Unmanned 3D paving and compacting cooperative construction method

Abstract

The invention relates to the technical field of engineering data analysis, in particular to an unmanned 3D (three-dimensional) paving and compacting cooperative construction method, which comprises the following steps of: acquiring the surface temperature of a paved mixture in real time by integrating an advanced temperature detection device; and an effective compaction time window is calculated by combining the elevation data, the wind speed acquired by the environment monitoring module and a preset compaction aging model, so that the surface temperature of the asphalt mixture is automatically analyzed to determine the optimal compaction time. According to the method, the uncertainty and subjectivity of estimating the proper compaction time of the mixture by depending on artificial experience or a simple temperature monitoring means in a traditional method are overcome, the compaction operation can be carried out under an ideal temperature condition, the construction quality is greatly improved, and the service life of a pavement is greatly prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of engineering data analysis, and specifically to an unmanned 3D paving and compaction collaborative construction method. Background Art

[0002] In modern road construction, the traditional asphalt mixture compaction process struggles to precisely control compaction time to ensure optimal pavement performance. Existing technologies often rely on manual experience or simple temperature monitoring to estimate the optimal compaction timing for the mixture. This can easily lead to under- or over-compaction and fail to fully utilize the material's temperature window for optimal densification. Consequently, the existing asphalt mixture compaction process is subject to uncertainty and subjectivity. To ensure compaction under ideal temperature conditions, improve construction quality, and enhance the lifespan of the pavement, an unmanned 3D paving and compaction collaborative construction method is urgently needed that can automatically analyze the asphalt mixture surface temperature to determine the optimal compaction time. Summary of the Invention

[0003] The technical problem solved by the present invention is to provide an unmanned 3D paving and compaction collaborative construction method to solve the technical problem that the existing asphalt mixture compaction process cannot fully utilize the temperature window of the material for compaction to achieve the best compaction effect.

[0004] The basic solution provided by the present invention is an unmanned 3D paving and compaction collaborative construction method, comprising the following steps:

[0005] S1: After the unmanned paver completes the paving operation, the surface temperature T of the paved mixture is collected in real time through the integrated temperature detection device;

[0006] S2: Calculate the paving layer thickness H in real time based on the elevation data during paving operations, and obtain the real-time wind speed V through the environmental monitoring module;

[0007] S3: Input the real-time temperature T, paving thickness H and wind speed V into the preset compaction time model to calculate the effective compaction time window Δt of the mixture;

[0008] S4: Based on the effective compaction time window Δt, the travel distance D and compaction speed v of the roller group are automatically calculated through the collaborative control algorithm;

[0009] S5: Wirelessly transmit control instructions to the unmanned roller group, so that it maintains a dynamic distance D from the paver and performs compaction operations at the optimized speed v;

[0010] S6: During the compaction process, density data of the mixture is obtained in real time, and fed back to the compaction time-effect model for dynamic parameter correction.

[0011] Furthermore, the method for obtaining the paving layer thickness H in S2 is:

[0012] The GNSS positioning module and laser elevation scanner on the paver are used to construct a three-dimensional point cloud model of the paving surface in real time, and the edge computing unit processes the model to generate millimeter-level precision thickness data.

[0013] Furthermore, the environmental monitoring module includes an ultrasonic wind speed sensor installed at the front end of the paver, wherein the ultrasonic wind speed sensor is set at a height of 0.5-1.2 meters from the paving surface.

[0014] Furthermore, the establishment formula of the compaction efficiency model is:

[0015]

[0016] Where K is the material correction coefficient, T0 is the initial compaction temperature threshold, α and β are the influencing factors of thickness and wind speed, respectively, which are determined by fitting experimental data.

[0017] Furthermore, the collaborative control algorithm adopts the fuzzy PID control strategy to establish a control rule base with Δt as the input variable and D and v as the output variables, where the roller speed is:

[0018]

[0019] Where η is the dynamic adjustment coefficient and 0.8≤η≤1.2.

[0020] Furthermore, a density feedback correction mechanism is provided in step S6. When the measured density deviates from the target value by more than 3%, the online learning of the parameters of the compaction efficiency model is automatically triggered, and the K, α, and β coefficients in the model are updated through the machine learning algorithm.

[0021] The principle and advantages of this invention lie in: It integrates an advanced temperature detection device to collect real-time surface temperature of the mixture after paving. This system automatically analyzes the surface temperature of the asphalt mixture to determine the optimal compaction time, combining elevation data, wind speed acquired by an environmental monitoring module, and a pre-set compaction efficiency model. This system overcomes the uncertainty and subjectivity inherent in traditional methods that rely on manual experience or simple temperature monitoring to estimate the optimal compaction time. This ensures that compaction operations can be performed under ideal temperature conditions, significantly improving construction quality and the lifespan of the pavement. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flowchart of the steps of an embodiment of an unmanned 3D paving and compaction collaborative construction method of the present invention. DETAILED DESCRIPTION

[0023] The following is further described in detail through specific implementation methods:

[0024] The specific implementation process is as follows:

[0025] Example 1

[0026] Example 1 is basically as shown in the attached Figure 1 As shown, an unmanned 3D paving and compaction collaborative construction method includes the following steps:

[0027] S1: After the unmanned paver completes the paving operation, the surface temperature T of the paved mixture is collected in real time through the integrated temperature detection device;

[0028] S2: Calculate the paving layer thickness H in real time based on the elevation data during paving operations, and obtain the real-time wind speed V through the environmental monitoring module;

[0029] S3: Input the real-time temperature T, paving thickness H and wind speed V into the preset compaction time model to calculate the effective compaction time window Δt of the mixture;

[0030] S4: Based on the effective compaction time window Δt, the travel distance D and compaction speed v of the roller group are automatically calculated through the collaborative control algorithm;

[0031] S5: Wirelessly transmit control instructions to the unmanned roller group, so that it maintains a dynamic distance D from the paver and performs compaction operations at the optimized speed v;

[0032] S6: During the compaction process, density data of the mixture is obtained in real time, and fed back to the compaction time-effect model for dynamic parameter correction.

[0033] The unmanned paver in this embodiment is a hydraulically driven crawler paver with an adjustable screed width (3-12m) and a paving speed range of 0.5-5m / min. The unmanned roller unit consists of two twin-steel-drum vibratory rollers and one rubber-wheel roller, equipped with a 5G communication module and a real-time compaction detection system based on a vibration acceleration sensor and spectrum analysis algorithm.

[0034] In S1, after the unmanned paver completes the paving operation, the surface temperature T of the paved mixture is collected in real time via an integrated temperature detection device. Specifically, the integrated temperature detection device is an infrared thermal imager array mounted 1 meter behind the paver. The rollers in the unmanned roller group in this embodiment are also equipped with an integrated temperature detection device. The infrared thermal imager array has a resolution of 640×480, a temperature measurement range of 50-250°C, an accuracy of ±1°C, and covers the entire width of the paving area. A temperature field distribution map is generated every 2 seconds, from which the surface temperature T of the paved mixture is obtained.

[0035] In S2, the paving layer thickness H is calculated in real time based on the elevation data during the paving operation, and the real-time wind speed V is obtained through the environmental monitoring module. In this embodiment, the paving layer thickness H is obtained by using the paver's onboard GNSS positioning module and laser elevation scanner to construct a three-dimensional point cloud model of the paving surface in real time. This is then processed by the edge computing unit to generate millimeter-level thickness data. An ultrasonic anemometer is also installed on the top of the paver, with a range of 0-20 m / s and an accuracy of ±0.2 m / s, and it outputs the wind speed V in real time.

[0036] In S3, the real-time temperature T, paving thickness H, and wind speed V are input into the preset compaction aging model to calculate the effective compaction time window Δt of the mixture. The establishment formula of the compaction aging model is:

[0037]

[0038] Where K is the material correction factor, T0 is the initial compaction temperature threshold, and α and β are the influencing factors of thickness and wind speed, respectively, determined by fitting experimental data. In this example, the parameters K = 0.85 (modified asphalt coefficient), T0 = 130°C, H = 80 mm, α = 0.012, and β = 0.35, resulting in Δt = 18 minutes.

[0039] In S4, based on the effective compaction time window Δt, the collaborative control algorithm automatically calculates the travel distance D and compaction speed v of the roller group. Specifically, the collaborative control algorithm adopts a fuzzy PID control strategy and establishes a control rule base with Δt as the input variable and D and v as the output variables, where the roller speed is:

[0040]

[0041] Where η is the dynamic adjustment coefficient and is 0.8≤η≤1.2. In this embodiment, the collaborative control algorithm plans the dynamic spacing D = 25m between the roller group and the paver based on Δt = 18 minutes, and the speed v = 3.6km / h (satisfying v = ηD / Δt, η is 1.0).

[0042] In S5, control instructions are wirelessly transmitted to the unmanned roller group, causing them to maintain a dynamic distance D from the paver and perform compaction operations at the optimized speed v. Specifically, during the compaction phase, the first steel-drum roller performs initial compaction in a high-frequency, low-amplitude mode (35Hz / 0.4mm), maintaining a 25m distance from the paver. When the first steel-drum roller detects that the mixture has cooled to 130°C, the second roller switches to a low-frequency, high-amplitude mode (28Hz / 0.8mm) for secondary compaction.

[0043] In S6, a density feedback correction mechanism is provided. When the measured density deviates from the target value by more than 3%, the online learning of the parameters of the compaction effectiveness model is automatically triggered, and the coefficients in the model are updated through the machine learning algorithm. The unmanned paver in this embodiment is provided with a density feedback system, which obtains the density data of the mixture in real time during the compaction process and feeds it back to the compaction effectiveness model for dynamic parameter correction. Specifically, when it is detected that the compaction degree of a local area is only 93% (target value ≥ 96%), the model parameter correction is triggered: the K value of the area is automatically increased to 0.90, and the subsequent compaction interval is shortened to 20m.

[0044] In summary, this invention integrates advanced temperature detection equipment to collect real-time surface temperature of the paved mixture. It then combines elevation data, wind speed data from an environmental monitoring module, and a pre-set compaction efficiency model to calculate an effective compaction time window, thereby automatically analyzing the surface temperature of the asphalt mixture to determine the optimal compaction time. This overcomes the uncertainty and subjectivity inherent in traditional methods that rely on manual experience or simple temperature monitoring to estimate the appropriate compaction timing for the mixture. This ensures that compaction operations can be performed under ideal temperature conditions, significantly improving construction quality and the lifespan of the pavement.

[0045] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme are not described in detail here. Ordinary technicians in the field are aware of all common technical knowledge in the technical field of the invention before the application date or priority date, can obtain all existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the field can improve and implement this scheme in combination with their own abilities under the inspiration given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several variations and improvements can be made, which should also be regarded as the scope of protection of the present invention. These will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. An unmanned 3D paving and compaction collaborative construction method, characterized in that: Including steps: S1: After the unmanned paver completes the paving operation, the surface temperature T of the paved mixture is collected in real time through the integrated temperature detection device; S2: Calculate the paving layer thickness H in real time based on the elevation data during paving operations, and obtain the real-time wind speed V through the environmental monitoring module; S3: Input the real-time temperature T, paving thickness H and wind speed V into the preset compaction time-effectiveness model to calculate the effective compaction time window Δt of the mixture; S4: Based on the effective compaction time window Δt, the travel distance D and compaction speed v of the roller group are automatically calculated through the collaborative control algorithm; S5: Wirelessly transmit control instructions to the unmanned roller group, so that it maintains a dynamic distance D from the paver and performs compaction operations at the optimized speed v; S6: During the compaction process, the density data of the mixture is obtained in real time, and the data is fed back to the compaction time-effect model for dynamic parameter correction.

2. The unmanned 3D paving and compaction collaborative construction method according to claim 1, characterized in that: The method for obtaining the paving layer thickness H in S2 is: Through the GNSS positioning module and laser elevation scanner equipped on the paver, a three-dimensional point cloud model of the paving surface is constructed in real time, and the edge computing unit processes it to generate millimeter-level precision thickness data.

3. The unmanned 3D paving and compaction collaborative construction method according to claim 2, characterized in that: The environmental monitoring module includes an ultrasonic wind speed sensor installed at the front end of the paver, wherein the ultrasonic wind speed sensor is set at a height of 0.5-1.2 meters from the paving surface.

4. The unmanned 3D paving and compaction collaborative construction method according to claim 3, characterized in that: The formula for establishing the compaction efficiency model is: Where K is the material correction coefficient, T0 is the initial compaction temperature threshold, α and β are the influencing factors of thickness and wind speed, respectively, which are determined by fitting experimental data.

5. The unmanned 3D paving and compaction collaborative construction method according to claim 4, characterized in that: The collaborative control algorithm adopts the fuzzy PID control strategy and establishes a control rule base with Δt as the input variable and D and v as the output variables, where the roller speed is: Where η is the dynamic adjustment coefficient and 0.8≤η≤1.

2.

6. The unmanned 3D paving and compaction collaborative construction method according to claim 5, characterized in that: In step S6, a density feedback correction mechanism is provided. When the measured density deviates from the target value by more than 3%, the online learning of the parameters of the compaction efficiency model is automatically triggered, and the coefficients in the model are updated through the machine learning algorithm.

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