Intelligent paving process for asphalt concrete pavement

Abstract

The invention provides an intelligent paving process for an asphalt concrete pavement, which comprises the following steps: after uniformly paving an asphalt mixture, compacting the pavement by using a road roller, and in the compacting process, acquiring pressure data and temperature field data in real time by using a pressure sensor and an infrared thermal imager; calculating an equivalent compaction degree according to the pressure data and the temperature field data of the corresponding area, and generating a thermodynamic diagram according to the temperature field data and the equivalent compaction degree data; and based on the compaction degree distribution of the thermodynamic diagram, generating an optimal rolling path by adopting an improved A * algorithm, and dynamically adjusting the rolling path of the road roller according to the optimal rolling path. The problem that in the prior art, in the rolling process, the asphalt rolling effect is controlled mostly through independent control of a single parameter, and consequently the construction efficiency and the construction quality are affected is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of road construction, and particularly to an intelligent paving process for asphalt concrete pavement. Background Technique

[0002] Asphalt concrete is a mixture made of mineral materials, crushed stones or crushed gravel, stone chips or sand, mineral powder, etc., and a certain proportion of road asphalt materials, which is prepared under strictly controlled conditions. Asphalt concrete is mainly used in road engineering construction. During road engineering construction, a paver is usually used to evenly pave asphalt concrete on the road surface to form an asphalt concrete pavement. After paving, a roller is used to roll the asphalt concrete pavement to compact it.

[0003] During the rolling process of traditional asphalt concrete pavement, most of them achieve the control of asphalt rolling effect through independent control of a single parameter (such as: initial rolling temperature, asphalt layer thickness, rolling pressure, asphalt feeding amount or rolling speed), which will affect the construction efficiency and construction quality.

[0004] For example, the existing patent CN118127885B discloses an intelligent paving system applicable to asphalt concrete road engineering. When rolling asphalt concrete, intelligent monitoring is realized according to the feeding amount, so as to reasonably allocate the rolling time to ensure the rolling temperature of asphalt, avoid the situation of asphalt hardening caused by too slow asphalt rolling speed, and at the same time, it can also avoid the problem of slow construction progress of asphalt pavement caused by insufficient feeding speed; by collecting historical asphalt rolling data and completing the correction of the prediction of asphalt rolling efficiency according to the historical asphalt rolling data, the intelligent system has a higher prediction accuracy, so as to more accurately coordinate the relationship between asphalt feeding amount and rolling speed and ensure the asphalt rolling effect. In the above intelligent paving system, only the asphalt feeding amount is controlled singly, and the compactness of the road surface is not controlled, resulting in easy occurrence of missed rolling or insufficient compaction on the road surface, and both missed rolling or insufficient compaction of the road surface are likely to cause premature oxidation of asphalt mixture, accelerate the peeling of aggregate and asphalt, and shorten the service life of the road surface.

[0005] Based on this, the present invention designs an intelligent paving process for asphalt concrete pavement to solve the problems raised in the above background technique. Summary of the Invention

[0006] Aiming at the deficiencies in the prior art, the present invention provides an intelligent paving process for asphalt concrete pavement, which solves the problem that in the prior art, during the rolling process, most of them achieve the control of asphalt rolling effect through independent control of a single parameter, which will affect the construction efficiency and construction quality.

[0007] According to an embodiment of the present invention, an intelligent paving process for asphalt concrete pavement includes the following steps:

[0008] After evenly paving the asphalt mixture, use a roller to compact the road surface. During the compaction process, a pressure sensor and an infrared thermal imager are used to collect pressure data and temperature field data in real time.

[0009] Calculate the equivalent compaction degree through the pressure data and the temperature field data of the corresponding area, and then generate a thermal map based on the temperature field data and the equivalent compaction degree data.

[0010] Based on the compaction degree distribution of the thermal map, use an improved A* algorithm to generate an optimal rolling path, and dynamically adjust the rolling path of the roller according to the optimal rolling path.

[0011] The technical principle of the present invention is as follows:

[0012] After evenly paving the asphalt mixture on the roadbed, use a roller to roll the asphalt mixture to compact the road surface. During the compaction process, a pressure sensor and an infrared thermal imager are used to collect pressure data and temperature field data in real time, and calculate the equivalent compaction degree according to the collected pressure data and the temperature field data of the asphalt mixture in the corresponding area. Then, based on the temperature field data during the rolling process and the calculated equivalent compaction degree data, generate a thermal map that intuitively displays the spatial data distribution with color gradients, and use an improved A* algorithm to generate an optimal rolling path based on the compaction degree distribution of the thermal map, so as to dynamically adjust the rolling path of the roller according to the optimal rolling path to preferentially cover the low-temperature and low-density areas.

[0013] Compared with the prior art, the present invention has the following beneficial effects:

[0014] By adopting the method of fusing the data collected by the pressure sensor and the infrared thermal imager, calculate the equivalent compaction degree, and finally generate an optimal rolling path to dynamically adjust the rolling path of the roller, which solves the problem that in the prior art, during the rolling process, most of the asphalt rolling effect is controlled by independent control of a single parameter, resulting in affecting the construction efficiency and construction quality, realizes the full compaction of the construction road surface, ensures the service life of the construction road surface, and at the same time, avoids over-compaction, saves compaction time, and improves compaction efficiency.

[0015] Preferably, the specific formula for calculating the equivalent compaction degree through the pressure data and the temperature data of the corresponding area is:

[0016]

[0017] Among them, C(x, y) represents the equivalent compaction degree at the coordinate (x, y); P(t) represents the dynamic pressure signal collected by the pressure sensor at time t; T(x, y) represents the temperature value of the asphalt mixture measured by the infrared thermal imager at the coordinate (x, y); T ambient represents the ambient temperature; η material represents the characteristic parameter of the asphalt mixture.

[0018] Preferably, for the compaction degree distribution based on the heat map, an improved A* algorithm is used to generate an optimal rolling path, and the rolling path of the roller is dynamically adjusted according to the optimal rolling path, including:

[0019] After obtaining the heat map, upload the heat map to the cloud, and the cloud calculator uses an improved A* algorithm to generate an optimal rolling path;

[0020] Transmit the optimal rolling path to the roller, so that the roller dynamically adjusts its own rolling path according to the optimal rolling path.

[0021] Preferably, the objective function for generating the optimal rolling path by using the improved A* algorithm is:

[0022]

[0023] Among them, W1 represents the compaction degree deviation weight coefficient, which is used to control the influence degree of the compaction degree deviating from the target value on the total cost; T i represents the measured real-time temperature value of the i-th section area in the rolling path; W2 represents the path length weight coefficient, which is used to balance the influence of the total length of the rolling path on the total cost; C i represents the equivalent compaction degree value of the i-th section area in the rolling path.

[0024] Preferably, the uniform paving of the asphalt mixture includes:

[0025] Use a paver to uniformly pave the asphalt mixture on the roadbed. During paving, use an infrared thermal imager and a lidar to collect temperature field data and road surface elevation field data in real time;

[0026] Through weighted fusion of the temperature field data and the road surface elevation field data, generate a temperature-elevation composite field, and dynamically adjust the paving speed based on the composite field data.

[0027] Preferably, during the process of uniformly paving the asphalt mixture on the roadbed by using the paver and compacting the road surface by using the roller, pack various data every 10 s and upload them to the cloud to support later quality traceability and process optimization.

[0028] Preferably, while using the roller for road surface compaction, a lidar is used to collect road surface elevation field data in real time, and a road surface elevation-temperature-pressure coupling model is established. When the real-time value of the composite control index of the road surface elevation-temperature-pressure coupling model exceeds a preset threshold, an audible and visual alarm is triggered, and the defect location is recorded.

[0029] Preferably, during the process of the lidar collecting road surface elevation field data in real time, it should be used in conjunction with a global satellite navigation system to realize the real-time comparison of the rolling track and the designed elevation.

[0030] Preferably, after identifying the defect location, the defect location is uploaded to the cloud. The cloud calculator automatically generates a supplementary compaction path based on the defect location and adjusts the rolling path of the roller according to the supplementary compaction path.

[0031] Preferably, after identifying the defect location, uploading the defect location to the cloud, and the cloud calculator intelligently generating a supplementary compaction path based on the defect location and dynamically adjusting the rolling path of the roller according to the supplementary compaction path, includes:

[0032] After identifying the defect location, relevant information of the defect location is uploaded to the cloud through a communication module to generate a compaction quality heat map with geographic coordinate information;

[0033] The cloud calculator automatically generates a supplementary compaction path based on the defect coordinate information in the heat map and transmits the supplementary compaction path to the roller;

[0034] After receiving the supplementary compaction path, the roller triggers the steering control system and adjusts the driving direction according to the supplementary compaction path;

[0035] After arriving at the defect location, the roller performs supplementary compaction on the defect location, and during the supplementary compaction process, the road surface elevation data, temperature data, and pressure data of the defect location are collected in real time;

[0036] When the real-time value of the composite control index of the road surface elevation-temperature-pressure coupling model reaches the preset threshold, the supplementary compaction operation is automatically stopped, and the status of the heat map is updated to qualified. Description of the Drawings

[0037] Figure 1 It is a flow step diagram of the intelligent paving process of the asphalt concrete road surface in the first embodiment of the present invention.

[0038] Figure 2 It is a specific step diagram of evenly paving the asphalt mixture in the first embodiment of the present invention.

[0039] Figure 3Specific step diagram for generating an optimal rolling path using an improved A* algorithm based on the compaction degree distribution of the heat map in the first embodiment of the present invention, and dynamically adjusting the rolling path of the roller according to the optimal rolling path.

[0040] Figure 4 Specific step diagram for dynamically adjusting the rolling path of the roller according to the optimal rolling path in the first embodiment of the present invention.

[0041] Figure 5 Flow step diagram of the intelligent paving process for asphalt concrete pavement in the second embodiment of the present invention.

[0042] Figure 6 Specific step diagram for uploading the defect location to the cloud after identifying the defect location in the second embodiment of the present invention, the cloud calculator intelligently generating a supplementary compaction path according to the defect location, and dynamically adjusting the rolling path of the roller according to the supplementary compaction path. Detailed implementation manners

[0043] The technical solutions in the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0044] First embodiment

[0045] As Figure 1 shown, the first embodiment of the present invention proposes an intelligent paving process for asphalt concrete pavement, including the following steps:

[0046] S1. After evenly paving the asphalt mixture, use a roller to compact the road surface. During the compaction process, use a pressure sensor and an infrared thermal imager to collect pressure data and temperature field data in real time.

[0047] Specifically, as Figure 2 shown, the step of evenly paving the asphalt mixture includes:

[0048] S11. Use a paver to evenly pave the asphalt mixture on the roadbed. During the paving process, use an infrared thermal imager and a lidar to collect temperature field data and road surface elevation field data in real time.

[0049] Among them, before using the paver to evenly pave the asphalt mixture on the roadbed, it is necessary to install the infrared thermal imager at the front ends of both the paver and the roller, install the lidar on the screed or the frame of the paver, embed the pressure sensor in the vibrating wheel or the steel wheel of the roller, and perform initialization processing on the parameters of the paver, the roller, the infrared thermal imager, the lidar, and the pressure sensor.

[0050] After initializing the parameters, the paver and the roller are used to perform the paving and compaction work on the road surface respectively. During the paving and compaction work, the infrared thermal imager is used to monitor the temperature field distribution of the paving layer in real time, and to track the temperature gradient of the rolling area, so as to ensure that during the process of evenly paving the asphalt mixture on the roadbed by the paver and compacting the road surface by the roller, the real-time temperature data of the road surface meet the design requirements, preventing under-compaction at low temperature or over-compaction at high temperature; through the lidar scanning, the paving thickness and flatness of the paving layer are measured in real time, so as to ensure that during the process of evenly paving the asphalt mixture on the roadbed by the paver and compacting the road surface by the roller, the real-time elevation field data of the road surface meet the design requirements, thus ensuring the paving quality of the paver during paving; the pressure data during rolling are collected in real time by the pressure sensor, so that the compaction degree of the corresponding area can be calculated by combining with the temperature field data of the area, and the areas with missed compaction / over-compaction are warned.

[0051] In addition, the infrared thermal imager and the lidar can also be replaced by an intelligent asphalt paving thickness sensor, which is based on ground penetrating radar technology and has the functions of on-line, continuous and non-destructive detection. By installing the intelligent asphalt paving thickness sensor on the screed of the paver, the paving thickness, aggregate dielectric constant and temperature of the asphalt can be monitored in real time (the measurement error is within ±3mm).

[0052] S12. Weightedly fuse the temperature field data and the road surface elevation field data to generate a temperature-elevation composite field, and dynamically adjust the paving speed based on the composite field data.

[0053] Among them, the specific formula for dynamically adjusting the paving speed based on the composite field data is:

[0054] V adjust =V0·(1-β·ΔC)

[0055] ΔC=C i,j -C target

[0056]

[0057] Among them, V adjust represents the adjusted paving speed; V0 represents the reference paving speed (preset initial value); β represents the speed adjustment coefficient, and the value range is (0,1]; C i,j represents the composite coefficient, which reflects the coupling effect of temperature and elevation and is used for paving quality evaluation; C target represents the target composite field value (the optimal value in the ideal state, usually taking 0.5); α represents the normalized weight factor of temperature and elevation data, and the value range is [0,1], which can be dynamically optimized through historical construction data; Ti,j represents the temperature value of the asphalt mixture measured by the infrared thermal imager at the coordinate (i, j); T max and T min respectively represent the maximum temperature threshold and the minimum temperature threshold within the current construction area; H i,j represents the elevation value of the road surface paving layer detected by the lidar at the coordinate (i, j); H max and H min respectively represent the maximum allowable paving thickness and the minimum paving thickness within the current construction area.

[0058] When ΔC > 0, it indicates that the temperature of the local area is too high or the thickness exceeds the standard, and the paver needs to reduce its speed; when ΔC < 0, it indicates that the temperature of the local area is insufficient or the thickness is insufficient, and the paver can appropriately increase its speed (but safety constraints need to be combined).

[0059] While using the paver to evenly spread the asphalt mixture on the roadbed, real-time temperature field data and real-time road surface elevation field data are respectively collected through the infrared thermal imager and the lidar, and the collected real-time temperature field data and real-time road surface elevation field data are weighted and fused to generate a temperature-elevation composite field, and then the paving speed of the paver is dynamically adjusted based on the temperature-elevation composite field, so as to realize multi-dimensional control of the paving speed of the paver, and further reduce the thickness error of the constructed road surface and ensure the qualified rate of the thickness of the constructed road surface.

[0060] In addition, by installing a vision camera on the paver and pre-arranging a guiding line on one side of the unpaved roadbed, the deviation between the paver and the guiding line is collected in real time to control the autonomous driving of the paver (in the straight-line working condition, the walking track error is controlled within ≤4 mm; in the turning working condition, the walking track error is controlled within ≤12 mm).

[0061] S2. Calculate the equivalent compaction degree through the pressure data and the temperature field data of the corresponding area, and then generate a thermal map based on the temperature field data and the equivalent compaction degree data.

[0062] Specifically, the specific formula for calculating the equivalent compaction degree through the pressure data and the temperature data of the corresponding area is:

[0063]

[0064] where C(x, y) represents the equivalent compaction degree at the coordinate (x, y); P(t) represents the dynamic pressure signal collected by the pressure sensor at the moment t; T(x, y) represents the temperature value of the asphalt mixture measured by the infrared thermal imager at the coordinate (x, y); T ambient represents the ambient temperature; η material represents the characteristic parameter of the asphalt mixture.

[0065] After calculating the real-time equivalent compaction degree value, the temperature field data and the compaction degree data are fused to generate a heat map that visually displays the spatial data distribution with color gradients. In this heat map, the abscissa represents the temperature, the ordinate represents the compaction degree, the high-temperature and high-density areas are shown in red, the low-temperature and low-density areas are shown in blue, and the transition areas are shown in yellow, so as to quickly identify the weak construction areas (blue areas) that need additional compaction or additional temperature.

[0066] S3. Based on the compaction degree distribution of the heat map, the improved A* algorithm is used to generate the optimal rolling path, and the rolling path of the roller is dynamically adjusted according to the optimal rolling path.

[0067] Specifically, as Figure 3 shown, the method of using the improved A* algorithm to generate the optimal rolling path based on the compaction degree distribution of the heat map and dynamically adjusting the rolling path of the roller according to the optimal rolling path includes:

[0068] S31. After obtaining the heat map, the heat map is uploaded to the cloud, and the cloud calculator uses the improved A* algorithm to generate the optimal rolling path.

[0069] Among them, the objective function for generating the optimal rolling path using the improved A* algorithm is:

[0070]

[0071] Among them, W1 represents the compaction degree deviation weight coefficient, which is used to control the influence degree of the compaction degree deviating from the target value on the total cost; T i represents the measured real-time temperature value of the i-th section area in the rolling path; W2 represents the path length weight coefficient, which is used to balance the influence of the total length of the rolling path on the total cost; C i represents the equivalent compaction degree value of the i-th section area in the rolling path.

[0072] Since the viscosity of asphalt material is moderate at about 150°C, it is easy to compact and can ensure the paving density. |T i -150| represents the deviation between the current temperature T i and the target value. The greater the deviation, the higher the penalty cost, forcing the algorithm to give priority to processing the low-temperature area (T i < 150°C) and compacting the low-temperature area; the compaction degree of the asphalt pavement needs to reach more than 95% of the theoretical maximum density to avoid water seepage or cracking caused by excessive void ratio. |C i -95%| represents the gap between the current compaction degree C i and the target value. The algorithm preferentially compacts the low-density area (C i < 95%) by dynamically adjusting the rolling path.

[0073] S32. Transmit the optimal rolling path to the roller so that the roller dynamically adjusts its own rolling path according to the optimal rolling path.

[0074] As Figure 4 shown, the dynamically adjusting its own rolling path according to the optimal rolling path includes:

[0075] S321. Dynamically adjust the overlapping width of adjacent rolling belts according to the type of roller. If the roller is a vibratory roller, the overlapping width is 0.4 - 0.5 m. If the roller is a tandem roller, the overlapping width is 1 / 2 of the rear wheel width;

[0076] S322. Dynamically adjust the rolling direction according to the type of road. If the road is a straight section, roll from both sides to the center. If the road is a curve section, roll from the inside to the outside;

[0077] S323. Dynamically adjust the rolling sequence according to the existence of retaining structures (such as: curb). If there is a retaining structure in the rolling area, give priority to rolling closely to the retaining area. If there is no retaining structure in the rolling area, roll in batches and avoid the edge area;

[0078] S324. Dynamically adjust the rolling sequence according to the existence of obstacles. If there is a fixed obstacle, adopt a "return" - shaped detour and increase the static pressure compensation by 1 - 2 times. If there is a moving obstacle, adopt a "forward - backward" rolling method, with each forward movement ≤ 1 m. If there is no obstacle, roll normally according to the optimal rolling path.

[0079] In addition, during the process of evenly spreading the asphalt mixture on the roadbed by using the paver and compacting the road surface by using the roller, pack various data (including time, coordinates, temperature, pressure, road surface elevation, paver running speed, roller rolling track) every 10 s and upload them to the cloud to support later quality traceability and process optimization.

[0080] Second Embodiment

[0081] As Figure 5 shown, an embodiment of the present invention provides an intelligent paving process for asphalt concrete roads, which further includes the following steps:

[0082] S4. While using the roller to compact the road surface, use a lidar to collect road surface elevation field data in real - time and establish a road surface elevation - temperature - pressure coupling model. When the real - time value of the composite control index of the road surface elevation - temperature - pressure coupling model exceeds the preset threshold, trigger an audible and visual alarm and record the defect location.

[0083] Specifically, during the real-time acquisition of road surface elevation field data, the lidar should be used in conjunction with the Global Navigation Satellite System (GNSS) to achieve real-time comparison of the rolling trajectory and the designed elevation, make up for the limitations of a single device, enhance the anti-interference ability during construction, and achieve continuous, stable, and smooth construction.

[0084] The objective function of the road surface elevation-temperature-pressure coupling model is as follows:

[0085]

[0086] Among them, Q represents the composite control index; α, β, and γ respectively represent the weight coefficients of road surface elevation, temperature, and pressure, and α + β + γ = 1 (the three are dynamically optimized and adjusted according to historical construction data); ΔH represents the maximum elevation difference of the paving layer within the paving area; H design represents the designed elevation value of the paving layer within the paving area; ΔT represents the maximum temperature difference of the asphalt mixture within the paving area; T ideal represents the ideal paving temperature of the asphalt mixture (usually set at 150 °C); P actual represents the pressure value actually measured by the pressure sensor within the paving area; P target represents the target pressure value required for the roller to compact the road surface within the paving area.

[0087] By establishing a road surface elevation-temperature-pressure coupling model and calculating the real-time value of the composite control index of the road surface elevation-temperature-pressure coupling model according to the objective function, when the real-time value of the composite control index of the road surface elevation-temperature-pressure coupling model exceeds the preset threshold, an audible and visual alarm can be triggered, and the defect location can be recorded to facilitate recompression of the defect location.

[0088] S5. After identifying the defect location, upload the defect location to the cloud. The cloud calculator automatically generates a recompression path based on the defect location and adjusts the rolling path of the roller according to the recompression path.

[0089] Specifically, as Figure 6 shown, after identifying the defect location, uploading the defect location to the cloud, the cloud calculator intelligently generates a recompression path based on the defect location, and dynamically adjusts the rolling path of the roller according to the recompression path, including:

[0090] S51. After identifying the defect location, relevant information about the defect location is uploaded to the cloud through the communication module (4G / 5G) to generate a compaction quality heat map with geographic coordinate information;

[0091] S52. The cloud calculator automatically generates a recompression path based on the defect coordinate information in the heat map and transmits the recompression path to the roller;

[0092] S53. After the road roller receives the additional compaction path, it triggers the steering control system and adjusts the driving direction according to the additional compaction path.

[0093] S54. After arriving at the defective position, the road roller compacts the defective position, and during the compaction process, it collects the road surface elevation data, temperature data, and pressure data of the defective position in real time.

[0094] S55. When the real-time value of the composite control index of the road surface elevation-temperature-pressure coupling model reaches the preset threshold, the additional compaction operation is automatically stopped, and the heat map status is updated to qualified.

[0095] Specifically, by collecting the road surface elevation field data, temperature field data, and pressure data in real time and establishing a road surface elevation-temperature-pressure coupling model, the defective position can be identified through the road surface elevation-temperature-pressure coupling model. Then, the road roller is used to perform secondary compaction on the identified defective position until the compaction degree of the construction road surface reaches the preset requirements, so as to further improve the compaction degree of the construction road surface, avoid the premature oxidation of the asphalt mixture on the under-compacted road surface, and accelerate the peeling of the aggregate and the asphalt, shortening the service life of the road surface.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. An intelligent paving process for asphalt concrete pavement, characterized in that, It includes the following steps: After the asphalt mixture is evenly spread, a road roller is used to compact the road surface. During the compaction process, a pressure sensor and an infrared thermal imager are used to collect pressure data and temperature field data in real time. The equivalent compaction degree is calculated through the pressure data and the temperature field data of the corresponding area, and then a thermal map is generated based on the temperature field data and the equivalent compaction degree data. Based on the compaction degree distribution of the thermal map, an improved A* algorithm is used to generate an optimal rolling path, and the rolling path of the road roller is dynamically adjusted according to the optimal rolling path.

2. An intelligent paving process for an asphalt concrete road surface according to claim 1, characterized in that: The specific formula for calculating the equivalent compaction degree through the pressure data and the temperature data of the corresponding area is: Among them, C(x, y) represents the equivalent compaction degree at the coordinate (x, y); P(t) represents the dynamic pressure signal collected by the pressure sensor at time t; T(x, y) represents the temperature value of the asphalt mixture measured by the infrared thermal imager at the coordinate (x, y); T ambient represents the ambient temperature; η material represents the characteristic parameters of the asphalt mixture.

3. An intelligent paving process for an asphalt concrete road surface according to claim 2, characterized in that: Based on the compaction degree distribution of the thermal map, using an improved A* algorithm to generate an optimal rolling path, and dynamically adjusting the rolling path of the road roller according to the optimal rolling path, including: After obtaining the thermal map, upload the thermal map to the cloud, and the cloud calculator uses an improved A* algorithm to generate an optimal rolling path; Transmit the optimal rolling path to the road roller, so that the road roller dynamically adjusts its own rolling path according to the optimal rolling path.

4. An intelligent paving process for an asphalt concrete road surface according to claim 3, characterized in that: The objective function for generating the optimal rolling path using the improved A* algorithm is: Among them, W1 represents the compaction degree deviation weight coefficient, which is used to regulate the influence degree of the compaction degree deviating from the target value on the total cost; T i represents the measured actual value of the real-time temperature of the i-th section area in the rolling path; W2 represents the path length weight coefficient, which is used to balance the influence of the total length of the rolling path on the total cost; C i represents the equivalent compaction degree value of the i-th section area in the rolling path.

5. An intelligent paving process for an asphalt concrete road surface according to claim 1, characterized in that: The evenly spreading of the asphalt mixture includes: Using a paver to evenly spread the asphalt mixture on the roadbed. During paving, an infrared thermal imager and a lidar are used to collect temperature field data and road surface elevation field data in real time; The temperature field data and the road surface elevation field data are weighted and fused to generate a temperature-elevation composite field, and the paving speed is dynamically adjusted based on the composite field data.

6. An intelligent paving process for an asphalt concrete road surface according to claim 5, characterized in that: During the process of using the paver to evenly spread the asphalt mixture on the roadbed and using the road roller to compact the road surface, all data is packed every 10s and uploaded to the cloud to support later quality traceability and process optimization.

7. An intelligent paving process for an asphalt concrete road surface according to claim 1, characterized in that: While using the road roller to compact the road surface, a lidar is used to collect road surface elevation field data in real time, and a road surface elevation-temperature-pressure coupling model is established. When the real-time value of the composite control index of the road surface elevation-temperature-pressure coupling model exceeds the preset threshold, an audible and visual alarm is triggered, and the defect location is recorded.

8. An intelligent paving process for an asphalt concrete road surface according to claim 7, characterized in that: During the process of the lidar collecting road surface elevation field data in real time, it should be used in conjunction with a global navigation satellite system to realize real-time comparison of the rolling track and the designed elevation.

9. An intelligent paving process for asphalt concrete pavement according to claim 7, characterized in that: After identifying the defect location, the defect location is uploaded to the cloud, and the cloud calculator automatically generates a supplementary compaction path according to the defect location and adjusts the rolling path of the roller according to the supplementary compaction path.

10. An intelligent paving process for asphalt concrete pavement according to claim 9, characterized in that: After identifying the defect location, the defect location is uploaded to the cloud, and the cloud calculator intelligently generates a supplementary compaction path according to the defect location and dynamically adjusts the rolling path of the roller according to the supplementary compaction path, including: After identifying the defect location, the relevant information of the defect location is uploaded to the cloud through the communication module to generate a compaction quality heat map with geographic coordinate information; The cloud calculator automatically generates a supplementary compaction path based on the defect coordinate information in the heat map and transmits the supplementary compaction path to the roller; After receiving the supplementary compaction path, the roller triggers the steering control system and adjusts the driving direction according to the supplementary compaction path; After arriving at the defect location, the roller performs supplementary compaction on the defect location, and during the supplementary compaction process, the road surface elevation data, temperature data, and pressure data of the defect location are collected in real time; When the real-time value of the composite control index of the road surface elevation-temperature-pressure coupling model reaches the preset threshold, the supplementary compaction operation is automatically stopped, and the status of the heat map is updated to qualified.

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