spraying, penetration, curing and rolling path during the pre-curing operation. Specifically, the uniformity coefficient index of the pre-curing operation is obtained by analyzing the comparison results of the average spraying uniformity coefficient, penetration uniformity coefficient, curing uniformity coefficient and rolling path uniformity coefficient with the corresponding reference values and coupling the influence of the corresponding weights; The correction factors corresponding to the uniformity coefficient index intervals stored in the database are extracted, and the correction factors corresponding to the uniformity coefficient index of the pre-maintenance operation are mapped and extracted, which are recorded as the second dynamic correction factors.

8. The method for determining the timing of asphalt pavement pre-curing according to claim 1, wherein: The specific analysis steps for the optimized pre-curing benchmark indicators are as follows: After the pre-maintenance work is completed, the pavement condition data is collected again and analyzed to obtain a comprehensive evaluation value of the pavement condition; The pavement condition comprehensive evaluation value, the first dynamic correction factor and the second dynamic correction factor are coupled to obtain a pavement condition evaluation correction value; Subtract the pre-maintenance benchmark index from the pavement condition evaluation correction value to obtain the pavement condition measured deviation index; Extract the number of historical errors stored in the database; Extract the allowable range of measured deviation indicators based on the number of historical errors; If the measured deviation index of the pavement condition is within the allowable range of the measured deviation index, the preset pre-maintenance benchmark index is used as the optimized pre-maintenance benchmark index; If the measured road condition deviation index exceeds the allowed range of the measured deviation index, the degree of excess of the measured road condition deviation index is obtained and recorded as the measured road condition deviation index deviation value; Extracting the pre-maintenance benchmark index correction value corresponding to the deviation value of the actual road condition deviation index stored in the database; The optimized pre-maintenance benchmark index is obtained based on the analysis of the pre-maintenance benchmark index and the pre-maintenance benchmark index correction value.

9. A system using the method for determining the timing of asphalt pavement pre-curing according to any one of claims 1 to 8, characterized in that: include: A pavement condition evaluation module is used to collect pavement condition data of a target road section based on a preset sampling interval and analyze a comprehensive evaluation value of the pavement condition; The sampling period dynamic adjustment module is used to obtain the environmental prediction data of the target road section and the comprehensive fluctuation index value of the road surface state, thereby generating the sampling period dynamic adjustment coefficient; The optimal pre-maintenance time window determination module is used to adaptively optimize the sampling interval based on the dynamic adjustment coefficient of the sampling period, construct an evolution trajectory model of the time-pavement condition comprehensive evaluation value, and match the evolution trajectory model with the pre-maintenance benchmark indicators to determine the optimal pre-maintenance time window; A pre-curing operation parameter feedback adjustment module is used to perform pre-curing operations based on the optimal pre-curing time window, collect spraying uniformity coefficients in real time, and perform feedback adjustment on pre-curing operation parameters to obtain a first dynamic correction factor; The pre-maintenance benchmark index optimization module is used to obtain uniformity parameters after pre-maintenance is completed, analyze the second dynamic correction factor, collect pavement condition data, and analyze the optimized pre-maintenance benchmark index by combining the first dynamic correction factor, the second dynamic correction factor and the number of historical errors.

10. An electronic device comprising: a processor, a memory for storing instructions executable by the processor; When the processor is configured to execute the instructions, the electronic device implements a method for determining the timing of pre-maintenance of an asphalt pavement.

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