A highway asphalt intelligent paving and compaction integrated control system and method

Abstract

This invention relates to the field of road construction control technology, specifically disclosing an integrated control system and method for intelligent asphalt paving and compaction. The invention generates a comprehensive quality index field by real-time acquisition of paving layer temperature and thickness distribution data, spatial registration, and fusion processing. Then, based on a nonlinear mapping model, it dynamically determines the number of compaction passes and rolling speed that vary with spatial location, and converts these parameters into control commands for the roller actuator. This solves the problems of data interaction loss and lack of coordination mechanisms caused by the independent operation of paving and compaction processes, and the inability to dynamically adjust compaction parameters according to the actual paving conditions. It achieves continuous and precise control of the number of compaction passes and rolling speed as the paving layer changes spatially, improving the uniformity of asphalt pavement compaction and the consistency of smoothness.

Description

Technical Field

[0001] This invention relates to the field of road construction control technology, and in particular to an integrated control system and method for intelligent asphalt paving and compaction of highways. Background Technology

[0002] With the continuous growth of highway traffic flow and the increasing vehicle load, the construction quality of asphalt pavement has a decisive impact on road durability and driving safety. Currently, in the asphalt pavement construction process, paving and compaction, as key processes, are generally controlled independently. The paver and roller operate according to their respective parameters, lacking an effective data exchange and coordination mechanism. This separate control method makes it impossible to obtain key quality parameters such as the temperature distribution and thickness uniformity of the paved layer in real time during the compaction process. It is also difficult to dynamically adjust the number of compaction passes and rolling speed according to the actual paving conditions, easily leading to under-compaction or over-compaction. At the same time, the complex and variable construction site environment makes it difficult to accurately control the optimal compaction time using traditional manual experience, resulting in uneven pavement compaction and unevenness deviations, directly affecting the service life and performance of the asphalt pavement. Therefore, achieving intelligent collaborative control of the paving and compaction processes has become a key technical challenge for improving the construction quality of asphalt pavement. Summary of the Invention

[0003] To address the aforementioned technical problems, this invention provides an integrated control system and method for intelligent asphalt paving and compaction of highways.

[0004] Firstly, the present invention provides an integrated intelligent asphalt paving and compaction control system for highways, the technical solution of which is as follows: The paving layer status acquisition module is used to collect temperature distribution data and thickness distribution data of the asphalt mixture after paving in real time; The paving quality analysis module is used to perform spatial registration and fusion processing on the temperature distribution data and thickness distribution data to generate a comprehensive quality index field that characterizes the compaction suitability of different spatial locations of the paving layer. The compaction parameter decision module is used to dynamically determine the number of compaction passes and the rolling speed of the road roller based on the difference between the comprehensive quality index field and the preset compaction target using a pre-constructed nonlinear mapping model, wherein the number of compaction passes and the rolling speed are continuous control parameters that vary with the spatial position of the paving layer. The compaction control execution module is used to convert the number of compaction passes and the rolling speed into control commands for the roller actuator.

[0005] Secondly, this invention provides an integrated control method for intelligent asphalt paving and compaction of highways, the technical solution of which is as follows: Real-time acquisition of temperature and thickness distribution data of asphalt mixture after paving; Spatial registration and fusion processing are performed on the temperature distribution data and thickness distribution data to generate a comprehensive quality index field characterizing the compaction suitability of different spatial locations of the paving layer; Based on the difference between the comprehensive quality index field and the preset compaction target, a pre-constructed nonlinear mapping model is used to dynamically determine the number of compaction passes and the rolling speed of the roller, wherein the number of compaction passes and the rolling speed are continuous control parameters that vary with the spatial position of the paving layer. The number of compaction passes and the rolling speed are converted into control commands for the roller actuator.

[0006] The technical solution of this invention generates a comprehensive quality index field by collecting real-time data on the temperature and thickness distribution of the paving layer and performing spatial registration and fusion processing. Then, based on a nonlinear mapping model, it dynamically determines the number of compaction passes and the rolling speed that vary with spatial location, and converts the above parameters into control commands for the roller actuator. This solves the problems of data interaction loss and lack of coordination mechanism caused by the independent operation of paving and compaction processes, and the inability to dynamically adjust compaction parameters according to the actual paving conditions. It achieves continuous and precise control of the number of compaction passes and the rolling speed that vary with the spatial location of the paving layer, thereby improving the uniformity of compaction and the consistency of smoothness of asphalt pavement.

[0007] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0009] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 This is a schematic diagram of an embodiment of the intelligent asphalt paving and compaction integrated control system of the present invention; Figure 2 This is a flowchart illustrating an embodiment of the intelligent asphalt paving and compaction integrated control method of the present invention. Detailed Implementation

[0010] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0011] Figure 1 A schematic diagram of an embodiment of the intelligent asphalt paving and compaction integrated control system provided by the present invention is shown. Figure 1 As shown, the system includes: The paving layer status acquisition module 110 is used to collect temperature distribution data and thickness distribution data of the asphalt mixture after paving in real time.

[0012] The term "paved asphalt mixture" refers to a mixture of asphalt and aggregate that has been evenly laid on the subgrade or base course by a paver but has not yet been compacted. For example, in the construction of the asphalt surface layer of a highway from K10+000 to K10+200, the paver evenly spreads AC-13 asphalt mixture on the base course to form a loose layer; this portion of the mixture is called the paved asphalt mixture. Temperature distribution data refers to a set of parameters characterizing the temperature values ​​at different spatial locations on or within the paved layer. For example, infrared temperature sensors installed every 0.5m along the paving width continuously scan behind the paver, obtaining temperature values ​​of 162℃, 158℃, and 160℃ at various transverse points at the K10+100 section; these data constitute the temperature distribution data for that section. Thickness distribution data refers to the set of parameters that characterize the loose thickness of the paving layer at different spatial locations. For example, a microwave thickness sensor installed at the same location as the temperature sensor measures thickness values ​​of 4.2cm, 4.1cm, and 4.0cm at various points in the K10+100 section. These data constitute the thickness distribution data of that section.

[0013] The paving quality analysis module 120 is used to perform spatial registration and fusion processing on the temperature distribution data and thickness distribution data to generate a comprehensive quality index field that characterizes the compaction suitability of different spatial locations of the paving layer.

[0014] The paving layer refers to the uncompacted pavement structure layer formed by the paved asphalt mixture. For example, in the section from K10+000 to K10+200, the AC-13 asphalt mixture laid by the paver forms a continuous and uniform paving layer with a width of 11.25m and a loose thickness of about 4.2cm.

[0015] Compaction suitability refers to the degree to which the current state of the paved layer (temperature, thickness, etc.) meets the requirements of the compaction process, reflecting the ease or difficulty of achieving the expected compaction effect under this state. For example, the temperature at the center point of section K10+100 is 162℃ and the thickness is 4.2cm, both within the suitable range, indicating good compaction suitability at this point. The temperature at the edge point is 148℃ and the thickness is 3.9cm, indicating poor compaction suitability. The comprehensive quality index field refers to a two-dimensional spatial distribution data field composed of the comprehensive quality indices of various spatial locations within the paved layer. For example, arranging the comprehensive quality indices of all grid points (e.g., one point every 0.5m × 0.5m) within the section from K10+000 to K10+200 according to spatial coordinates forms a comprehensive quality index field covering the entire construction section. The index at the center point of section K10+100 is 0.95, and the index at the edge point is 0.70.

[0016] The compaction parameter decision module 130 is used to dynamically determine the number of compaction passes and the rolling speed of the road roller based on the difference between the comprehensive quality index field and the preset compaction target, using a pre-constructed nonlinear mapping model, wherein the number of compaction passes and the rolling speed are continuous control parameters that change with the spatial position of the paving layer.

[0017] The compaction target refers to the standard value of density that the paved layer should achieve after compaction, as set according to pavement design specifications or construction requirements. For example, the compaction target for the asphalt surface layer in this section is 98%, which means that the compaction density of the mixture after compaction should reach 98% of the maximum theoretical density. A nonlinear mapping model refers to a mathematical function or algorithm that describes the nonlinear relationship between input variables (such as the difference between the comprehensive quality index field and the compaction target) and output variables (such as the number of compaction passes and rolling speed). For example, a pre-constructed mathematical model containing an S-shaped growth function and an exponential decay function, when the input difference value is 0.03, calculates the output compaction pass count as 6 passes and the rolling speed as 4.5 km / h.

[0018] Here, a road roller refers to construction machinery used to compact asphalt mixtures after paving; for example, a double-drum vibratory roller deployed on a construction site is responsible for compaction work on the section from K10+000 to K10+200. The number of compaction passes refers to the number of times the road roller reciprocates at the same spatial location; for example, at the center point of the K10+100 section, the road roller needs to roll back and forth 6 times to achieve the required density; these 6 times are called the number of compaction passes at that point. The rolling speed refers to the speed at which the road roller travels during compaction work; for example, near the K10+100 section, the road roller travels at a constant speed of 4.5 km / h; this speed is called the rolling speed at that location.

[0019] The compaction control execution module 140 is used to convert the number of compaction passes and the rolling speed into control commands for the roller actuator.

[0020] The actuator of a road roller refers to the mechanical device on the road roller used to achieve actions such as walking and vibration; for example, it includes a vibrating steel wheel, a travel motor, and a vibrating pump, where the vibrating steel wheel is responsible for generating the excitation force, and the travel motor drives the road roller to move. Control commands refer to identifiable signals generated by the control system and sent to the actuator of the road roller, used to drive the actuator to act according to predetermined parameters; for example, voltage signals or CAN bus data frames containing parameters such as vibration frequency, amplitude, and travel speed, controlling the vibrating steel wheel to vibrate at a frequency of 42Hz and the travel motor to rotate at a speed of 1200r / min.

[0021] The technical solution of this embodiment collects real-time data on the temperature and thickness distribution of the paving layer and performs spatial registration and fusion processing to generate a comprehensive quality index field. Then, based on a nonlinear mapping model, it dynamically determines the number of compaction passes and the rolling speed that change with spatial location, and converts the above parameters into control commands for the roller actuator. This solves the problems of data interaction loss and lack of coordination mechanism caused by the independent operation of paving and compaction processes, and the inability to dynamically adjust compaction parameters according to the actual paving conditions. It achieves continuous and precise control of the number of compaction passes and the rolling speed as the paving layer changes with spatial location, and improves the uniformity of compaction and smoothness of asphalt pavement.

[0022] In an alternative embodiment, the paving layer status acquisition module 110 is specifically used for: Multiple infrared temperature sensors are arranged at transverse intervals behind the paver. Each infrared temperature sensor performs a longitudinal continuous scan of the paved asphalt mixture to obtain temperature distribution data covering the entire width of the paved layer.

[0023] Among them, a paver refers to engineering machinery used to evenly spread asphalt mixture on the road base layer; for example, a tracked paver is responsible for spreading the AC-13 mixture delivered to the site to the designed width and thickness. An infrared temperature sensor refers to a device that uses the principle of infrared radiation to non-contactly measure the surface temperature of an object; for example, infrared thermometers installed on the crossbeam at the rear of the paver, one every 0.5m, monitor the surface temperature of the paved layer in real time. Longitudinal continuous scanning refers to the sensor continuously and uninterruptedly measuring the paved layer along the direction of the paver's movement; for example, as the paver moves forward at a speed of 3m / min, the infrared temperature sensor collects data 10 times per second, forming a continuous temperature trajectory along the longitudinal direction of the road.

[0024] Multiple microwave thickness sensors are installed at the same lateral position as the infrared temperature sensor behind the paver. Each microwave thickness sensor emits a microwave signal to the paved asphalt mixture and receives the reflected signal. The thickness distribution data at the corresponding longitudinal position is calculated based on the phase difference of the reflected signal.

[0025] Among them, microwave thickness sensors refer to devices that measure the thickness of paving layers using the principle of microwave reflection; for example, a microwave thickness gauge installed at the same position as an infrared sensor behind the paver calculates the distance from the sensor to the base layer by transmitting and receiving microwave signals, thereby obtaining the thickness of the paving layer.

[0026] Microwave signal refers to the electromagnetic wave emitted by a microwave thickness sensor for measuring distance; for example, the sensor emits a 24GHz continuous frequency modulated microwave towards the road surface, which penetrates the paving layer and is reflected back to the receiving antenna by the base layer. Reflected signal refers to the electromagnetic wave reflected back to the sensor after the microwave signal encounters the base layer interface; for example, the microwave signal reflected back from the base layer is received by the sensor, and its frequency changes compared to the emitted signal. The microwave propagation distance can be calculated by analyzing the frequency difference. Longitudinal position refers to the spatial coordinates along the road's forward direction (paving direction); for example, starting from K10+000, a longitudinal position is marked every 10m forward, such as K10+010, K10+020, etc., to identify the spatial positions of different cross-sections.

[0027] Add timestamps and spatial location identifiers to the temperature distribution data and thickness distribution data respectively to generate temperature distribution data and thickness distribution data with the same spatial coordinate reference system.

[0028] The timestamp refers to the time information that identifies the moment the data was collected; for example, 10:25:30 AM on January 1, 2026, this timestamp, associated with the temperature and thickness data of section K10+100, indicates that the data was collected at that moment. The spatial location identifier refers to the marking information used to uniquely determine the spatial coordinates of a point on the paving layer; for example, for the center point of section K10+100, its spatial location identifier is "station number K10+100 + lateral distance 5.625m", where 5.625m is the distance from the road centerline.

[0029] In the above-mentioned optional methods, infrared temperature sensors and microwave thickness sensors are further deployed at transverse intervals behind the paver. Temperature distribution data and thickness distribution data covering the entire width of the paving layer are obtained through continuous longitudinal scanning. Timestamps and spatial location identifiers are added to establish a unified spatial coordinate reference system, which solves the problems of inconsistent spatial reference and lack of spatiotemporal correlation during the temperature and thickness data acquisition process, and improves the spatial registration accuracy and temporal consistency of multi-source data.

[0030] In an alternative embodiment, the paving quality analysis module 120 is specifically used for: Based on the timestamp and the spatial location identifier, the temperature distribution data and the thickness distribution data are spatiotemporally registered in a unified spatial coordinate reference system to establish a mapping relationship between the temperature value and the thickness value corresponding to each spatial location point.

[0031] The mapping relationship refers to the corresponding relationship between different attribute data at the same spatial location point. For example, at the center point of the K10+100 section, the temperature value of 162℃ and the thickness value of 4.2cm are mapped through the spatial location identifier, that is, the point has two attributes at the same time: temperature of 162℃ and thickness of 4.2cm.

[0032] For each spatial location, a temperature suitability coefficient is calculated based on the deviation of the temperature value at that location from the preset suitable compaction temperature range. At the same time, a thickness suitability coefficient is calculated based on the deviation of the thickness value at that location from the preset target compaction thickness.

[0033] The suitable compaction temperature range refers to the temperature range of the paving layer that achieves good compaction results for a specific asphalt mixture. For example, the suitable compaction temperature range for AC-13 asphalt mixture is 150℃~165℃. Below 150℃, the mixture is too hard to compact, while above 165℃, it may cause asphalt aging. The temperature suitability coefficient is a dimensionless index calculated based on the degree of matching between the actual temperature of the paving layer and the suitable compaction temperature range. For example, the temperature at the center point of section K10+100 is 162℃, which is within the suitable range, and the calculated temperature suitability coefficient is 1.0; the temperature at the edge point is 148℃, which is below the lower limit, and the calculated temperature suitability coefficient is 0.6.

[0034] The target compacted thickness refers to the thickness that the pavement structure layer should achieve after compaction, as required by the design. For example, if the target compacted thickness of the asphalt surface layer is 4cm, then the pavement thickness after rolling should be 4cm. The thickness suitability coefficient is a dimensionless index calculated based on the degree of matching between the actual loose paving thickness and the target compacted thickness. For example, if the loose paving thickness at the center point of section K10+100 is 4.2cm, and it is expected to reach 4cm after compaction, the calculated thickness suitability coefficient is 0.95; if the loose paving thickness at the edge point is 3.9cm, then the thickness suitability coefficient is 0.80.

[0035] The temperature suitability coefficient and the thickness suitability coefficient are weighted and fused to generate a comprehensive quality index characterizing the compaction suitability of the spatial location point. The comprehensive quality indices of all spatial locations constitute the comprehensive quality index field of the paving layer at different spatial locations.

[0036] The comprehensive quality index refers to a single quantitative value that characterizes the compaction suitability of a certain spatial location after comprehensively considering temperature suitability and thickness suitability. For example, the temperature suitability coefficient of the center point of the K10+100 section is 1.0, the thickness suitability coefficient is 0.95, and the comprehensive quality index after weighted fusion is 0.97.

[0037] In the above-mentioned optional methods, the temperature distribution data and thickness distribution data are further spatially registered according to the timestamp and spatial location identifier to establish the mapping relationship between the temperature value and the thickness value at each spatial location point. Based on the weighted fusion of the temperature suitability coefficient and the thickness suitability coefficient, a comprehensive quality index field is generated to solve the problem of incomplete compaction suitability judgment caused by isolated evaluation of temperature and thickness parameters, and realize the spatial continuous quantitative characterization of the compaction suitability of the paving layer.

[0038] In an alternative embodiment, the paving quality analysis module 120 is specifically used for: For each spatial location, obtain the temperature and thickness values ​​for that location.

[0039] Based on the temperature value at the spatial location point and the lower and upper limits of the preset suitable compaction temperature range, and taking into account the modulation effect of the thickness value at the spatial location point on the temperature attenuation characteristics, the temperature suitability coefficient of the spatial location point is calculated using the first nonlinear coupling function.

[0040] Among them, temperature decay characteristics refer to the change law of the temperature of the paving layer decreasing over time; for example, at an ambient temperature of 15℃, the temperature drops by 5℃ after 10 minutes and by 12℃ after 20 minutes. This rate and magnitude of cooling is called temperature decay characteristics.

[0041] Wherein, the first nonlinear coupling function is:

[0042]

[0043] In the formula, Indicates the first Temperature suitability coefficient at a spatial location point Indicates the first Temperature values ​​at spatial locations Indicates the first The thickness value at each spatial location point and These represent the lower and upper limits of the preset suitable compaction temperature range, respectively. This indicates the center value of the suitable compaction temperature range. This indicates the preset target compaction thickness. Indicates the base temperature half-width factor. This represents the thickness-modulated adaptive temperature half-width factor. This indicates a penalty factor for thickness deviation from temperature suitability. This represents the expansion factor indicating the thickness deviation from the temperature tolerance. This represents a very small constant that prevents division by zero.

[0044] In the above-mentioned optional methods, the modulation effect of thickness value on temperature decay characteristics is further introduced when calculating the temperature suitability coefficient. The temperature half-width coefficient is adaptively adjusted with the degree of thickness deviation through the first nonlinear coupling function, which solves the problem that the accuracy of temperature suitability assessment is limited due to the failure to consider the influence of thickness coupling, and improves the adaptability of temperature assessment to changes in paving layer thickness.

[0045] In an alternative embodiment, the paving quality analysis module 120 is specifically used for: Based on the thickness value at the spatial location and the preset target compaction thickness, and taking into account the modulation effect of the temperature value at the spatial location on the thickness deviation tolerance, the thickness suitability coefficient at the spatial location is calculated using a second nonlinear coupling function.

[0046] Thickness deviation tolerance refers to the sensitivity or allowable range of compaction parameters to thickness deviation from the target value. For example, when the thickness deviation is small, the adjustment range of compaction parameters is small; when the thickness deviation is large, the number of compaction passes needs to be significantly adjusted. The tolerance of this adjustment is called thickness deviation tolerance.

[0047] Wherein, the second nonlinear coupling function is:

[0048]

[0049] In the formula, Indicates the first Thickness suitability coefficient at each spatial location point This indicates the tolerance for thickness deviation in the base layer. This represents the thickness deviation tolerance modulation factor as it varies with temperature. and These represent the minimum and maximum values ​​of the thickness deviation tolerance modulation factor, respectively. This represents the enhancement factor of temperature on the suitability of thickness.

[0050] In the above-mentioned optional methods, the modulation effect of temperature value on thickness deviation tolerance is further introduced when calculating the thickness suitability coefficient. The thickness deviation tolerance is dynamically adjusted with temperature change through the second nonlinear coupling function, which solves the problem of insufficient evaluation accuracy caused by not considering the temperature coupling effect in the thickness suitability assessment, and optimizes the adaptability of thickness assessment to different temperature conditions.

[0051] In an alternative embodiment, the compaction parameter decision module 130 is specifically used for: Obtain the comprehensive quality index of each spatial location point in the comprehensive quality index field.

[0052] The difference value for each spatial location is calculated based on the difference between the comprehensive quality index of each spatial location and the target index corresponding to the compaction target.

[0053] The target index refers to the benchmark value of the comprehensive quality index corresponding to the preset compaction target. For example, when the compaction target is 98%, the corresponding target index is determined to be 0.95 through calibration, meaning that the comprehensive quality index at each point after compaction should not be lower than 0.95. The difference value refers to the difference between the comprehensive quality index and the target index at a certain spatial location. For example, if the comprehensive quality index at the center point of section K10+100 is 0.97 and the target index is 0.95, the difference value is +0.02; if the comprehensive quality index at the edge point is 0.70, the difference value is -0.25.

[0054] The difference value of each spatial location point is input into a pre-constructed nonlinear mapping model. The number of compaction passes and the rolling speed of each spatial location point are calculated through the nonlinear mapping model. The nonlinear mapping model contains a combination of an S-shaped growth function and an exponential decay function, such that the number of compaction passes increases in an S-shape with the increase of the difference value and is modulated by exponential decay, and the rolling speed decreases exponentially with the increase of the difference value and is affected by a fluctuation suppression term.

[0055] Dynamic constraints are applied to the product of the number of compaction passes and the rolling speed based on the difference value of each spatial location point, so that the number of compaction passes and the rolling speed satisfy a preset energy matching relationship.

[0056] Among them, the energy matching relationship refers to the constraint condition that the product of the number of compaction passes and the rolling speed (characterizing compaction energy) must meet in order to ensure compaction quality; for example, the preset energy matching relationship is that the number of compaction passes × the rolling speed should be approximately 24 (passes·km / h), and when the difference value is 0.02, the allowable fluctuation range is ±5%.

[0057] The number of compaction passes and rolling speed at all spatial locations are calculated and output as continuous control parameters that vary with the spatial location of the paving layer.

[0058] In the above-mentioned optional methods, the number of compaction passes and the rolling speed are further determined dynamically through a nonlinear mapping model based on the difference between the comprehensive quality index and the compaction target. Dynamic constraints are applied to the product of the number of compaction passes and the rolling speed to satisfy the energy matching relationship, thereby solving the problem of lack of adaptive adjustment and energy synergistic optimization in compaction parameter decision-making and realizing the synergistic optimization of the number of compaction passes and the rolling speed as the spatial location changes.

[0059] In one alternative approach, the nonlinear mapping model is represented as:

[0060]

[0061]

[0062] In the formula, Indicates the first Number of compaction passes at each spatial location point Indicates the first The crushing speed at a spatial location point and These represent the preset minimum and maximum number of compaction passes, respectively. and These represent the preset minimum and maximum compaction speeds, respectively. Indicates the first The difference value of each spatial location point This represents the difference value corresponding to the median point of the S-shaped growth curve, indicating the number of compaction passes. The characteristic scale representing the exponential decay of compaction speed, The optimal difference value representing the energy matching relationship. The width coefficient representing the energy matching relationship. The growth rate coefficient of the S-shaped curve representing the number of compaction passes is... This represents the magnitude coefficient of the exponential decay due to the number of compaction passes. The rate coefficient representing the exponential decay of compaction speed. This represents the amplitude coefficient for suppressing rolling speed fluctuations. This represents the preset target compaction energy. This represents the modulation coefficient for energy matching.

[0063] In the above-mentioned optional methods, a nonlinear mapping model that combines an S-shaped growth function and an exponential decay function is further used to make the number of compaction passes exhibit an S-shaped growth with the increase of the difference value and be modulated by exponential decay, while the rolling speed exhibits an exponential decay with the increase of the difference value. This solves the problem of the rigid mapping relationship between compaction parameters and quality differences, and improves the accuracy and adaptability of compaction energy control.

[0064] In an alternative embodiment, the compaction control execution module 140 is specifically used for: Obtain the number of compaction passes and the rolling speed at each spatial location point.

[0065] The relationship between the number of vibrations and the travel distance of the roller at each spatial location is determined based on the number of compaction passes, and the number of vibrations is converted into the opening and closing timing control signal of the roller's vibration mechanism.

[0066] The correspondence refers to the conversion relationship between the number of compaction passes, the number of vibrations of the roller, and the travel distance. For example, 6 compaction passes means that the roller needs to travel back and forth 3 times at that position, vibrating once each time it moves forward and backward, for a total of 6 vibrations, and the travel distance is 3 times the width of the paving layer. The start-stop sequence control signal refers to the electrical signal that controls the roller's vibration mechanism to start or stop in a predetermined time sequence. For example, when the roller travels to the K10+100 section, the control system sends a high-level signal to start vibration, and sends a low-level signal to stop vibration after traveling out of the section, forming a series of pulse signals.

[0067] The driving speed of the roller's traveling motor is calculated based on the compaction speed, and the driving speed is converted into a speed adjustment signal for the roller's traveling mechanism.

[0068] The roller travel motor refers to the hydraulic or electric motor that drives the roller. For example, the hydraulic motor on the rear wheel of the roller controls the motor speed by adjusting the hydraulic oil flow, thereby changing the travel speed. The drive speed refers to the number of revolutions per minute (rpm) of the roller travel motor. For example, when a compaction speed of 4.5 km / h is required, the calculated travel motor drive speed should be 1200 r / min. The speed regulation signal refers to the command signal that controls the travel motor speed, usually an analog voltage or current signal. For example, the controller outputs a 4-20mA current signal to the hydraulic proportional valve, and the valve opening changes with the current, thus regulating the motor speed. The traveling mechanism of a road roller refers to the mechanical transmission system used to drive the entire machine, including the traveling motor, reducer, drive wheels, and related hydraulic or electrical control components. For example, during the construction of the section from K10+000 to K10+200, the traveling mechanism of the road roller consists of a hydraulic traveling motor, a planetary reducer, and a rear steel wheel. When the control system sends a speed adjustment signal, the traveling motor drives the road roller to move forward at a constant speed of 4.5 km / h.

[0069] Based on the spatial location identifier of each spatial location point, trajectory planning data for the roller's travel path is generated, and the trajectory planning data is synchronously associated with the opening and closing timing control signal and the speed adjustment signal to generate the control command containing position coordinates, vibration status, and travel speed.

[0070] The roller's travel path refers to the planned trajectory of the roller on the paving layer. For example, based on the paving layer width and compaction pass requirements, the roller is planned to travel longitudinally from K10+000 to K10+200 three times, with each pass laterally offset by half a wheel width, forming a zigzag path. The trajectory planning data refers to the sequence of coordinate points and motion parameters describing the roller's travel path; for example, it includes the starting coordinates (K10+000, 0m), the ending coordinates (K10+200, 0m), the coordinates of each turnaround point, and the travel speed for each segment of the path.

[0071] The location coordinates refer to the quantified position of a point on the paving layer within a preset coordinate system. For example, with K10+000 as the origin, the longitudinal direction of the road as the X-axis, and the transverse direction as the Y-axis, the location coordinates of the center point of the K10+100 section are (100m, 5.625m). Vibration state refers to the current operating mode of the roller's vibration mechanism, including vibration frequency, amplitude, and whether vibration is on or off. For example, at the K10+100 section, the vibration state is "high-frequency low-amplitude vibration on," with a frequency of 42Hz and an amplitude of 0.4mm. Travel speed refers to the current speed of the roller, usually expressed in km / h or m / min. For example, in the section from K10+000 to K10+100, the roller's travel speed is 4.5 km / h.

[0072] In the above-mentioned optional methods, the correspondence between the number of vibrations and the travel distance is further determined based on the number of compaction passes and converted into a vibration mechanism start-stop timing control signal. The travel motor drive speed is calculated based on the rolling speed and converted into a speed adjustment signal. At the same time, the trajectory planning data is associated to generate control commands, which solves the problem of the compaction control command being disconnected from the spatial position and the action of the actuator, and improves the spatial positioning accuracy and action synchronization of the roller actuator.

[0073] In an alternative embodiment, the system further includes: The anomaly diagnosis module is used to monitor the temperature distribution data and the thickness distribution data in real time. When the temperature value of any spatial location point exceeds the preset safe temperature range or the thickness value exceeds the preset safe thickness range, an anomaly location identifier and an anomaly type identifier are generated.

[0074] The safe temperature range refers to the allowable temperature interval set to ensure the performance of asphalt mixtures and construction safety. For example, for AC-13 mixtures, the safe temperature range is 140℃~180℃. Temperatures below 140℃ may not allow for sufficient compaction, while temperatures above 180℃ may cause asphalt aging or even ignition. The safe thickness range refers to the allowable loose paving thickness range set to ensure compaction effect and interlayer bonding. For example, if the designed loose paving thickness for this road section is 4cm, the safe thickness range is 3.5cm~4.5cm. Exceeding this range may result in insufficient compaction or shoving cracking.

[0075] The anomaly location identifier refers to the information used to mark the spatial location of the anomaly when abnormal data is detected. For example, if the temperature at the edge point of section K10+100 exceeds the safe range, the system records the anomaly location identifier for that point as "station number K10+100 + lateral distance 0m". The anomaly type identifier refers to the marking information used to distinguish the type of anomaly, such as temperature anomaly or thickness anomaly. For example, the above point is marked as "excessively high temperature anomaly", coded as "T_HIGH".

[0076] The alarm output module is used to generate an alarm signal based on the abnormal location identifier and the abnormal type identifier, and send the alarm signal to the paver control terminal and the roller control terminal.

[0077] Alarm signals refer to signals generated by the system when it detects an anomaly, used to alert the operator; for example, a red warning box pops up on the cab display screen, accompanied by a buzzer sound, displaying "Temperature too high at the edge point of section K10+100". The paver control terminal refers to the human-machine interface device installed on the paver for displaying information and receiving instructions; for example, a tablet computer in the paver cab that displays paving speed, temperature data, and alarm information in real time. The roller control terminal refers to the human-machine interface device installed on the roller for displaying information and receiving instructions; for example, an LCD screen on the roller's control panel that receives instructions from the control system regarding compaction passes and rolling speed, and displays them to the operator.

[0078] In the above-mentioned optional methods, by further monitoring temperature distribution data and thickness distribution data in real time, when the temperature value or thickness value is detected to exceed the safe range, an abnormal location identifier and an abnormality type identifier are generated and an alarm is triggered. This solves the problem of quality abnormalities not being detected and located in a timely manner during construction, and improves the safety early warning capability and quality controllability of the construction process.

[0079] Figure 2 This diagram illustrates a flowchart of an embodiment of an integrated intelligent asphalt paving and compaction control method for highways provided by the present invention. This integrated intelligent asphalt paving and compaction control method employs the integrated intelligent asphalt paving and compaction control system 100 provided by the present invention. Figure 2 As shown, the method includes the following steps: S1. Real-time acquisition of temperature and thickness distribution data of the asphalt mixture after paving; S2. Spatial registration and fusion processing are performed on the temperature distribution data and thickness distribution data to generate a comprehensive quality index field characterizing the compaction suitability of different spatial locations of the paving layer; S3. Based on the difference between the comprehensive quality index field and the preset compaction target, the number of compaction passes and the rolling speed of the road roller are dynamically determined using a pre-constructed nonlinear mapping model, wherein the number of compaction passes and the rolling speed are continuous control parameters that vary with the spatial position of the paving layer. S4. Convert the number of compaction passes and the rolling speed into control commands for the roller actuator.

[0080] The technical solution of this embodiment collects real-time data on the temperature and thickness distribution of the paving layer and performs spatial registration and fusion processing to generate a comprehensive quality index field. Then, based on a nonlinear mapping model, it dynamically determines the number of compaction passes and the rolling speed that change with spatial location, and converts the above parameters into control commands for the roller actuator. This solves the problems of data interaction loss and lack of coordination mechanism caused by the independent operation of paving and compaction processes, and the inability to dynamically adjust compaction parameters according to the actual paving conditions. It achieves continuous and precise control of the number of compaction passes and the rolling speed as the paving layer changes with spatial location, and improves the uniformity of compaction and smoothness of asphalt pavement.

[0081] The methods and system embodiments provided above belong to the same concept, and their specific implementation process can be found in the system embodiments, which will not be repeated here.

[0082] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0083] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0084] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A smart asphalt paving and compaction integrated control system for highways, characterized in that, The system includes: The paving layer status acquisition module is used to collect temperature distribution data and thickness distribution data of the asphalt mixture after paving in real time; The paving quality analysis module is used to perform spatial registration and fusion processing on the temperature distribution data and thickness distribution data to generate a comprehensive quality index field that characterizes the compaction suitability of different spatial locations of the paving layer. The compaction parameter decision module is used to dynamically determine the number of compaction passes and the rolling speed of the road roller based on the difference between the comprehensive quality index field and the preset compaction target using a pre-constructed nonlinear mapping model, wherein the number of compaction passes and the rolling speed are continuous control parameters that vary with the spatial position of the paving layer. The compaction control execution module is used to convert the number of compaction passes and the rolling speed into control commands for the roller actuator.

2. The integrated intelligent asphalt paving and compaction control system for highways according to claim 1, characterized in that, The paving layer status acquisition module is specifically used for: Multiple infrared temperature sensors are arranged at transverse intervals behind the paver. Each infrared temperature sensor performs a longitudinal continuous scan of the paved asphalt mixture to obtain temperature distribution data covering the entire width of the paved layer. Multiple microwave thickness sensors are installed at the same lateral position as the infrared temperature sensor behind the paver. Each microwave thickness sensor emits a microwave signal to the paved asphalt mixture and receives the reflected signal. The thickness distribution data at the corresponding longitudinal position is calculated based on the phase difference of the reflected signal. Add timestamps and spatial location identifiers to the temperature distribution data and thickness distribution data respectively to generate temperature distribution data and thickness distribution data with the same spatial coordinate reference system.

3. The integrated intelligent asphalt paving and compaction control system for highways according to claim 2, characterized in that, The paving quality analysis module is specifically used for: Based on the timestamp and the spatial location identifier, the temperature distribution data and the thickness distribution data are spatiotemporally registered in a unified spatial coordinate reference system to establish a mapping relationship between the temperature value and the thickness value corresponding to each spatial location point. For each spatial location, a temperature suitability coefficient is calculated based on the degree of deviation between the temperature value at that location and the preset suitable compaction temperature range. At the same time, a thickness suitability coefficient is calculated based on the degree of deviation between the thickness value at that location and the preset target compaction thickness. The temperature suitability coefficient and the thickness suitability coefficient are weighted and fused to generate a comprehensive quality index characterizing the compaction suitability of the spatial location point. The comprehensive quality indices of all spatial locations constitute the comprehensive quality index field of the paving layer at different spatial locations.

4. The integrated intelligent asphalt paving and compaction control system for highways according to claim 3, characterized in that, The paving quality analysis module is specifically used for: For each spatial location, obtain the temperature and thickness values ​​for that location. Based on the temperature value at the spatial location point and the lower and upper limits of the preset suitable compaction temperature range, and taking into account the modulation effect of the thickness value at the spatial location point on the temperature attenuation characteristics, the temperature suitability coefficient of the spatial location point is calculated using the first nonlinear coupling function. Wherein, the first nonlinear coupling function is: , In the formula, Indicates the first Temperature suitability coefficient at a spatial location point Indicates the first Temperature values ​​at spatial locations Indicates the first Thickness value at each spatial location point and These represent the lower and upper limits of the preset suitable compaction temperature range, respectively. This indicates the center value of the suitable compaction temperature range. This indicates the preset target compaction thickness. Indicates the base temperature half-width factor. This represents the thickness-modulated adaptive temperature half-width factor. This indicates a penalty factor for thickness deviation from temperature suitability. This represents the expansion factor indicating the thickness deviation from the temperature tolerance. This represents a very small constant that prevents division by zero.

5. The integrated intelligent asphalt paving and compaction control system for highways according to claim 4, characterized in that, The paving quality analysis module is specifically used for: Based on the thickness value at the spatial location point and the preset target compaction thickness, and taking into account the modulation effect of the temperature value at the spatial location point on the thickness deviation tolerance, the thickness suitability coefficient at the spatial location point is calculated using the second nonlinear coupling function. The second nonlinear coupling function is: , In the formula, Indicates the first Thickness suitability coefficient at each spatial location point This indicates the tolerance for thickness deviation in the base layer. This represents the thickness deviation tolerance modulation factor as it varies with temperature. and These represent the minimum and maximum values ​​of the thickness deviation tolerance modulation factor, respectively. This represents the enhancement factor of temperature on the suitability of thickness.

6. The integrated intelligent asphalt paving and compaction control system for highways according to claim 5, characterized in that, The compaction parameter decision module is specifically used for: Obtain the comprehensive quality index of each spatial location point in the comprehensive quality index field; The difference value for each spatial location is calculated based on the difference between the comprehensive quality index of each spatial location and the target index corresponding to the compaction target. The difference value of each spatial location point is input into a pre-constructed nonlinear mapping model. The number of compaction passes and the rolling speed of each spatial location point are calculated through the nonlinear mapping model. The nonlinear mapping model contains a combination of an S-shaped growth function and an exponential decay function, so that the number of compaction passes increases in an S-shape with the increase of the difference value and is modulated by exponential decay, and the rolling speed decreases exponentially with the increase of the difference value and is affected by a fluctuation suppression term. Based on the difference value of each spatial location point, a dynamic constraint is applied to the product of the corresponding number of compaction passes and the rolling speed, so that the number of compaction passes and the rolling speed satisfy a preset energy matching relationship; The number of compaction passes and rolling speed at all spatial locations are calculated and output as continuous control parameters that vary with the spatial location of the paving layer.

7. The integrated intelligent asphalt paving and compaction control system for highways according to claim 6, characterized in that, The nonlinear mapping model is expressed as follows: ， ， In the formula, Indicates the first Number of compaction passes at each spatial location point Indicates the first The crushing speed at a spatial location point and These represent the preset minimum and maximum number of compaction passes, respectively. and These represent the preset minimum and maximum compaction speeds, respectively. Indicates the first The difference value of each spatial location point This represents the difference value corresponding to the median point of the S-shaped growth curve, indicating the number of compaction passes. The characteristic scale representing the exponential decay of compaction speed, The optimal difference value representing the energy matching relationship. The width coefficient representing the energy matching relationship. The growth rate coefficient of the S-shaped curve represents the number of compaction passes. This represents the magnitude coefficient of the exponential decay due to the number of compaction passes. The rate coefficient representing the exponential decay of compaction speed. This represents the amplitude coefficient for suppressing rolling speed fluctuations. This represents the preset target compaction energy. This represents the modulation coefficient for energy matching.

8. The integrated intelligent asphalt paving and compaction control system for highways according to claim 7, characterized in that, The compaction control execution module is specifically used for: Obtain the number of compaction passes and the compaction speed at each spatial location point; The relationship between the number of vibrations and the travel distance of the road roller at each spatial location is determined based on the number of compaction passes, and the number of vibrations is converted into the opening and closing timing control signal of the road roller's vibration mechanism; The driving speed of the roller's travel motor is calculated based on the compaction speed, and the driving speed is converted into a speed adjustment signal for the roller's travel mechanism. Based on the spatial location identifier of each spatial location point, trajectory planning data for the roller's travel path is generated, and the trajectory planning data is synchronously associated with the opening and closing timing control signal and the speed adjustment signal to generate the control command containing position coordinates, vibration status, and travel speed.

9. The integrated intelligent asphalt paving and compaction control system for highways according to any one of claims 1 to 8, characterized in that, The system also includes: Anomaly diagnosis module is used to monitor the temperature distribution data and the thickness distribution data in real time. When the temperature value of any spatial location point exceeds the preset safe temperature range or the thickness value exceeds the preset safe thickness range, an anomaly location identifier and an anomaly type identifier are generated. The alarm output module is used to generate an alarm signal based on the abnormal location identifier and the abnormal type identifier, and send the alarm signal to the paver control terminal and the roller control terminal.

10. A method for integrated control of intelligent asphalt paving and compaction on highways, employing the integrated control system for intelligent asphalt paving and compaction as described in any one of claims 1 to 9, characterized in that, The method includes: Real-time acquisition of temperature and thickness distribution data of asphalt mixture after paving; Spatial registration and fusion processing are performed on the temperature distribution data and thickness distribution data to generate a comprehensive quality index field characterizing the compaction suitability of different spatial locations of the paving layer; Based on the difference between the comprehensive quality index field and the preset compaction target, a pre-constructed nonlinear mapping model is used to dynamically determine the number of compaction passes and the rolling speed of the road roller, wherein the number of compaction passes and the rolling speed are continuous control parameters that change with the spatial position of the paving layer; the number of compaction passes and the rolling speed are converted into control commands for the road roller actuator.

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