A dynamic high-precision detection method for highway traffic volume and axle load

Abstract

This invention relates to the field of traffic detection technology, and more particularly to a method for dynamic high-precision detection of axle load in highway traffic volume. The method includes the following steps: acquiring raw axle load detection data of highway traffic volume; performing spatiotemporal synchronization and noise suppression processing on the raw axle load detection data, while simultaneously performing load-response mapping estimation to obtain a preliminary estimate of the highway traffic volume axle load; acquiring a vehicle type database and performing vehicle axle load verification and time-segmented distribution statistics, while simultaneously assessing the cumulative axle load damage to the highway pavement to obtain the cumulative axle load damage value; performing dynamic high-precision detection based on the highway time-segmented axle load distribution data and the cumulative axle load damage value, and formulating a corresponding highway pavement axle load maintenance plan. This invention enables real-time acquisition and dynamic updating of highway axle load data, providing high-precision data support for road maintenance and traffic management.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of traffic detection, in particular to a dynamic high-precision detection method for highway traffic volume and axle load. BACKGROUND

[0002] With the continuous development of social economy and the acceleration of urbanization process, the traffic volume of highways continues to increase, and traffic management and road maintenance are facing unprecedented challenges. Dynamic monitoring of traffic volume and vehicle axle load is an important means to ensure the safe operation of highways and optimize traffic management. In the dynamic detection of highways, the axle load of vehicles is one of the key data for evaluating road load, predicting road damage and developing maintenance strategies. However, the traditional detection methods based on static or simplified dynamic models cannot fully reflect the dynamic load characteristics of vehicles under high-speed driving conditions, resulting in low detection accuracy. At the same time, it cannot provide stable detection data under variable environmental conditions, especially in the case of weather changes, traffic congestion or high vehicle speed, which often causes interference, resulting in unstable detection data and inability to timely plan road maintenance. SUMMARY

[0003] Therefore, it is necessary to provide a dynamic high-precision detection method for highway traffic volume and axle load to solve at least one of the above technical problems.

[0004] To achieve the above purpose, a dynamic high-precision detection method for highway traffic volume and axle load comprises the following steps:

[0005] Step S1: Collecting dynamic response signals and vehicle traffic characteristic data corresponding to the highway pavement through the laid multi-type sensor array to obtain the original axle load detection data of the highway traffic volume; performing time-space synchronization and noise suppression processing on the original axle load detection data of the highway traffic volume to generate a standardized axle load detection data set;

[0006] Step S2: Based on the standardized axle load detection data set, vehicle axle type recognition and axle position positioning are performed to extract axle group distribution characteristics and axle spacing parameters, and generate highway vehicle axle system feature data; based on the highway vehicle axle system feature data and the corresponding attenuation characteristics of the pavement response signal, load-response mapping estimation is performed to obtain the preliminary estimated value of the highway traffic volume and axle load;

[0007] Step S3: Acquiring a vehicle type database, and based on the preliminary estimated value of the highway traffic volume and axle load and the vehicle type database, vehicle axle load verification and time period distribution statistics are performed on the standardized axle load detection data set to obtain highway time period axle load distribution data; acquiring highway pavement structure parameters, and based on the highway pavement structure parameters and the highway time period axle load distribution data, pavement axle load cumulative damage evaluation is performed to obtain the highway pavement axle load cumulative damage value;

[0008] Step S4: Based on the highway time-sharing axle load distribution data and the highway pavement axle load cumulative damage value, dynamic high-precision detection is carried out to generate a highway traffic volume axle load dynamic detection report; and a corresponding highway pavement axle load maintenance scheme is formulated according to the highway traffic volume axle load dynamic detection report.

[0009] Further, step S1 includes the following steps:

[0010] Step S11: A multi-type sensor array composed of strain sensors, acceleration sensors arranged at different depth layers of the highway pavement, and laser profile sensors and video recognition devices arranged at the roadside of the highway is used, wherein the strain sensors collect dynamic response signals corresponding to the strain of the pavement when the vehicle passes, the acceleration sensors collect dynamic response signals corresponding to the vibration of the pavement, the laser profile sensors collect the vehicle length and wheelbase geometric parameters, and the video recognition devices collect the vehicle type and passing trajectory to obtain highway traffic volume original axle load detection data;

[0011] Step S12: Based on the time synchronization module corresponding to the multi-type sensor array, the collection time stamps of each sensor are unified to the same clock reference, and the error is controlled within a preset range to generate multi-source data time synchronization parameters;

[0012] Step S13: The coordinate mapping relationship of each sensing detection point is established according to the spatial arrangement position of each sensor in the multi-type sensor array, and the dynamic response signals and vehicle passing feature data in the highway traffic volume original axle load detection data are associated to the same coordinate mapping relationship to generate multi-source data space calibration parameters;

[0013] Step S14: Based on the multi-source data time synchronization parameters and the multi-source data space calibration parameters, the highway traffic volume original axle load detection data is integrated in time and space to generate a highway axle load time-space alignment detection data set containing time and space;

[0014] Step S15: The highway axle load time-space alignment detection data set is subjected to noise suppression and standardization, and high-frequency interference noise is removed by wavelet threshold denoising and standardized processing to generate a standardized axle load detection data set.

[0015] Further, step S2 includes the following steps:

[0016] Step S21: The dynamic response signals in the standardized axle load detection data set are subjected to axle load signal peak value statistics to statistically analyze the peak value occurrence time, peak value occurrence amplitude and peak signal frequency spectrum corresponding to each peak value of the highway axle load dynamic signal to generate a highway axle load signal peak value sequence;

[0017] Step S22: Perform signal peak feature analysis based on the peak value sequence of the highway axle load signal to obtain the peak time interval, amplitude variation rate, and spectral energy distribution corresponding to the highway axle load dynamic signal;

[0018] Step S23: Perform vehicle axle type recognition analysis according to the peak time interval, amplitude variation rate, and spectral energy distribution corresponding to the highway axle load dynamic signal, combined with the corresponding vehicle type and passing trajectory in the standardized axle load detection data set, to identify and distinguish the vehicle axle type groups corresponding to single axle, tandem dual axle, and tandem triple axle, and obtain the highway traffic volume axle type group classification result;

[0019] Step S24: Based on the highway traffic volume axle type group classification result and combined with the peak time interval and amplitude variation rate corresponding to the highway axle load dynamic signal, perform vehicle axle position positioning statistics on the standardized axle load detection data set to generate highway vehicle axle system feature data;

[0020] Step S25: Based on the highway vehicle axle system feature data and combined with the attenuation characteristics of the road response signal, perform load-response mapping estimation to obtain the preliminary estimated value of the highway traffic volume axle load.

[0021] Further, step S24 includes the following steps:

[0022] Step S241: Obtain the corresponding highway vehicle passing speed through the corresponding vehicle type and passing trajectory in the standardized axle load detection data set;

[0023] Step S242: Perform vehicle axle spacing conversion based on the peak time interval and amplitude variation rate corresponding to the highway axle load dynamic signal, combined with the highway vehicle passing speed, to obtain the initial parameter of the highway vehicle axle spacing;

[0024] Step S243: Perform axle spacing comparison and correction calculation on the initial parameter of the highway vehicle axle spacing based on the corresponding vehicle length and wheel spacing geometric parameters in the standardized axle load detection data set to obtain the comparison and correction parameter of the highway axle spacing;

[0025] Step S244: Perform axle position positioning calculation for each axle group based on each vehicle axle type group in the highway traffic volume axle type group classification result, combined with the highway vehicle passing speed, to position and calculate the highway road position coordinates corresponding to each axle group, and generate a highway axle group coordinate data set. Based on the highway axle group coordinate data set, perform axle group distribution statistics to analyze and extract the corresponding axle number and distribution arrangement in each axle group, and obtain the highway axle group distribution characteristics;

[0026] Step S245: Based on the highway axle spacing comparison correction parameter and the distribution characteristics of each axle group of the highway, the vehicle axle system characteristics are integrated to generate highway vehicle axle system characteristic data containing the corresponding axle number, axle position and axle spacing of each axle group.

[0027] Further, step S25 includes the following steps:

[0028] Step S251: Signal attenuation characteristic analysis is performed on the road surface response signals corresponding to the road surface strain and road surface vibration in the standardized axle load detection data set to calculate the amplitude attenuation rate and phase shift amount of the road surface response signal at different propagation distances, and generate highway road surface response attenuation characteristic parameters;

[0029] Step S252: Based on the axle position coordinate information in the highway vehicle axle system characteristic data, the road surface response signal collection points corresponding to each axle group are determined, and based on the road surface response signal collection points corresponding to each axle group, the axle position-attenuation correlation analysis is performed in combination with the highway road surface response attenuation characteristic parameters to obtain the highway vehicle axle position-response attenuation correlation data;

[0030] Step S253: Based on the axle number and axle spacing in the highway vehicle axle system characteristic data and in combination with the response attenuation characteristic parameters corresponding to the highway vehicle axle position-response attenuation correlation data, load-response nonlinear mapping analysis is performed to obtain the nonlinear mapping relationship between the highway vehicle axle load and the road surface response signal attenuation;

[0031] Step S254: Based on the nonlinear mapping relationship between the highway vehicle axle load and the road surface response signal attenuation and in combination with the highway vehicle axle system characteristic data and the highway road surface response attenuation characteristic parameters, the axle load-response mapping function is constructed to generate the highway vehicle axle load-response mapping function;

[0032] Step S255: Based on the highway vehicle axle load-response mapping function, load-response mapping estimation is performed to obtain the preliminary estimated value of the highway traffic volume axle load.

[0033] Further, step 255 includes the following steps:

[0034] By arranging environmental sensors on the corresponding highway pavement to synchronously collect corresponding temperature, humidity, road surface wetness and rainfall data, a highway pavement environmental parameter data set is generated;

[0035] Based on each environmental parameter in the highway pavement environmental parameter data set, environmental-axle load characteristic influence analysis is performed on the highway vehicle axle system characteristic data to generate a highway environmental-axle load characteristic influence distribution curve;

[0036] The axle load influence correction factor corresponding to different environment condition combinations is calculated based on the highway environment-axle load characteristic influence distribution curve, and the load-response mapping correction estimation is performed based on the highway axle load influence correction factor and the highway vehicle axle load-response mapping function, so as to obtain the preliminary estimation value of the highway traffic volume axle load.

[0037] Further, step S3 comprises the following steps:

[0038] Step S31: Obtain a vehicle type database, which comprises the standard axle load range, typical axle group configuration and axle load distribution corresponding to different vehicle types;

[0039] Step S32: Based on the preliminary estimation value of the highway traffic volume axle load and the vehicle type database, the vehicle axle load verification is performed on the standardized axle load detection data set, so as to generate the verified highway traffic volume axle load data;

[0040] Step S33: According to the preset time period cycle, the time period distribution statistics is performed on the verified highway traffic volume axle load data, so as to calculate and count the vehicle quantity, axle load sum and proportion corresponding to different axle load intervals in each time period, so as to obtain the highway time period axle load distribution data;

[0041] Step S34: Obtain the highway pavement structure parameters, which comprise the thickness, material type, compressive strength, flexural tensile strength and fatigue characteristic parameters of each structure layer of the pavement;

[0042] Step S35: Based on the highway pavement structure parameters and the highway time period axle load distribution data, the pavement axle load cumulative damage evaluation is performed, so as to obtain the highway pavement axle load cumulative damage value.

[0043] Further, step S32 comprises the following steps:

[0044] Based on the vehicle type database, the standard axle load parameters corresponding to the matched vehicle type axle group are obtained, and the deviation comparison calculation is performed based on the preliminary estimation value of the highway traffic volume axle load and the standard axle load parameters, so as to obtain the highway traffic volume axle load deviation rate;

[0045] Based on the comparison between the highway traffic volume axle load deviation rate and the preset axle load deviation range, the standardized axle load detection data set corresponding to the highway traffic volume axle load deviation rate exceeding the preset axle load deviation range is screened out, so as to obtain the highway axle load data which does not conform to the deviation rate;

[0046] Obtain the wheelbase and tire quantity constraint conditions corresponding to the vehicle type, and based on the wheelbase and tire quantity constraint conditions corresponding to the vehicle type, the highway axle load data that does not meet the deviation rate is constrained, corrected, adjusted and verified, and the verified highway traffic volume axle load data is integrated with the highway axle load data that meets the deviation rate, to generate the verified highway traffic volume axle load data.

[0047] Further, step S35 includes the following steps:

[0048] Step S351: Based on the material type and fatigue characteristic parameters in the highway pavement structure parameters, axle load conversion matching analysis is performed to determine the equivalent axle conversion coefficients corresponding to different axle loads, and a highway fatigue axle load equivalent conversion table is generated;

[0049] Step S352: Based on the highway fatigue axle load equivalent conversion table and the highway time period axle load distribution data, the equivalent axles in each time period corresponding to different axle load intervals are calculated to generate highway time period equivalent axle data;

[0050] Step S353: Based on the thickness, compressive strength and flexural tensile strength of each structural layer of the pavement in the highway pavement structure parameters, a pavement axle load damage evaluation model is constructed, and the highway time period equivalent axle data is input into the pavement axle load damage evaluation model to calculate the cumulative damage amount of each structural layer of the pavement in time sequence, and the cumulative damage amount of each structural layer of the pavement is summed based on the cumulative damage amount of each structural layer of the pavement to obtain the highway pavement axle load cumulative damage value.

[0051] Further, step S4 includes the following steps:

[0052] Step S41: Obtain the highway vehicle axle load quantity and proportion corresponding to different axle load intervals and the pavement axle load cumulative damage value in each time period through the highway time period axle load distribution data and the highway pavement axle load cumulative damage value;

[0053] Step S42: Based on the highway vehicle axle load quantity and proportion corresponding to different axle load intervals and the pavement axle load cumulative damage value in each time period, axle load-damage correlation analysis is performed to analyze and identify the correlation between the vehicle axle load distribution and the cumulative damage in each time period, and generate the highway vehicle axle load-cumulative damage correlation feature;

[0054] Step S43: Obtain the highway real-time axle load detection data, and based on the highway vehicle axle load-cumulative damage correlation feature, perform dynamic high-precision detection correction on the highway real-time axle load detection data to generate a highway traffic volume axle load dynamic detection report;

[0055] Step S44: comparing and evaluating the accumulated damage value of the corresponding road surface in the highway traffic volume axle load dynamic detection report with the preset road surface design service life threshold to obtain the current damage degree of the highway road surface;

[0056] Step S45: formulating the corresponding vehicle axle load restriction and maintenance opportunity plan based on the current damage degree of the highway road surface and combining the highway traffic volume axle load dynamic detection report, and generating the highway road surface axle load maintenance scheme including the maintenance process, the maintenance range and the implementation priority.

[0057] The beneficial effects of the present application are:

[0058] The high-speed highway traffic volume axle load dynamic high-precision detection method has the beneficial effects that, compared with the prior art, the dynamic response signal and the vehicle passing feature data of the high-speed highway pavement are collected through the layout of the multi-type sensor array, the original axle load detection data of the high-speed highway traffic volume can be obtained in real time and comprehensively, different types of sensors can capture different traffic information such as vehicle axle load, speed, position, etc., the diversity and integrity of the data are ensured, sufficient original data support is provided for subsequent data processing, the time and space synchronous processing can effectively unify and coordinate the sensor data from different positions and times, the time and space errors are eliminated, the timeliness and accuracy of the data are ensured, and the noise suppression processing can remove the noise caused by the environment, equipment or other interference factors, so that the data set obtained finally has a high signal-to-noise ratio, the reliability of subsequent analysis is improved, and the accurate evaluation of traffic flow and pavement damage is promoted. Secondly, on the basis of the standardized axle load detection data set, vehicle axle type recognition and axle position positioning are performed, more detailed and specific information for traffic volume estimation can be provided, the axle group distribution characteristics and axle spacing parameters of the vehicle can be extracted through accurate identification of the axle type and axle position of each vehicle, a more in-depth basis for understanding the load applied by the vehicle to the pavement is provided, these data not only reflect the characteristics of the traffic flow, but also reflect the vehicle weight and distribution, so that the bearing capacity of the pavement at different time periods can be more accurately evaluated. Meanwhile, through the combination of the load-response mapping estimation based on the attenuation characteristics of the pavement response signal, the preliminary estimated value of the traffic volume axle load of the high-speed highway can be calculated, and a reliable load basis for further analysis is provided. Then, by obtaining and utilizing the vehicle type database, the axle load verification is performed by combining the high-speed highway traffic volume axle preliminary estimated value and the vehicle type database, the previous estimation results can be verified, and the traffic flow distribution data of each period can be further refined, this process effectively improves the accuracy of the vehicle axle load data, and the fluctuation trend of the traffic flow in different time periods can be found in the statistics, which helps decision-makers understand the changes of the road use intensity in different time periods, so as to provide data basis for the formulation of the pavement maintenance plan. Further, the pavement axle load cumulative damage evaluation is performed by combining the pavement structure parameters of the high-speed highway, the damage accumulation of the road under different traffic intensities can be quantified, and the environmental influence factor is introduced, so that more accurate and stable detection data can be provided under variable environmental conditions, the evaluation result can reflect the damage degree of the pavement in the long-term use, which is helpful for predicting the demand for pavement maintenance in advance and providing a reference for formulating a scientific maintenance strategy.Finally, by conducting dynamic high-precision detection based on the time-segmented axle load distribution data and the cumulative axle load damage value of the highway pavement, a more accurate dynamic traffic volume and axle load detection report can be obtained. This report not only provides real-time dynamic traffic volume data but also reflects the damage changes of the pavement under the actual traffic flow, thus providing comprehensive data support for the formulation of maintenance plans. Dynamic detection can help relevant departments monitor traffic volume and pavement conditions in real time, promptly identify potential damage risks, and formulate targeted emergency response plans. This enables more scientific planning of road maintenance work, thereby avoiding the decline in road function caused by sudden damage or uneven traffic load, and also providing stronger protection for road safety. Attached Figure Description

[0059] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0060] Figure 1 This is a schematic diagram of the steps in the method for dynamic high-precision detection of axle load in highway traffic volume according to the present invention.

[0061] Figure 2 for Figure 1 A detailed flowchart of step S1;

[0062] Figure 3 for Figure 1 A detailed flowchart of step S2. Detailed Implementation

[0063] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0064] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0065] It should be understood that, although the terms "first", "second" and the like can be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the example embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0066] To achieve the above object, please refer to Figures 1 to 3 The application provides a dynamic high-precision detection method for highway traffic volume axle load, which comprises the following steps:

[0067] Step S1: collecting dynamic response signals and vehicle passing feature data corresponding to the highway pavement through a plurality of types of sensor arrays arranged to obtain original axle load detection data of the highway traffic volume; and performing time-space synchronization and noise suppression processing on the original axle load detection data of the highway traffic volume to generate a standardized axle load detection data set;

[0068] Step S2: identifying vehicle axle types and positioning axle positions based on the standardized axle load detection data set to extract axle group distribution features and axle spacing parameters, and generating vehicle axle system feature data of the highway; and performing load-response mapping estimation based on the vehicle axle system feature data of the highway and in combination with the corresponding attenuation characteristics of the pavement response signals to obtain preliminary estimated values of the highway traffic volume axle load;

[0069] Step S3: obtaining a vehicle type database, and performing vehicle axle load verification and time period distribution statistics on the standardized axle load detection data set based on the preliminary estimated values of the highway traffic volume axle load and the vehicle type database to obtain highway time period axle load distribution data; obtaining highway pavement structure parameters, and performing pavement axle load cumulative damage assessment based on the highway pavement structure parameters in combination with the highway time period axle load distribution data to obtain a highway pavement axle load cumulative damage value;

[0070] Step S4: performing dynamic high-precision detection based on the highway time period axle load distribution data and the highway pavement axle load cumulative damage value to generate a highway traffic volume axle load dynamic detection report; and formulating a corresponding highway pavement axle load maintenance scheme according to the highway traffic volume axle load dynamic detection report.

[0071] In the embodiment of the application, please refer to Figure 1 The application provides a dynamic high-precision detection method for highway traffic volume axle load, which comprises the following steps:

[0072] Step S1: Collecting dynamic response signals and vehicle traffic characteristic data corresponding to the highway pavement through the laid multi-type sensor array to obtain the original axle load detection data of the highway traffic volume; performing time-space synchronization and noise suppression processing on the original axle load detection data of the highway traffic volume to generate a standardized axle load detection data set;

[0073] In the embodiment of the application, a multi-type sensor array is laid from K10+000 to K10+500 of the four-lane highway, and a group of detection points is arranged every 10 m along the longitudinal direction of each lane. Each group includes a fiber bragg grating strain gauge (range -2000 to +2000με, accuracy ±2με, sampling frequency 1 kHz) buried 5 cm below the surface layer of the pavement, a piezoelectric accelerometer (range ±50g, sensitivity 100 mV / g, sampling frequency 2 kHz) 10 cm below the surface layer, a laser profile sensor (scanning frequency 100 Hz, measurement range 0.5-10 m, accuracy ±2 mm) installed 1.5 m from the roadside, and a high-definition camera (resolution 1920×1080, frame rate 25 fps) fixed on a 6 m high pole. When a six-axle truck passes at 60 km / h, the strain gauge collects a 1200με strain signal, the accelerometer records 8g vibration data, the laser profile sensor measures a vehicle length of 12 m and a wheelbase of 1.8 m, and the camera identifies a heavy truck and records the trajectory. The time stamps of each sensor are unified to ±1 ms through a GPS time module (synchronization accuracy 10 ns), a space mapping is established according to the lane coordinate system (X axis along the driving direction, Y axis transversely, and origin at the center of K10+000 lane), and the strain, vibration, and geometric parameters of the same vehicle are associated to the same coordinate point. Noise reduction is performed using db4 wavelet basis 5-layer decomposition, strain spikes >2000με and vibration outliers >50g are removed, and the data is standardized to the [-1,1] interval to generate a standardized axle load detection data set containing time stamp, coordinate, strain, vibration, vehicle length, wheelbase, and vehicle type.

[0074] Step S2: Vehicle axle type identification and axle position positioning based on the standardized axle load detection data set to extract axle group distribution characteristics and axle spacing parameters, and generate highway vehicle axle system characteristic data; load-response mapping estimation based on the highway vehicle axle system characteristic data and in combination with the corresponding attenuation characteristics of the pavement response signal to obtain preliminary estimated values of the highway traffic volume axle load;

[0075] In the embodiment of the present application, by identifying the axle type of the standardized axle load detection data set: extracting the peak value sequence of the strain signal of the six-axle truck, the first peak value appears at 1620000000.123s (1200με), the second peak value appears at 1620000000.303s (1100με), the interval is 0.18s, and the amplitude decreases by 8.3%, which is determined as a double axle; the interval between the third and fourth peak values is 0.12s (910με, 868με), and the decrease is 4.6%, which is a double axle; the interval between the fifth and sixth peak values is 0.24s (812με, 742με), and the decrease is 8.6%, which is a double axle, and the axle type group is determined as 6x4. The axle position coordinates are calculated in combination with the vehicle speed of 16.67m / s: the first axle X=10.00m, the second axle X=13.00m, the third axle X=17.00m, the fourth axle X=19.00m, the fifth axle X=22.00m, and the sixth axle X=26.00m, Y is 2.8m, the axle spacing is 3.00m, 4.00m, 2.00m, 3.00m, and 4.00m, and the axle system characteristic data is generated. The road surface response attenuation characteristics are analyzed: the strain of 100-120kN axle load is 1200με at X=10m, and the strain is attenuated to 720με (attenuation rate 40%) at X=15m, and the mapping function axle load (kN)=strain (με) x 0.05 ÷ (1-attenuation rate / 100) is established. The axle data is substituted: the first axle 1200με x 0.05 ÷ 1=60kN, the second axle 1100με x 0.05 ÷ 0.85≈64.71kN, the third axle 910με x 0.05 ÷ 0.7≈65kN, the fourth axle 868με x 0.05 ÷ 0.65≈66.77kN, the fifth axle 812με x 0.05 ÷ 0.6≈67.67kN, and the sixth axle 742με x 0.05 ÷ 0.55≈67.45kN, and the preliminary estimated value of the axle load is obtained.

[0076] Step S3: obtaining a vehicle type database, and based on the preliminary estimated value of the highway traffic axle load and the vehicle type database, the vehicle axle load verification and the time period distribution statistics of the standardized axle load detection data set are performed to obtain the highway time period axle load distribution data; obtaining the highway pavement structure parameters, and based on the highway pavement structure parameters, the highway time period axle load distribution data is combined to perform the pavement axle load cumulative damage evaluation, and the highway pavement axle load cumulative damage value is obtained;

[0077] In the embodiment of the present application, the standard axle load range of the 6x4 six-axle truck in the vehicle type database is: 65-75kN for the first and second axles, 95-105kN for the third and fourth axles, and 110-120kN for the fifth and sixth axles. Comparing the preliminary estimated value with the standard, the deviation of the third to sixth axles is more than 20%, so the constraint correction is performed: the third and fourth axles are corrected to 98kN and 102kN (double axle balance, total 200kN), the fifth and sixth axles are corrected to 113kN and 117kN (total 230kN), and the first and second axles remain 60kN and 64.71kN (deviation <5kN), to generate the data after verification. According to the early peak 7:00-9:00, the flat peak 9:00-17:00, and the late peak 17:00-19:00, statistics are as follows: 100 trucks in the early peak, 200 axles of 50-70kN (total 12471kN, accounting for 22%), and 400 axles of 100-120kN (total 43000kN, accounting for 78%); 80 trucks in the flat peak, corresponding to 160 axles (9976.8kN) and 320 axles (34400kN); 120 trucks in the late peak, corresponding to 240 axles (14965.2kN) and 480 axles (51600kN), to obtain the axle load distribution data in different time periods. Obtain the pavement structure parameters: asphalt surface layer 18cm (compression resistance 4.5MPa, bending tensile resistance 1.0MPa, fatigue life 5.0x10 6 times), cement stabilized macadam base 36cm (compression resistance 3.0MPa, bending tensile resistance 0.8MPa, fatigue life 3.0x10 6 times). Calculate the damage using the Miner theory: 354.65 equivalent axles in the early peak, surface layer damage 7.09x10 -5 , base layer 1.18x10 -4 ; 210.32 in the flat peak, surface layer 4.21x10 -5 , base layer 7.01x10 -5 ; 380.17 in the late peak, surface layer 7.60x10 -5 , base layer 1.27x10 -4 , and the total cumulative damage value is 5.04x10 -4 .

[0078] Step S4: Based on the highway axle load distribution data in different time periods and the cumulative damage value of the highway pavement axle load, dynamic high-precision detection is performed to generate a highway traffic volume axle load dynamic detection report; and a corresponding highway pavement axle load maintenance scheme is formulated according to the highway traffic volume axle load dynamic detection report.

[0079] In the embodiment of the present application, from the time period data and the cumulative damage value, 200 axles of 50-70kN in the early peak (accounting for 22%, damage 5.04x10 -5), 100-120 kN axis 400 (accounting for 78%, damage 2.565 x 10 -4 ); flat peak corresponding to 160 (3.85 x 10 -5 ), 320 (1.978 x 10 -4 ); late peak 240 (5.78 x 10 -5 ), 480 (3.08 x 10 -4 ). Analysis of 100-120 kN axis contribution 83%-85% damage, late peak damage is 1.2 times the early peak, forming the axis load-damage correlation characteristics. Real-time detection of 100-120 kN axis 500 (increased by 25%), 50-70 kN axis 200, according to the late peak coefficient 1.2 correction damage value is 4.351 x 10 -4 , generate dynamic report, including axis number 700, accounting for 71.4% / 28.6%, deviation rate +25%. The preset life threshold is 1.0 (15 years), the current damage degree is 5.04 x 10 -4 x 365 x 3 ≈ 0.552 (accounting for 62.5% of the base layer), the remaining life is about 2.4 years. Formulate maintenance program: late peak limit 100-120 kN axis load vehicle 30%, speed limit 80 km / h; base layer within 1 month with emulsified asphalt (dosage 3.5 kg / m 2 , 160℃ spread), range K10+000-K15+000; surface layer within 3 months micro-surfacing (3-5mm stone, oil-stone ratio 6.0%), full section coverage, ensure that the base layer damage rate is reduced by 40%, the surface layer is extended by 2 years.

[0080] Further, as an embodiment of the present application, referring to FIG. Figure 2 , it is a detailed step flow diagram of step S1 in Figure 1 , step S1 in this embodiment includes the following steps:

[0081] Step S11: By arranging strain sensors, acceleration sensors at different depths of the highway pavement, and a multi-type sensor array composed of laser profile sensors and video recognition devices arranged on the roadside of the highway, wherein the strain sensors collect dynamic response signals corresponding to the strain of the road surface when the vehicle passes, the acceleration sensors collect dynamic response signals corresponding to the vibration of the road surface, the laser profile sensors collect the vehicle length and wheel spacing geometric parameters, and the video recognition device collects the vehicle type and passing trajectory, to obtain the original axle load detection data of the highway traffic volume;

[0082] In the embodiment of the application, by arranging a plurality of types of sensor arrays in the highway pavement structure layer, a strain sensor adopts a buried optical fiber grating strain gauge (range -2000 to +2000με, accuracy ±2με), which is buried at a depth of 5 cm, 10 cm and 15 cm below the surface layer of the pavement respectively, and a group of three sensors is arranged every 10 m along the longitudinal direction of the lane in an equilateral triangle distribution (spacing 50 cm) for collecting strain dynamic response signals (sampling frequency 1 kHz) generated when a vehicle passes. An acceleration sensor is a piezoelectric accelerometer (range ±50g, sensitivity 100 mV / g), which is arranged at the same depth as the strain sensor for collecting pavement vibration acceleration signals (sampling frequency 2 kHz). A laser profile sensor (scanning frequency 100 Hz, measurement range 0.5-10 m, accuracy ±2 mm) is arranged at a height of 1.5 m from the pavement on the roadside, and is installed at an interval of 3 m along the lateral direction of the lane for collecting geometric parameters such as vehicle length (error ±5 cm) and wheelbase (error ±3 cm). A video recognition device is a high-definition camera (resolution 1920×1080, frame rate 25 fps), which is installed on a roadside pole (height 6 m) with the lens facing the oncoming vehicle direction, and collects vehicle type (distinguishing between cars, trucks and buses) and passing track (positioning accuracy ±10 cm). When a six-axle truck passes through the detection area at a speed of 60 km / h, the strain sensor outputs a strain signal of 1200με, the acceleration sensor outputs a vibration signal of 8g, the laser profile sensor measures a vehicle length of 12 m and a wheelbase of 1.8 m, the video recognition device determines that it is a heavy truck, and the track deviation is ≤5 cm. All data are collected to form the original axle load detection data of the highway traffic volume.

[0083] Step S12: based on the time synchronization module corresponding to the plurality of types of sensor arrays, the collection time stamps of the sensors are unified to the same clock reference, and the error is controlled within a preset range, to generate a multi-source data time synchronization parameter;

[0084] In the embodiment of the application, the time synchronization module is configured by the plurality of types of sensor arrays, GPS time service (synchronization accuracy 10 ns) is combined with a local constant temperature crystal oscillator (frequency stability 1×10 -9)Construct clock reference. The acquisition time stamp of strain sensor, acceleration sensor is accessed to the synchronization module through RS485 bus, and the laser profile sensor and the video recognition device realize time synchronization through Ethernet (IEEE1588 PTP protocol). The synchronization module calibrates the clock of each sensor once every 100 ms, and controls the time stamp error in the range of ±1 ms. For example, the time stamp of the strain sensor for collecting the strain signal of a truck is 1620000000.123 s, the time stamp of the acceleration sensor for collecting the vibration signal is 1620000000.124 s, the time stamp of the laser profile sensor for detecting the truck is 1620000000.123 s, and the time stamp of the video recognition device for capturing the truck is 1620000000.125 s. After calibration by the synchronization module, all time stamps are unified to 1620000000.123 s±0.5 ms, and multi-source data time synchronization parameters are generated to ensure that the time marks of different sensor data triggered by the same vehicle are consistent.

[0085] Step S13: establishing a coordinate mapping relationship of each sensing detection point according to the spatial layout position of each sensor in the multi-type sensor array, and associating the dynamic response signal in the original axle load detection data of the highway traffic volume and the vehicle passing feature data to the same coordinate mapping relationship to generate multi-source data space calibration parameters.

[0086] In the embodiment of the application, the coordinate mapping relationship is established according to the spatial layout position of the sensor, and the lane starting point is taken as the origin (0, 0, 0), the longitudinal direction of the lane is taken as the X axis (precision ±1 cm), the transverse direction is taken as the Y axis (precision ±1 cm), and the vertical direction is taken as the Z axis (depth, precision ±0.5 cm). The strain sensor is located at (10 m, 2.5 m, 5 cm), (10 m, 3.0 m, 10 cm), and (10 m, 3.5 m, 15 cm), the acceleration sensor is located at (10 m, 3.0 m, 5 cm), (10 m, 3.0 m, 10 cm), and (10 m, 3.0 m, 15 cm), the laser profile sensor is located at (10 m, 0 m, 1.5 m), and the video recognition device is located at (10 m, 5 m, 6 m). When the front wheel of a vehicle passes X=10 m, the strain sensor generates a strain signal at X=10 m, the laser profile sensor detects the wheel spacing at X=10 m, and the video recognition device records the front wheel track at X=10 m. Through coordinate mapping, these data are associated to the same transverse section (Y axis range 0-5 m) at X=10 m, multi-source data space calibration parameters are generated, the spatial correspondence between the dynamic response signal (at X=10 m, Y=3.0 m) and the vehicle passing feature (at X=10 m, Y=2.8 m, front wheel) is determined, and the error is controlled within ±5 cm.

[0087] Step S14: based on the multi-source data time synchronization parameters and multi-source data space calibration parameters, the highway traffic volume original axle load detection data is integrated in time and space, and a highway axle load time-space alignment detection data set containing time and space is generated;

[0088] In the embodiment of the application, the original axle load detection data is integrated in time and space based on the multi-source data time synchronization parameters (time stamp unified to ±0.5ms) and space calibration parameters (coordinate mapping error ±5cm). A certain truck passes X=10m at 1620000000.123s, at which time the strain sensor (10m, 3.0m, 10cm) collects 1200με strain, the acceleration sensor (10m, 3.0m, 10cm) collects 8g vibration, and the laser profile sensor measures the wheel track at this position to be 1.8m, and the video recognition device records that the front wheel of the six-axle truck passes. When integrating, these data are marked as associated data of the same time (1620000000.123s) and the same space position (X=10m), and are arranged in order according to the vehicle driving direction (X-axis increases). For the continuous positions of the vehicle passing (X=10m, X=10.5m, X=11m…), the sensor data of the corresponding time points are integrated respectively to form a highway axle load time-space alignment detection data set containing time stamp, X / Y / Z coordinate, strain value, acceleration value, vehicle length, wheel track and vehicle type, so as to ensure that the full journey data of the same vehicle is continuously associated in time and space dimensions.

[0089] Step S15: noise suppression and standardization are performed on the highway axle load time-space alignment detection data set to remove high-frequency interference noise by wavelet threshold denoising and standardization processing, so as to generate a standardized axle load detection data set.

[0090] In this embodiment of the invention, noise suppression and standardization are performed on the spatiotemporal alignment detection dataset of highway axle loads. Wavelet threshold denoising is used: a db4 wavelet basis is selected, and the data is decomposed into 5 layers. Hard thresholding is applied to the high-frequency coefficients of each layer (the threshold is 1.5 times the root mean square error) to remove high-frequency interference (such as instantaneous spikes caused by road particle vibration) >2000με in the strain signal and outliers (such as sensor noise) >50g in the acceleration signal. During standardization, the strain data is converted to the [-1,1] interval (mean 800με, standard deviation 300με) according to (measured value - mean) / standard deviation, and the acceleration data is converted to the [-1,1] interval (mean 5g, standard deviation 2g). Geometric parameters such as vehicle length and wheelbase are standardized according to the ratio of actual size to the maximum possible value (vehicle length 20m, wheelbase 2.5m). After processing, the strain data of a truck was converted from 1200με to 1.33, the acceleration data from 8g to 1.5, the vehicle length from 12m to 0.6, and the wheelbase from 1.8m to 0.72. All data were retained to three decimal places to generate a standardized axle load test dataset, eliminating the influence of dimensions to facilitate subsequent analysis.

[0091] Furthermore, as an embodiment of the present invention, reference is made to... Figure 3 As shown, Figure 1 A detailed flowchart of step S2 is shown below. In this embodiment, step S2 includes the following steps:

[0092] Step S21: Perform peak value statistics on the corresponding dynamic response signals in the standardized axle load detection dataset to statistically analyze the peak occurrence time, peak amplitude, and peak signal spectrum of each peak value of the highway axle load dynamic signal, and generate a highway axle load signal peak value sequence.

[0093] In the embodiment of the present application, the peak value of the axle load signal is counted by detecting the dynamic response signal (strain signal range -2000 to +2000με, sampling frequency 1 kHz) in the standardized axle load detection data set. The strain signal sequence of the six-axle truck passing through is selected, and six effective peak values are identified by the peak value detection algorithm (threshold value is set to 500με). The first peak value appears at 1620000000.123s, with an amplitude of 1200με. The main peak frequency of the frequency spectrum obtained by Fourier transform is 10 Hz. The second peak value appears at 1620000000.303s, with an amplitude of 1100με. The main peak frequency of the frequency spectrum is 12 Hz. The third to sixth peak values appear at 1620000000.543s, 1620000000.663s, 1620000000.843s, and 1620000001.083s, respectively, with amplitudes of 910με, 868με, 812με, and 742με, respectively. The main peak frequencies of the frequency spectrum are 9 Hz, 11 Hz, 10 Hz, and 12 Hz, respectively. These data are arranged in time sequence to generate a highway axle load signal peak value sequence, which contains the accurate time stamp (to the millisecond level), amplitude (to 1με), and spectral parameters of each peak value, ensuring complete recording of the peak value characteristics.

[0094] Step S22: performing signal peak value feature analysis based on the highway axle load signal peak value sequence to obtain the peak value time interval, amplitude change rate, and spectral energy distribution corresponding to the highway axle load dynamic signal;

[0095] In the embodiment of the present application, the signal peak value feature analysis is performed based on the highway axle load signal peak value sequence. The peak value time interval is calculated: the interval between the first and second peak values is 0.18s, the interval between the second and third peak values is 0.24s, the interval between the third and fourth peak values is 0.12s, the interval between the fourth and fifth peak values is 0.18s, and the interval between the fifth and sixth peak values is 0.24s. The amplitude change rate is calculated: the amplitude decreases by 8.3% ((1200-1100) / 1200x100%) from the first to the second peak value, the amplitude decreases by 17.3% from the second to the third peak value, the amplitude decreases by 4.6% from the third to the fourth peak value, the amplitude decreases by 6.4% from the fourth to the fifth peak value, and the amplitude decreases by 8.6% from the fifth to the sixth peak value. The spectral energy distribution is analyzed: the energy of the 10 Hz frequency component accounts for 35%, the energy of the 11 Hz frequency component accounts for 25%, the energy of the 12 Hz frequency component accounts for 30%, and the energy of other frequencies accounts for 10%. The energy is mainly concentrated in the 9-12 Hz frequency band. By drawing the time interval curve, the amplitude change rate curve, and the spectral energy column chart, the peak value feature parameters corresponding to the highway axle load dynamic signal are obtained, and the time sequence and energy correlation between the peak values are determined.

[0096] Step S23: According to the peak time interval, amplitude change rate and spectral energy distribution corresponding to the highway axle load dynamic signal, and combining the corresponding vehicle type and passing trajectory in the standardized axle load detection data set, vehicle axle type recognition analysis is carried out to identify and distinguish single axle, double axle and three axle vehicle axle type groups, and the highway traffic volume axle type group classification result is obtained;

[0097] In the embodiment of the application, vehicle axle type recognition is performed according to the peak time interval, amplitude change rate and spectral energy distribution, and combining the vehicle type (six axle truck) and passing trajectory (straight driving, offset <5cm) in the standardized axle load detection data set. The first and second peak intervals are 0.18s (corresponding to an axle spacing of 3.00m), the amplitudes are similar (a decrease of 8.3%), and the spectral characteristics are consistent (10Hz, 12Hz), which are determined as double axle. The third and fourth peak intervals are 0.12s (axle spacing 2.00m), the amplitude decreases by 4.6%, and the spectra both contain an 11Hz component, which are determined as double axle. The fifth and sixth peak intervals are 0.24s (axle spacing 4.00m), the amplitude decreases by 8.6%, and the spectral characteristics match, which are determined as double axle. Combining the vehicle type, the axle type group is confirmed as "3 double axle groups", i.e. 6x4 axle type group, and the highway traffic volume axle type group classification result is generated. Each axle type group is marked with the corresponding peak group number and characteristic parameters to ensure that the axle type recognition is consistent with the actual structure.

[0098] Step S24: Based on the highway traffic volume axle type group classification result and combining the peak time interval and amplitude change rate corresponding to the highway axle load dynamic signal, vehicle axle position positioning statistics are performed on the standardized axle load detection data set, and the highway vehicle axle system characteristic data is generated;

[0099] In the embodiment of the present application, the vehicle axle position is statistically positioned by classifying the results of the shaft type group (6x4 shaft type group) and the peak time interval and amplitude change rate based on the standardized axle load detection data set. The known vehicle passing speed is 60.0 km / h (16.67 m / s), the first peak appears when the front wheel of the vehicle is located at X=10.00 m, and the axle position coordinates are calculated according to the time interval: the second axle X=10.00+16.67x0.18=13.00 m, the third axle X=13.00+16.67x0.24=17.00 m, the fourth axle X=17.00+16.67x0.12=19.00 m, the fifth axle X=19.00+16.67x0.18=22.00 m, the sixth axle X=22.00+16.67x0.24=26.00 m, and the Y coordinates are all 2.8 m (track center). The number of shafts is 6, the shaft spacing is 3.00 m, 4.00 m, 2.00 m, 3.00 m, and 4.00 m, and the highway vehicle axle system characteristic data is generated, including axle position coordinates (accurate to 0.01 m), shaft spacing (accurate to 0.01 m), and shaft type grouping, which fully reflects the spatial distribution of the vehicle axle system.

[0100] Step S25: Load-response mapping estimation is performed based on the highway vehicle axle system characteristic data and in combination with the attenuation characteristics of the corresponding road response signal to obtain the preliminary estimated value of the highway traffic volume axle load.

[0101] In the embodiment of the present application, load-response mapping estimation is performed based on the highway vehicle axle system characteristic data (axle position, shaft spacing) and the attenuation characteristics of the road response signal (15% attenuation per 5 m). The first axle is located at X=10.00 m (attenuation rate 0%), the strain is 1200 με, and the calculation according to the mapping relationship (axle load=strainx0.05÷(1-attenuation rate / 100)) gives 60 kN; the second axle X=13.00 m (attenuation rate 15%), the strain is 1100 με, and the calculation gives 1100x0.05÷0.85≈64.71 kN; the third axle X=17.00 m (attenuation rate 30%), the strain is 910 με, and the calculation gives 910x0.05÷0.7≈65.00 kN; the fourth axle X=19.00 m (attenuation rate 35%), the strain is 868 με, and the calculation gives 868x0.05÷0.65≈66.77 kN; the fifth axle X=22.00 m (attenuation rate 40%), the strain is 812 με, and the calculation gives 812x0.05÷0.6≈67.67 kN; the sixth axle X=26.00 m (attenuation rate 45%), the strain is 742 με, and the calculation gives 742x0.05÷0.55≈67.45 kN. The generated preliminary estimated value of the highway traffic volume axle load is rounded to two decimal places, providing basic data for subsequent correction.

[0102] Further, step S24 includes the following steps:

[0103] Step S241: Obtain the highway vehicle passing speed corresponding to the vehicle type and passing trajectory in the standardized axle load detection data set;

[0104] In the embodiment of the present application, the highway vehicle passing speed is obtained by the vehicle type (six-axle truck) in the standardized axle load detection data set and the passing trajectory data. The passing trajectory data contains the continuous position record of the vehicle in the X-axis direction, and the time stamp interval is 0.1 seconds. The time of a certain six-axle truck passing X=10m is 1620000000.123s, and the time of passing X=20m is 1620000000.723s, the distance between the two points is 10m, and the time difference is 0.6 seconds. The calculation speed is 10m ÷ 0.6s = 16.67m / s, which is converted to 60km / h (1m / s = 3.6km / h). The average value of the speed calculation results of the continuous 5 position points (59.8km / h, 60.2km / h, 60.0km / h, 59.9km / h, 60.1km / h) is taken, and the final vehicle passing speed is determined as 60.0km / h, with the error controlled within ±0.5km / h, which ensures that the speed data accurately reflects the actual driving state of the vehicle.

[0105] Step S242: Based on the peak time interval and amplitude change rate corresponding to the highway axle load dynamic signal, and combined with the highway vehicle passing speed, the vehicle axle spacing is converted to obtain the initial parameter of the highway vehicle axle spacing;

[0106] In the embodiment of the present application, the axle spacing is converted based on the peak time interval and amplitude change rate of the highway axle load dynamic signal combined with the vehicle passing speed 60.0km / h (16.67m / s). In the dynamic signal of the six-axle truck collected by the strain sensor, the peak time interval corresponding to the adjacent two axles is 0.3 seconds, and the amplitude change rate decreases from 1200με to 1000με (decrease of 16.7%). The calculation of the axle spacing = speed × time interval = 16.67m / s × 0.3s = 5.00m. The peak time interval of all adjacent axle pairs (0.3s, 0.4s, 0.5s, 0.3s, 0.4s) is calculated in turn, and the initial parameter of the axle spacing is obtained as 5.00m, 6.67m, 8.33m, 5.00m, 6.67m, each value is kept to two decimal places, and the axle spacing conversion result is consistent with the actual axle distribution trend of the vehicle.

[0107] Step S243: Based on the vehicle length and wheelbase geometric parameters corresponding to the vehicle in the standardized axle load detection data set, the initial parameter of the highway vehicle axle spacing is calculated for axle spacing comparison and correction to obtain the highway axle spacing comparison and correction parameter;

[0108] In the embodiment of the application, the initial parameters of the axle spacing are compared and corrected based on the vehicle length 12 m (standardized value 0.6) and the wheel track 1.8 m (standardized value 0.72) in the standardized axle load detection data set. The relationship between the theoretical vehicle length and the total axle spacing of a six-axle truck is: total axle spacing = vehicle length - front suspension - rear suspension (front suspension 1.5 m, rear suspension 1.5 m, total axle spacing = 12-1.5-1.5 = 9 m). The initial total axle spacing = 5.00+6.67+8.33+5.00+6.67 = 31.67 m, which is much higher than the theoretical value, indicating that there is an error in the multi-axle identification. Combined with the wheel track 1.8 m corresponding to the double-axle parallel feature, the adjacent repeated axle signals are merged during correction, and the corrected axle spacing is 3.00 m, 4.00 m, and 2.00 m (total 9.00 m), which is consistent with the theoretical total axle spacing. The comparison and correction parameters of the axle spacing are obtained, ensuring that the axle spacing meets the structural features of the six-axle truck.

[0109] Step S244: Based on the axle type group in the highway traffic volume axle type group classification result and combined with the highway vehicle passing speed, the axle position positioning calculation of each axle group is performed to locate the corresponding highway road position coordinates of each axle group, and the highway axle group axle position coordinate data set is generated; based on the highway axle group axle position coordinate data set, the axle group distribution statistics are performed to analyze and extract the corresponding axle number and distribution arrangement mode in each axle group, and the highway axle group distribution characteristics are obtained;

[0110] In the embodiment of the application, the axle position positioning calculation is performed based on the traffic volume axle type group classification result (six-axle truck belongs to axle type group 6x4) and combined with the vehicle passing speed 60.0 km / h. Taking X = 10 m as the starting point, the first axle passing time is 1620000000.123 s, the second axle passing time = 1620000000.123 s + 3.00 m ÷ 16.67 m / s = 1620000000.303 s, and the corresponding X coordinate = 10 m + 3.00 m = 13.00 m. Similarly, the third axle X = 17.00 m, the fourth axle X = 19.00 m, the fifth axle X = 22.00 m, and the sixth axle X = 26.00 m, and the axle group axle position coordinate data set is generated: (10.00 m, 2.8 m), (13.00 m, 2.8 m), (17.00 m, 2.8 m), (19.00 m, 2.8 m), (22.00 m, 2.8 m), (26.00 m, 2.8 m). The axle group distribution statistics show that this axle type group contains 6 axles, arranged as front 2 axles, middle 2 axles, and rear 2 axles, and the axle group distribution characteristics are "2+2+2" arrangement mode.

[0111] Step S245: Based on the highway axle spacing comparison and correction parameters and the highway axle group distribution characteristics, the vehicle axle system characteristics are integrated to generate the highway vehicle axle system characteristic data containing the corresponding axle number, axle position, and axle spacing of each axle group.

[0112] In the embodiment of the application, the shaft system characteristics are integrated by modifying parameters (3.00 m, 4.00 m, 2.00 m) based on the shaft spacing contrast and the shaft group distribution characteristics (“2+2+2” arrangement). The first shaft group (2 shafts) has a shaft spacing of 3.00 m and shaft coordinates of (10.00 m, 2.8 m) and (13.00 m, 2.8 m); the second shaft group (2 shafts) has a shaft spacing of 4.00 m and shaft coordinates of (13.00 m, 2.8 m) and (17.00 m, 2.8 m); and the third shaft group (2 shafts) has a shaft spacing of 2.00 m and shaft coordinates of (19.00 m, 2.8 m), (22.00 m, 2.8 m), (22.00 m, 2.8 m), and (26.00 m, 2.8 m). After integration, the highway vehicle shaft system characteristic data is generated, including the number of shafts in each shaft group (all 2 shafts), shaft coordinates (accurate to 0.01 m), and shaft spacing (accurate to 0.01 m), which fully reflects the shaft system structure of a six-axle truck and provides precise vehicle structure parameters for dynamic axle load detection.

[0113] Further, step S25 includes the following steps:

[0114] Step S251: signal attenuation characteristic analysis is performed on the road surface response signals corresponding to the road surface strain and road surface vibration in the standardized axle load detection data set to calculate the amplitude attenuation rate and phase shift of the road surface response signals at different propagation distances, and generate highway road surface response attenuation characteristic parameters;

[0115] In the embodiment of the application, the road surface strain signal (1200 με) and the vibration signal (8g) when the six-axle truck passes through are selected as the original signals by performing attenuation characteristic analysis on the road surface response signals in the standardized axle load detection data set. Sensors are arranged at a propagation distance of 0 m (signal source), 5 m, 10 m, 15 m, and 20 m to collect signals, and the amplitude attenuation rate and phase shift are calculated: at a distance of 5 m, the strain amplitude is reduced to 1020 με (attenuation rate 15%), and the phase shift is 0.02π; at 10 m, the strain amplitude is 840 με (attenuation rate 30%), and the phase shift is 0.05π; at 15 m, the strain amplitude is 720 με (attenuation rate 40%), and the phase shift is 0.08π; at 20 m, the strain amplitude is 600 με (attenuation rate 50%), and the phase shift is 0.1π. The vibration signal has an amplitude of 6.8g (attenuation rate 15%) at 5 m, 5.6g (attenuation rate 30%) at 10 m, 4.8g (attenuation rate 40%) at 15 m, and 4g (attenuation rate 50%) at 20 m, and the phase shift is consistent with that of the strain signal. The generated highway road surface response attenuation characteristic parameters include the attenuation rate (increasing by 15% every 5 m) and the phase shift (increasing by 0.03π every 5 m) corresponding to different distances, which clearly shows the signal propagation law.

[0116] Step S252: determining the road surface response signal collection points corresponding to each axle group based on the axle position coordinate information in the highway vehicle axle system characteristic data, and performing axle position-attenuation correlation analysis based on the road surface response signal collection points corresponding to each axle group in combination with the highway road surface response attenuation characteristic parameters to obtain the highway vehicle axle position-response attenuation correlation data;

[0117] In the embodiment of the present application, the road surface response signal collection points corresponding to each axle group are determined based on the axle position coordinates (10.00 m, 2.8 m), (13.00 m, 2.8 m), etc. in the vehicle axle system characteristic data, the first axle corresponds to the sensor at X = 10 m, the second axle corresponds to the sensor at X = 13 m, and so on. In combination with the road surface response attenuation characteristic parameters, the strain signal of the first axle at X = 10 m is calculated as 1200 με, which attenuates to 720 με (attenuation rate 40%) at X = 15 m; the strain signal of the second axle at X = 13 m is 1100 με, which attenuates to 660 με (attenuation rate 40%) at X = 18 m. The axle position is correlated with the attenuation rate through coordinate mapping to obtain the axle position-response attenuation correlation data: the first axle (10.00 m) corresponds to an attenuation rate of 0% (at the collection point), the second axle (13.00 m) corresponds to an attenuation rate of 15% (propagated for 5 m), and the third axle (17.00 m) corresponds to an attenuation rate of 30% (propagated for 10 m), ensuring that the response signal attenuation value of each axle position can be checked.

[0118] Step S253: performing load-response nonlinear mapping analysis based on the number of axles and the inter-axle distance in the highway vehicle axle system characteristic data in combination with the response attenuation characteristic parameters corresponding to the highway vehicle axle position-response attenuation correlation data to obtain the nonlinear mapping relationship between the highway vehicle axle load and the road surface response signal attenuation;

[0119] In the embodiment of the present application, the load-response nonlinear mapping analysis is performed based on the axle system characteristic data (6 axles, inter-axle distance 3.00 m, 4.00 m, 2.00 m) and the axle position-response attenuation correlation data. The first axle load of 60 kN corresponds to a strain of 1200 με (attenuation rate 0%) at X = 10 m, the second axle load of 55 kN corresponds to a strain of 1100 με (attenuation rate 15%) at X = 13 m, the third axle load of 65 kN corresponds to a strain of 910 με (attenuation rate 30%) at X = 17 m, the fourth axle load of 62 kN corresponds to a strain of 868 με (attenuation rate 35%) at X = 19 m, the fifth axle load of 58 kN corresponds to a strain of 812 με (attenuation rate 40%) at X = 22 m, and the sixth axle load of 53 kN corresponds to a strain of 742 με (attenuation rate 45%) at X = 26 m. The mapping relationship is obtained through nonlinear fitting (using a cubic polynomial): axle load (kN) = 0.05 × strain (με) + 0.0001 × strain (με) 2Decay rate (%) which has a nonlinear relationship with the axle load and the signal decay, and the error is less than 2% in verification.

[0120] Step S254: Based on the nonlinear mapping relationship between the highway vehicle axle load and the pavement response signal decay, and combined with the highway vehicle axle system characteristic data and the highway pavement response decay characteristic parameters, the axle load-response mapping function is constructed to generate the highway vehicle axle load-response mapping function;

[0121] In the embodiment of the application, the axle load-response mapping function is constructed based on the nonlinear mapping relationship, the input parameters are the strain signal amplitude and the decay rate, and the output is the axle load. The function expression is: axle load (kN) = (strain amplitude x 0.05) ÷ (1-decay rate / 100) + (strain amplitude2 x 0.00002) ÷ (1-decay rate / 100) 2 , wherein 0.05 is a proportional coefficient determined based on the basic linear relationship between the axle load and the strain. By collecting 100 groups of sample data of known axle load (30-150 kN) and corresponding strain signal (600-3000 με), the average ratio of the axle load (kN) to the strain (με) is calculated to be 0.05 (for example, 60 kN corresponds to 1200 με, 60 ÷ 1200 = 0.05; 55 kN corresponds to 1100 με, 55 ÷ 1100 = 0.05). The value reflects the reference value of the axle load corresponding to the unit strain. The stability is verified by linear regression analysis (R 2 = 0.98). 100 is a percentage conversion coefficient of the decay rate. Because the decay rate is expressed in percentage (such as 15%) in calculation, it needs to be converted into decimal form (0.15) for operation. Therefore, by dividing by 100, the unit normalization (15% ÷ 100 = 0.15) is realized to ensure that the correction effect of the decay rate on the axle load (such as 15% decay, the correction coefficient is 1 ÷ (1-0.15) = 1.176) conforms to the signal propagation decay law. 0.00002 is the coefficient of the nonlinear correction term, which is derived from high-order term fitting. The strain square term is added in the basic linear relationship to compensate for the nonlinear error. The three polynomial fitting (axle load = k1 x strain + k2 x strain2 ÷ decay rate) is performed on 100 groups of sample data. The k2 is calculated to be 0.00002 by the least square method (for example, when the strain = 1200 με and the decay rate = 0%, 0.00002 x 1200 2= 28.8, which is consistent with the compensation value of the measured axle load deviation), residual error analysis (average residual error <1.5 kN) confirms that the coefficient can effectively reduce the nonlinear error, and the function expression composed of the three is verified by 50 new samples, and the axle load calculation error is reduced from 5.2% to 1.8% by the simple linear relationship, which meets the dynamic detection accuracy requirement. For the first axis verification: strain 1200με, attenuation rate 0%, calculated 60kN; the second axis strain is 1100με, the attenuation rate is 15%, and the calculation is 55kN; the third axis strain is 910με, the attenuation rate is 30%, and the calculation is 65kN, which is consistent with the actual axle load. The attenuation rate correction term is introduced in the function to ensure that the signals of different propagation distances can accurately calculate the axle load, and the average error is 1.8% after 100 group data verification, and the formal highway vehicle axle load-response mapping function is generated.

[0122] Step S255: Load-response mapping estimation based on the highway vehicle axle load-response mapping function to obtain the preliminary estimation value of the highway traffic volume axle load.

[0123] In the embodiment of the application, by load-response mapping estimation based on the axle load-response mapping function, when a certain six-axle truck passes, the strain signals and attenuation rates corresponding to each axle are: first axle 1200με(0%), second axle 1100με(15%), third axle 910με(30%), fourth axle 868με(35%), fifth axle 812με(40%), and sixth axle 742με(45%). Substituting the function calculation: first axle = 1200×0.05÷1+(1200 2 ×0.00002)÷1 2 = 60+28.8 = 88.8kN; second axle = 1100×0.05÷0.85+(1100 2 ×0.00002)÷0.85 2 ≈64.71+33.86 = 98.57kN; third axle = 910×0.05÷0.7+(910 2 ×0.00002)÷0.7 2 ≈65+33.86 = 98.86kN; the calculation results of subsequent axles are 95.24kN, 92.11kN, and 87.65kN in turn. The preliminary estimation value of the highway traffic volume axle load generated by retaining two decimal places fully reflects the load size of each axle.

[0124] Further, step 255 includes the following steps:

[0125] By arranging environmental sensors on the corresponding highway pavement to synchronously collect corresponding temperature, humidity, pavement dry and wet state, and rainfall data, a highway pavement environmental parameter data set is generated;

[0126] In the embodiment of the present application, by arranging a group of environmental sensors every 50 m along the lane longitudinally on the highway pavement, each group contains 4 types of sensors: temperature sensor (range -40℃ to 80℃, accuracy ±0.5℃) buried 2 cm below the surface layer of the pavement, humidity sensor (range 0% to 100% RH, accuracy ±3% RH) installed 1 m above the pavement at the roadside, pavement dry-wet state sensor (using infrared reflection principle, resolution 0.1%) attached to the pavement surface, and rainfall sensor (range 0 mm / h to 50 mm / h, accuracy ±0.2 mm / h) fixed to the roadside vertical pole 3 m high. The sampling frequency of all sensors is 1 time / minute, and the time stamp is synchronized with the axle load detection system (error ±1 s). The data collected in a certain period of time are: temperature 25℃, humidity 60% RH, pavement dry-wet state dry (moisture content 0.5%), and rainfall 0 mm / h. After continuous collection for 24 hours, the highway pavement environmental parameter data set containing time stamp, temperature, humidity, pavement state code (dry = 1, wet = 2, water accumulation = 3), and rainfall is generated, and one set of average data is stored every hour to ensure that the environmental parameters and axle load detection data correspond in the time dimension.

[0127] Preferably, the environmental-axle load characteristic influence analysis is performed on the highway vehicle axle system characteristic data based on each environmental parameter in the highway pavement environmental parameter data set to generate a highway environmental-axle load characteristic influence distribution curve.

[0128] In the embodiment of the present application, the environmental-axle load characteristic influence analysis is performed based on the highway pavement environmental parameter data set and vehicle axle system characteristic data. The strain response 1200 με corresponding to the inter-axle distance 3.00 m and axle position coordinates (10.00 m, 2.8 m) of a six-axle truck is selected as the reference value, and the response changes under different environmental conditions are compared: the strain increases to 1260 με (amplitude 5%) at temperature 35℃, decreases to 1140 με (amplitude 5%) at temperature 15℃, decreases to 1080 με (amplitude 10%) when the pavement is wet (moisture content 10%), and decreases to 960 με (amplitude 20%) when the rainfall is 5 mm / h. With the environmental parameters as the horizontal axis (temperature 15-35℃, humidity 40%-80% RH) and the strain change rate as the vertical axis (-20% to +5%), the environmental-axle load characteristic influence distribution curve is drawn, and the curve shows that the strain increases by 3% for every 10℃ increase in temperature, the strain decreases by 2% for every 20% increase in humidity, and the strain decreases by 20% when the pavement changes from dry to water accumulation state, thus clarifying the quantitative relationship between each environmental parameter and the dynamic response of the axle load.

[0129] Preferably, the corresponding highway axle load influence correction factors under different environmental condition combinations are calculated based on the highway environment-axle load characteristic influence distribution curve, and the load-response mapping correction estimation is performed based on the highway axle load influence correction factors and the highway vehicle axle load-response mapping function, so as to obtain the preliminary estimation value of the highway traffic volume axle load.

[0130] In the embodiment of the present application, the axle load influence correction factors under different environmental condition combinations are calculated based on the environment-axle load characteristic influence distribution curve: the temperature 25℃ (reference temperature) correction factor is 1.00, 35℃ is 1.05 (1+5%), and 15℃ is 0.95 (1-5%); the road surface dry correction factor is 1.00, 0.90 (1-10%) when wet, and 0.80 (1-20%) when waterlogged; the rainfall 0mm / h correction factor is 1.00, and 0.80 when 5mm / h. The strain of a certain six-axle truck under the temperature 35℃ and the road surface wet environment is 1080με, and the comprehensive correction factor = 1.05 x 0.90 = 0.945. The highway vehicle axle load-response mapping function is axle load (kN) = strain (με) x 0.05, the reference state axle load = 1200 x 0.05 = 60kN, and the preliminary estimation value of the corrected axle load = 1080 x 0.05 ÷ 0.945 ≈ 57.14kN, with two decimal places reserved. The same method is used to calculate the preliminary estimation value of each axle load, which are 57.14kN, 53.57kN, 61.90kN, 59.52kN, 55.95kN, and 52.38kN respectively, so as to form the complete axle load estimation result.

[0131] Further, the step S3 comprises the following steps:

[0132] Step S31: acquiring a vehicle type database, which comprises the standard axle load range, typical axle group configuration and axle weight distribution corresponding to different vehicle types;

[0133] In the embodiment of the present application, the vehicle type database contains the axle load characteristic parameters of 10 common vehicle types, which are stored in the structure of "vehicle type-axle type group-standard axle load range-axle group configuration-axle load distribution". The 6x4 axle type six-axle truck entry is: the standard axle load range of the first axle is 65-75kN, the second axle is 65-75kN, the third axle is 95-105kN, the fourth axle is 95-105kN, the fifth axle is 110-120kN, and the sixth axle is 110-120kN; the typical axle group configuration is 3 groups of double axles (2+2+2); the axle load distribution coefficients of the first axle, the second axle, the third axle, the fourth axle, the fifth axle and the sixth axle are 0.12, 0.12, 0.17, 0.17, 0.18 and 0.18 (total 1.0) respectively. The database adopts a structured table form, each parameter is accurate to 0.1kN or 0.01 coefficient, and is established by statistically measuring 500 vehicles of the same type, so as to ensure that the standard value covers 95% of the actual vehicle axle load range and provides an authoritative reference basis for subsequent verification.

[0134] Step S32: vehicle axle load verification is performed on the standardized axle load detection data set based on the preliminary estimation value of the highway traffic volume axle load and the vehicle type database, to generate the verified highway traffic volume axle load data;

[0135] In the embodiment of the present application, verification is performed based on the preliminary estimation value (60kN, 64.71kN, 65.00kN, 66.77kN, 67.67kN, 67.45kN) of the highway traffic volume axle load and the vehicle type database. The first axle 60kN is lower than the lower limit 65kN of the standard range, with a deviation of 5kN; the second axle 64.71kN is close to the lower limit 65kN, with a deviation of 0.29kN; the third to sixth axles are all lower than the lower limit of the standard range, with a deviation of 30-47.53kN. The axle group balancing correction method is adopted, and the axle load difference of the double axle group is forced to be ≤2kN: the third and fourth axles are corrected to 98kN and 102kN (both within the range of 95-105kN), and the fifth and sixth axles are corrected to 113kN and 117kN (both within the range of 110-120kN); the first and second axles remain unchanged (the deviation is within the allowable error ±5kN), to generate the verified highway traffic volume axle load data: 60kN, 64.71kN, 98kN, 102kN, 113kN, 117kN, all of which are kept to two decimal places, which not only meets the database standard, but also keeps a reasonable error range.

[0136] Step S33: time period distribution statistics are performed on the verified highway traffic volume axle load data according to a preset time period cycle, to statistically calculate the number of vehicles, the total axle load and the proportion corresponding to different axle load intervals in each time period, to obtain the highway time period axle load distribution data;

[0137] In the embodiment of the present application, the axle load data after verification is statistically analyzed by time period according to preset time period cycle (morning peak 7:00-9:00, flat peak 9:00-17:00, evening peak 17:00-19:00). 100 six-axle trucks are counted in the morning peak: 200 axles in the 50-70kN range (first and second axles), total axle load = (60+64.71) x 100 = 12471kN, accounting for 22%; 70-100kN range has no data; 400 axles in the 100-120kN range (third to sixth axles), total axle load = (98+102+113+117) x 100 = 43000kN, accounting for 78%. 80 trucks are counted in the flat peak period: total axle load in the 50-70kN range is 9976.8kN, accounting for 22%; total in the 100-120kN range is 34400kN, accounting for 78%. 120 trucks are counted in the evening peak: total in the 50-70kN range is 14965.2kN, accounting for 22%; total in the 100-120kN range is 51600kN, accounting for 78%. The generated highway axle load distribution data by time period includes the number of axles, total, and proportion in each time period, accurately reflecting the axle load distribution law in different time periods.

[0138] Step S34: Obtain the highway pavement structure parameters, including the thickness, material type, compressive strength, flexural tensile strength, and fatigue characteristic parameters of each structure layer of the pavement;

[0139] In the embodiment of the present application, the obtained highway pavement structure parameters are determined by drilling and laboratory testing: the surface layer is asphalt concrete, divided into 3 layers with a total thickness of 18cm (4cm upper layer, 6cm middle layer, and 8cm lower layer), 25℃ compressive strength 4.5MPa, 15℃ flexural tensile strength 1.0MPa, and fatigue life coefficient 5.0 x 10 6 6 The base layer is cement stabilized gravel, 36cm thick, 7d compressive strength 3.0MPa, flexural tensile strength 0.8MPa, and fatigue life coefficient 3.0 x 10 6 All parameters are averaged by 3 parallel tests, thickness is measured to 0.1cm, strength is detected to 0.1MPa, and fatigue characteristics are determined by indoor small beam bending fatigue test (loading frequency 10Hz, stress ratio 0.5), ensuring that the data truly reflect the pavement structure performance.

[0140] Step S35: Based on the highway pavement structure parameters and the highway axle load distribution data by time period, the pavement axle load cumulative damage is evaluated, and the highway pavement axle load cumulative damage value is obtained.

[0141] In the embodiment of the present application, the cumulative damage is evaluated based on the pavement structure parameters and the time-segmented axle load distribution data. The equivalent axle times of the morning peak, the flat peak and the evening peak are calculated by the Miner theory: the equivalent axle times of the morning peak is 354.65, the surface layer damage is 354.65 / 5.0x10 6 =7.09x10 -5 , and the base layer damage is 354.65 / 3.0x10 6 =1.18x10 -4 ; the equivalent axle times of the flat peak is 210.32, the surface layer damage is 4.21x10 -5 , and the base layer damage is 7.01x10 -5 ; the equivalent axle times of the evening peak is 380.17, the surface layer damage is 7.60x10 -5 , and the base layer damage is 1.27x10 -4 . The cumulative damage is calculated according to the time sequence: the total surface layer damage is 7.09x10 -5 +4.21x10 -5 +7.60x10 -5 =1.89x10 -4 , and the total base layer damage is 1.18x10 -4 +7.01x10 -5 +1.27x10 -4 =3.15x10 -4 . The sum of the total surface layer damage and the total base layer damage is the cumulative damage value of the highway pavement, which is 5.04x10 -4 . The cumulative damage value is used to evaluate the remaining life of the pavement, and the maintenance and repair is needed when the cumulative damage reaches 1.0.

[0142] Further, the step S32 comprises the following steps:

[0143] The standard axle load parameters of the matched axle group of the corresponding vehicle type are obtained based on the vehicle type database, and the deviation ratio of the highway traffic volume axle load is calculated based on the preliminary estimated value and the standard axle load parameters, so as to obtain the deviation ratio of the highway traffic volume axle load;

[0144] In the embodiment of the present application, the standard axle load parameters of the 6x4 axle type six axle truck are obtained by calling from the vehicle type database: first axle 70kN, second axle 70kN, third axle 100kN, fourth axle 100kN, fifth axle 115kN, and sixth axle 115kN. The preliminary estimated values of the highway traffic volume axle load (60kN, 64.71kN, 65.00kN, 66.77kN, 67.67kN, and 67.45kN) are compared with the standard axle load parameters. The first axle deviation rate is (60-70) / 70x100%=-14.29%, the second axle is (64.71-70) / 70x100%=-7.56%, the third axle is (65.00-100) / 100x100%=-35.00%, the fourth axle is (66.77-100) / 100x100%=-33.23%, the fifth axle is (67.67-115) / 115x100%=-41.16%, and the sixth axle is (67.45-115) / 115x100%=-41.35%. All the deviation rates are kept to two decimal places to generate the highway traffic volume axle load deviation rate, which clearly shows the deviation degree of the estimated values of each axle from the standard values.

[0145] Preferably, the comparison between the highway traffic volume axle load deviation rate and the preset axle load deviation range is used to judge the standardized axle load detection data set corresponding to the highway traffic volume axle load deviation rate exceeding the preset axle load deviation range, so as to obtain the highway axle load data that does not conform to the deviation rate.

[0146] In the embodiment of the present application, the preset axle load deviation range is ±20%, and the deviation rates of the axles are compared with the range. The first axle deviation rate is-14.29% (within the range), the second axle is-7.56% (within the range), the third axle is-35.00% (exceeding the range), the fourth axle is-33.23% (exceeding the range), the fifth axle is-41.16% (exceeding the range), and the sixth axle is-41.35% (exceeding the range). The axle load data whose deviation rate exceeds the preset range is screened out: the third axle 65.00kN, the fourth axle 66.77kN, the fifth axle 67.67kN, and the sixth axle 67.45kN, and the corresponding standardized axle load detection data set contains the strain signals (910με, 868με, 812με, and 742με) of these axles, the axle position coordinates (17.00m, 19.00m, 22.00m, and 26.00m), and the attenuation rates (30%, 35%, 40%, and 45%), so as to obtain the highway axle load data that does not conform to the deviation rate, which provides a clear object for subsequent correction.

[0147] Preferably, the wheelbase and tire quantity constraints corresponding to the vehicle type are obtained, and the highway axle load data that does not meet the deviation rate is corrected and adjusted based on the wheelbase and tire quantity constraints corresponding to the vehicle type, and the corrected highway axle load data is integrated with the highway axle load data that meets the deviation rate to generate the corrected highway traffic volume axle load data.

[0148] In the embodiment of the present application, the wheelbase constraint condition (the distance between adjacent axle groups is 3.00-5.00 m) and the tire quantity constraint condition (each axle group has double tires, and the load distribution coefficient is 1.0) of the 6×4 axle type six-axle truck are obtained. The axle load data that does not meet the deviation rate is corrected as follows: the third axle and the fourth axle are parallel double axles, and the standard axle load is 100 kN, based on the double axle load balancing constraint, the third axle is corrected to 98 kN, and the fourth axle is corrected to 102 kN (the total is 200 kN, which is consistent with the standard total of 200 kN); the fifth axle and the sixth axle are parallel double axles, and the standard axle load is 115 kN, the fifth axle is corrected to 113 kN, and the sixth axle is corrected to 117 kN (the total is 230 kN, which is consistent with the standard total of 230 kN). The corrected data is verified as follows: the third axle is 98 kN (the deviation rate is -2.00%), the fourth axle is 102 kN (the deviation rate is +2.00%), the fifth axle is 113 kN (the deviation rate is -1.74%), and the sixth axle is 117 kN (the deviation rate is +1.74%), which are all within the preset range. The corrected data is integrated with the first axle 60 kN and the second axle 64.71 kN that meet the deviation rate to generate the corrected highway traffic volume axle load data, which ensures that all axle loads meet the vehicle structure constraint and are within the deviation allowed range.

[0149] Further, the step S35 comprises the following steps:

[0150] Step S351: based on the material type and fatigue characteristic parameters in the highway pavement structure parameters, axle load conversion matching analysis is performed to determine the equivalent axle conversion coefficients corresponding to different axle loads, and a highway fatigue axle load equivalent conversion table is generated;

[0151] In the embodiment of the present application, based on the highway pavement structure parameters (asphalt concrete surface layer, compressive strength 4.5 MPa, fatigue life coefficient 5.0×10 6 times; cement stabilized gravel base, compressive strength 3.0 MPa, fatigue life coefficient 3.0×10 6 times), axle load conversion matching analysis is performed, the modified AASHTO axle load conversion formula is used, and 100 kN single axle double wheel group is used as the standard axle load to calculate the equivalent axle conversion coefficients corresponding to different axle loads: The generated highway fatigue axle load equivalent conversion table is divided according to axle load intervals (50-70kN, 70-100kN, 100-120kN), and the conversion coefficients corresponding to each interval are clearly defined to ensure the accurate quantitative relationship between the axle load and the equivalent axle times.

[0152] Step S352: Based on the highway fatigue axle load equivalent conversion table, the equivalent axle times corresponding to different axle load intervals in each time period are calculated by combining the highway time-period axle load distribution data, to generate highway time-period equivalent axle times data.

[0153] In the embodiment of the present application, the equivalent axle times in each time period are calculated based on the highway fatigue axle load equivalent conversion table and the time-period axle load distribution data (morning peak 7:00-9:00, flat peak 9:00-17:00, evening peak 17:00-19:00). In the morning peak period: 60kN axle appears 120 times, 70kN axle appears 150 times, 98kN axle appears 80 times, 102kN axle appears 70 times, 113kN axle appears 50 times, and 117kN axle appears 40 times. The equivalent axle times in each axle load interval are calculated: 60kN axle = 120x0.1296 = 15.55 times, 70kN axle = 150x0.2401 = 36.02 times, 98kN axle = 80x0.8852 = 70.82 times, 102kN axle = 70x1.0824 = 75.77 times, 113kN axle = 50x1.6305 = 81.53 times, 117kN axle = 40x1.8739 = 74.96 times, and the total equivalent axle times = 15.55+36.02+70.82+75.77+81.53+74.96 = 354.65 times. The total equivalent axle times in the flat peak period is 210.32 times, and the total equivalent axle times in the evening peak period is 380.17 times. The highway time-period equivalent axle times data is generated, and the two decimal places are retained to reflect the differences in the fatigue influence of axle loads on the pavement in different time periods.

[0154] Step S353: Based on the thickness, compressive strength and flexural tensile strength of each structural layer of the pavement in the highway pavement structure parameters, a pavement axle load damage evaluation model is constructed, and the highway time-period equivalent axle times data is input into the pavement axle load damage evaluation model to calculate the cumulative damage amount of each structural layer of the pavement in time sequence, and the cumulative damage amount of each structural layer of the pavement is summed to obtain the highway pavement axle load cumulative damage value.

[0155] In the embodiment of the present application, the pavement axle load damage evaluation model is constructed based on the pavement structure parameters (surface layer thickness 18 cm, compressive strength 4.5 MPa, flexural tensile strength 1.0 MPa; base layer thickness 36 cm, compressive strength 3.0 MPa, flexural tensile strength 0.8 MPa), the model adopts the Miner linear cumulative damage theory, and the damage coefficient = equivalent axle times / fatigue life coefficient. The equivalent axle times data in different time periods are input into the model: early morning peak surface layer damage = 354.65 / 5.0 x 10 6 = 7.09 x 10 -5 , base layer damage = 354.65 / 3.0 x 10 6 = 1.18 x 10 -4 ; flat peak surface layer damage = 210.32 / 5.0 x 10 6 = 4.21 x 10 -5 , base layer damage = 210.32 / 3.0 x 10 6 = 7.01 x 10 -5 ; late morning peak surface layer damage = 380.17 / 5.0 x 10 6 = 7.60 x 10 -5 , base layer damage = 380.17 / 3.0 x 10 6 = 1.27 x 10 -4 . The total damage of the surface layer = 7.09 x 10 -5 + 4.21 x 10 -5 + 7.60 x 10 -5 = 1.89 x 10 -4 , the total damage of the base layer = 1.18 x 10 -4 + 7.01 x 10 -5 + 1.27 x 10 -4 = 3.15 x 10 -4 . The cumulative damage sum is obtained: the cumulative damage value of the highway pavement axle load = 1.89 x 10 -4 + 3.15 x 10 -4 = 5.04 x 10 -4 , which is used to evaluate the fatigue damage degree of the pavement.

[0156] Further, step S4 comprises the following steps:

[0157] Step S41: obtaining the number and proportion of highway vehicle axle loads corresponding to different axle load intervals and the cumulative damage value of the pavement axle load in each time period through the highway time period axle load distribution data and the highway pavement axle load cumulative damage value;

[0158] In the embodiment of the application, by extracting the information of each period from the highway time-sharing axle load distribution data and the cumulative damage value of the road surface axle load, the early peak is 7:00-9:00, the number of vehicle axle loads in the 50-70kN interval is 200, the proportion is 22%, and the corresponding cumulative damage value of the road surface is 1.89x10 -5 (cover layer) + 3.15x10 -5 (base layer) = 5.04x10 -5 ; the number of axle loads in the 100-120kN interval is 400, the proportion is 78%, and the damage value is 1.70x10 -4 (cover layer) + 8.65x10 -5 (base layer) = 2.565x10 -4 . The flat peak is 9:00-17:00, the number of axle loads in the 50-70kN interval is 160, the proportion is 22%, and the damage value is 1.43x10 -5 + 2.42x10 -5 = 3.85x10 -5 ; the number of axle loads in the 100-120kN interval is 320, the proportion is 78%, and the damage value is 1.31x10 -4 + 6.68x10 -5 = 1.978x10 -4 . The late peak is 17:00-19:00, the number of axle loads in the 50-70kN interval is 240, the proportion is 22%, and the damage value is 2.15x10 -5 + 3.63x10 -5 = 5.78x10 -5 ; the number of axle loads in the 100-120kN interval is 480, the proportion is 78%, and the damage value is 2.05x10 -4 + 1.03x10 -4 = 3.08x10 -4 . All data are classified and arranged according to the axle load interval and the period, and the corresponding relationship between the number of axle loads in each interval, the proportion and the damage value is clear.

[0159] Step S42: performing axle-damage correlation analysis based on the number and proportion of vehicle axle loads in different axle load intervals in each period and the cumulative damage value of the axle load borne by the road surface, to analyze and identify the correlation and corresponding relationship between the vehicle axle load distribution and the cumulative damage in each period, and generate the axle-cumulative damage correlation characteristics of the highway vehicle;

[0160] In the embodiment of the application, by performing axle-damage correlation analysis based on the period data, the damage contribution degree of different axle load intervals is calculated: the early peak 100-120kN interval accounts for 78%, and the contribution is 2.565x10 -4 damage value (total damage 3.069x10 -4(83.6%); the 50-70kN range accounted for 22%, contributing 5.04×10 -5 (16.4%). The peak load contribution from the 100-120kN range was 1.978×10. -4 (Total 2.363×10) -4 (83.7%), with a contribution of 3.85 × 10 in the 50-70 kN range. -5 (16.3%). The evening peak hour contribution from the 100-120kN range was 3.08 × 10⁻⁶. -4 (Total 3.658×10) -4 (84.2%), with the 50-70kN range contributing 5.78×10. -5 (15.8%). A correlation curve was plotted, with the horizontal axis representing the axle load range and the vertical axis representing the damage percentage. The curve showed that the damage contribution in the 100-120kN range was stable at 83%-85%. The correlation characteristics of axle load and cumulative damage on highways were generated: the larger the axle load and the higher the percentage, the more significant the contribution to pavement damage. Moreover, the damage rate during the evening peak was 1.2 times higher than that during the morning peak.

[0161] Step S43: Obtain real-time axle load detection data of the highway, and perform dynamic high-precision detection and correction of real-time traffic volume axle load based on the highway vehicle axle load-cumulative damage correlation characteristics to generate a dynamic detection report of highway traffic volume axle load.

[0162] In this embodiment of the invention, real-time axle load detection data of highways is obtained: at a certain moment, the number of axle loads in the 100-120kN range suddenly increases by 500 (25% higher than the historical average for the same period), and the number in the 50-70kN range increases by 200. Based on the axle load-cumulative damage correlation characteristics, the real-time data is corrected: the 100-120kN range is corrected using a peak-hour damage coefficient of 1.2, and the damage value is adjusted from the theoretical 2.565 × 10⁻⁶. -4 ×1.25=3.206×10 -4 Adjusted to 3.206×10 -4 ×1.2=3.847×10 -4 The 50-70kN range is corrected at 22%, with a damage value of 5.04×10⁻⁶. -5 The values ​​remain unchanged. The generated dynamic detection report includes the number of real-time axle loads (700), the percentage of each range (71.4% for 100-120kN, 28.6% for 50-70kN), and the corrected damage value of 4.351×10⁻⁶. -4 The deviation rate from the historical average (+25%) ensures that the data reflects the current actual damage trend.

[0163] Step S44: Based on the corresponding road surface bearing axle load cumulative damage value in the highway traffic volume axle load dynamic detection report and the preset road surface design service life threshold, road surface damage comparison and evaluation is performed to obtain the current damage degree of the highway road surface;

[0164] In the embodiment of the present application, the preset road surface design service life threshold is 1.0 (corresponding to a design service life of 15 years), and the current highway road surface axle load cumulative damage value is 5.04x10 -4 (per day). The current damage degree is calculated as follows: daily damage value x 365 days x service life (3 years) = 5.04x10 -4 x 365 x 3 ≈ 0.552. Compared with the threshold value 1.0, the current damage degree is 55.2%, of which the surface layer damage accounts for 37.5% (0.189 / 0.504), and the base layer accounts for 62.5% (0.315 / 0.504). The evaluation result shows that the base layer damage progresses faster and needs to be paid more attention to, and the remaining life is calculated as follows: (1.0-0.552) / (5.04x10 -4 x 365) ≈ 2.4 years, which provides a time basis for maintenance planning.

[0165] Step S45: Based on the current damage degree of the highway road surface and combined with the highway traffic volume axle load dynamic detection report, corresponding vehicle axle load restriction and maintenance timing planning are made, and a highway road surface axle load maintenance scheme including maintenance technology, maintenance range and implementation priority is generated.

[0166] In the embodiment of the present application, based on the current damage degree 55.2% and the dynamic detection report, the vehicle axle load restriction is made as follows: 100-120kN axle load vehicles are restricted by 30% during late peak hours (17:00-19:00), and the speed is reduced from 100km / h to 80km / h (reducing the impact coefficient by 15%). The maintenance scheme includes: emulsified asphalt filling maintenance is used for the base layer (process parameters: asphalt dosage 3.5kg / m 2 , spraying temperature 160℃), and the range is K10+000-K15+000 (damage concentrated section); micro-surfacing treatment is used for the surface layer (stone particle size 3-5mm, oil stone ratio 6.0%), and the range covers the whole detection section. The implementation priority is: base layer maintenance (within 1 month) > axle load restriction (executed immediately) > surface layer maintenance (within 3 months). The scheme clearly specifies the material dosage, construction temperature and acceptance standard of each process, so as to ensure that the base layer damage rate is reduced by 40% after maintenance, and the service life of the surface layer is extended by 2 years.

[0167] The foregoing is considered as illustrative only of the principles of the application. Numerous modifications and changes will readily occur to those skilled in the art, and it is intended to embrace all such modifications and changes that fall within the scope of the application. Accordingly, the application is not to be restricted in scope to the specific embodiments disclosed herein but is to be accorded the full scope that the principles and novel features request appropriately granted.

Claims

1. A method for dynamic high-precision detection of axle load in highway traffic volume, characterized in that, Includes the following steps: Step S1: Collect dynamic response signals and vehicle traffic characteristic data corresponding to the highway road surface through a multi-type sensor array to obtain the original axle load detection data of highway traffic volume; perform spatiotemporal synchronization and noise suppression processing on the original axle load detection data of highway traffic volume to generate a standardized axle load detection dataset. Step S2: Based on the standardized axle load detection dataset, identify vehicle axle type and locate axle position to extract axle group distribution features and axle spacing parameters, and generate highway vehicle axle system feature data; Load-response mapping estimation is performed based on the characteristic data of highway vehicle axle system and the attenuation characteristics corresponding to the road surface response signal to obtain a preliminary estimate of highway traffic volume axle load. Step S3: Obtain the vehicle type database, and perform vehicle axle load verification and time-segmented distribution statistics on the standardized axle load detection dataset based on the preliminary estimate of highway traffic volume axle load and the vehicle type database to obtain highway time-segmented axle load distribution data; obtain highway pavement structure parameters, and perform pavement axle load cumulative damage assessment based on highway pavement structure parameters combined with highway time-segmented axle load distribution data to obtain highway pavement axle load cumulative damage value. Step S4: Perform dynamic high-precision detection based on the time-segmented axle load distribution data of the expressway and the cumulative axle load damage value of the expressway pavement to generate a dynamic detection report of expressway traffic volume axle load; formulate a corresponding expressway pavement axle load maintenance plan based on the dynamic detection report of expressway traffic volume axle load.

2. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: By deploying strain sensors and acceleration sensors at different depths on the highway surface, and deploying a multi-type sensor array consisting of laser profile sensors and video recognition equipment on the side of the highway, the strain sensors collect the dynamic response signal corresponding to the road surface strain when a vehicle passes, the acceleration sensors collect the dynamic response signal corresponding to the road surface vibration, the laser profile sensors collect the vehicle length and wheelbase geometric parameters, and the video recognition equipment collects the vehicle type and travel trajectory, the original axle load detection data of highway traffic volume is obtained. Step S12: Based on the time synchronization module corresponding to the multi-type sensor array, the acquisition timestamps of each sensor are unified to the same clock reference, and the error is controlled within the preset range to generate multi-source data time synchronization parameters. Step S13: Establish the coordinate mapping relationship of each sensor detection point according to the spatial layout of each sensor in the multi-type sensor array, and associate the dynamic response signal in the original axle load detection data of highway traffic volume with the vehicle traffic characteristic data to the same coordinate mapping relationship to generate multi-source data spatial calibration parameters. Step S14: Based on the time synchronization parameters and spatial calibration parameters of multi-source data, the original axle load detection data of highway traffic volume is spatiotemporally synchronized and integrated to generate a spatiotemporally aligned detection dataset of highway axle load containing time and space. Step S15: Perform noise suppression and standardization on the highway axle load spatiotemporal alignment detection dataset. Wavelet threshold denoising is used to remove high-frequency interference noise and standardize the data to generate a standardized axle load detection dataset.

3. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 2, characterized in that, Step S2 includes the following steps: Step S21: Perform peak value statistics on the corresponding dynamic response signals in the standardized axle load detection dataset to statistically analyze the peak occurrence time, peak amplitude, and peak signal spectrum of each peak value of the highway axle load dynamic signal, and generate a highway axle load signal peak value sequence. Step S22: Perform signal peak feature analysis based on the peak sequence of highway axle load signal to obtain the peak time interval, amplitude change rate and spectral energy distribution corresponding to the dynamic signal of highway axle load; Step S23: Based on the peak time interval, amplitude change rate, and spectral energy distribution of the dynamic signal of highway axle load, and combined with the corresponding vehicle type and traffic trajectory in the standardized axle load detection dataset, perform vehicle axle type identification analysis to identify and distinguish vehicle axle type groups corresponding to single axle, parallel dual axle, and parallel triple axle, and obtain the highway traffic volume axle type group classification results. Step S24: Based on the classification results of highway traffic volume axle type groups and combined with the peak time interval and amplitude change rate corresponding to the highway axle load dynamic signal, perform vehicle axle position positioning statistics on the standardized axle load detection dataset to generate highway vehicle axle system feature data. Step S25: Based on the axle system characteristic data of highway vehicles and combined with the attenuation characteristics of the road surface response signal, perform load-response mapping estimation to obtain a preliminary estimate of the axle load of highway traffic volume.

4. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 3, characterized in that, Step S24 includes the following steps: Step S241: Obtain the corresponding highway vehicle speed by matching the vehicle type and travel trajectory within the standardized axle load detection dataset; Step S242: Based on the peak time interval and amplitude change rate corresponding to the dynamic signal of highway axle load, and combined with the speed of highway vehicles, the vehicle axle spacing is converted to obtain the initial parameters of highway vehicle axle spacing; Step S243: Based on the vehicle length and wheelbase geometric parameters corresponding to the standardized axle load detection dataset, perform axle spacing comparison and correction calculation on the initial parameters of highway vehicle axle spacing to obtain highway axle spacing comparison and correction parameters; Step S244: Based on the classification results of axle type groups of highway traffic volume and combined with the highway vehicle speed, calculate the axle position of each axle group to locate the corresponding highway pavement coordinates and generate a dataset of axle position coordinates for each axle group; perform axle group distribution statistics based on the dataset of axle position coordinates for each axle group to analyze and extract the number and distribution of axles in each axle group, and obtain the distribution characteristics of each axle group of the highway. Step S245: Based on the highway axle spacing comparison correction parameters and the distribution characteristics of each axle group on the highway, integrate the vehicle axle system features to generate highway vehicle axle system feature data containing the number of axles, axle position and axle spacing of each axle group.

5. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 3, characterized in that, Step S25 includes the following steps: Step S251: Perform signal attenuation characteristic analysis on the pavement response signals corresponding to pavement strain and pavement vibration in the standardized axle load test dataset to calculate the amplitude attenuation rate and phase shift of the pavement response signal at different propagation distances, and generate highway pavement response attenuation characteristic parameters. Step S252: Based on the axle coordinate information in the highway vehicle axle system feature data, determine the road surface response signal acquisition points corresponding to each axle group, and perform axle position-attenuation correlation analysis based on the road surface response signal acquisition points corresponding to each axle group and the highway road surface response attenuation feature parameters to obtain highway vehicle axle position-response attenuation correlation data. Step S253: Based on the number of axles and axle spacing in the axle system characteristic data of highway vehicles and combined with the response attenuation characteristic parameters corresponding to the axle position-response attenuation correlation data of highway vehicles, perform load-response nonlinear mapping analysis to obtain the nonlinear mapping relationship between highway vehicle axle load and road surface response signal attenuation. Step S254: Based on the nonlinear mapping relationship between highway vehicle axle load and road surface response signal attenuation, and combined with highway vehicle axle system characteristic data and highway road surface response attenuation characteristic parameters, construct the axle load-response mapping function to generate the highway vehicle axle load-response mapping function; Step S255: Perform load-response mapping estimation based on the highway vehicle axle load-response mapping function to obtain a preliminary estimate of highway traffic volume axle load.

6. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 5, characterized in that, Step 255 includes the following steps: By deploying environmental sensors on the corresponding highway surface to synchronously collect corresponding temperature, humidity, road surface dryness and wetness status and rainfall data, a highway surface environmental parameter dataset is generated. Based on the environmental parameters in the highway pavement environmental parameter dataset, an environmental-axle load characteristic influence analysis is performed on the highway vehicle axle system characteristic data to generate the highway environmental-axle load characteristic influence distribution curve. Based on the distribution curve of the influence of highway environment-axle load characteristics, the corresponding highway axle load influence correction factor under different environmental condition combinations is calculated. Based on the highway axle load influence correction factor and the highway vehicle axle load-response mapping function, the load-response mapping correction estimation is performed to obtain the preliminary estimate of highway traffic volume axle load.

7. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 1, characterized in that, Step S3 includes the following steps: Step S31: Obtain the vehicle type database, which includes the standard axle load range, typical axle group configuration and axle weight distribution for different vehicle types; Step S32: Based on the preliminary estimate of highway traffic volume axle load and the vehicle type database, perform vehicle axle load verification on the standardized axle load detection dataset to generate verified highway traffic volume axle load data; Step S33: Perform time-segmented distribution statistics on the verified highway traffic volume axle load data according to the preset time period, so as to calculate the number of vehicles, total axle load and proportion corresponding to different axle load intervals in each time period, so as to obtain the highway time-segmented axle load distribution data. Step S34: Obtain the structural parameters of the highway pavement, including the thickness, material type, compressive strength, flexural tensile strength and fatigue characteristics of each pavement structural layer; Step S35: Based on the highway pavement structure parameters and the highway time-segmented axle load distribution data, conduct a pavement axle load cumulative damage assessment to obtain the highway pavement axle load cumulative damage value.

8. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 7, characterized in that, Step S32 includes the following steps: The standard axle load parameters for matching the corresponding vehicle type axle group are obtained from the vehicle type database, and the deviation is calculated by comparing the preliminary estimated value of highway traffic volume axle load with the standard axle load parameters to obtain the highway traffic volume axle load deviation rate. The standard axle load test dataset corresponding to the axle load deviation rate of highway traffic volume is selected by comparing and judging between the axle load deviation rate of highway traffic volume and the preset axle load deviation range, so as to obtain the highway axle load data with deviation rate that does not meet the requirements. Obtain the wheelbase and tire quantity constraints corresponding to the vehicle type, and perform constraint correction and adjustment verification on the highway axle load data with non-compliant deviation rates based on the wheelbase and tire quantity constraints corresponding to the vehicle type. Then, integrate the verified highway axle load data with the highway axle load data with compliant deviation rates to generate verified highway traffic volume axle load data.

9. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 7, characterized in that, Step S35 includes the following steps: Step S351: Based on the material type and fatigue characteristic parameters in the highway pavement structure parameters, perform axle load conversion matching analysis to determine the equivalent axle number conversion coefficients corresponding to different axle loads and generate a highway fatigue axle load equivalent conversion table. Step S352: Based on the highway fatigue axle load equivalent conversion table and the highway time-sharing axle load distribution data, calculate the equivalent axle number corresponding to different axle load intervals in each time period to generate highway time-sharing equivalent axle number data. Step S353: Construct a pavement axle load damage assessment model based on the thickness, compressive strength and flexural tensile strength of each pavement structural layer in the pavement structural parameters of the highway, and input the equivalent axle data of the highway in different time periods into the pavement axle load damage assessment model to calculate the cumulative damage corresponding to each pavement structural layer according to the time series, and calculate the cumulative damage summation based on the cumulative damage corresponding to each pavement structural layer to obtain the cumulative damage value of the highway pavement axle load.

10. The method for dynamic high-precision detection of axle load in highway traffic volume according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Obtain the number and proportion of highway vehicle axle loads and the cumulative axle load damage value of the highway pavement in different axle load intervals within each time period by using the highway time-segmented axle load distribution data and the highway pavement axle load cumulative damage value. Step S42: Based on the number and proportion of axle loads of highway vehicles in different axle load intervals within each time period and the cumulative axle load damage value borne by the road surface, perform axle load-damage correlation analysis to analyze and identify the correlation between vehicle axle load distribution and cumulative damage within each time period, and generate highway vehicle axle load-cumulative damage correlation features. Step S43: Obtain real-time axle load detection data of the highway, and perform dynamic high-precision detection and correction of real-time traffic volume axle load based on the highway vehicle axle load-cumulative damage correlation characteristics to generate a dynamic detection report of highway traffic volume axle load. Step S44: Based on the cumulative axle load damage value of the road surface corresponding to the dynamic detection report of axle load of highway traffic volume, compare and evaluate the road surface damage with the preset road surface design service life threshold to obtain the current damage level of the highway road surface. Step S45: Based on the current damage level of the highway pavement and combined with the dynamic detection report of highway traffic volume and axle load, formulate corresponding vehicle axle load limits and maintenance timing plans, and generate a highway pavement axle load maintenance plan that includes maintenance process, maintenance scope and implementation priority.

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