Real-time measurement method for virtual paving thickness of asphalt paver based on thickness metering

Abstract

The present application relates to the field of mechanical engineering, disclose the asphalt paver virtual paving thickness real-time measurement method based on thickness measurement, the method is through synchronous deployment laser ranging array, contact pressure sensing array and temperature sensor, real-time collection loose paving material surface height, local contact pressure and temperature data;Embedded processing unit according to the stiffness mapping table of pre-marking, dynamic calculation material instantaneous stiffness and correct geometric ranging value, output compensated virtual paving thickness. The system supports multi-grade adaptive switching and sensor fault degradation output, ensure data continuous and reliable. The present application improves the stability of thickness measurement and process control accuracy, enhances the robustness of equipment and engineering applicability through multi-physical field coupling sensing and lightweight real-time compensation model.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of mechanical engineering, and particularly relates to a real-time measurement method for virtual paving thickness of an asphalt paver based on thickness measurement. BACKGROUND

[0002] In the modern road engineering construction system, asphalt concrete paving operation is a key process for forming a pavement structure, and its construction quality directly determines the service life, driving comfort and maintenance cost of the road. The virtual paving thickness, as a core process parameter for controlling the final thickness after compaction in the paving process, its measurement accuracy not only affects the uniformity of the interlayer structure, but also is closely related to the material usage control, cost accounting and engineering acceptance standard. Therefore, realizing high-precision, real-time and online measurement of the virtual paving thickness has become an important direction for the evolution of intelligent construction and maintenance equipment technology. The early offline detection method relying on manual insertion of a ruler or fixed-point sampling has been gradually replaced by an automatic sensing system integrated in the paver body, marking a fundamental change from experience-driven to data-driven construction control.

[0003] The current mainstream technical solution generally uses a single physical principle non-contact distance measuring sensor, such as an ultrasonic sensor or a laser displacement sensor, installed behind or on the side of the screed, to measure the vertical distance from the sensor to the top surface of the loose paving material, and indirectly calculate the virtual paving thickness by combining the known reference surface height. Such a solution has the advantages of rapid response, simple structure and no mechanical wear under ideal working conditions, and has played an important role in improving the level of construction automation. Its technical logic is based on the assumption that the geometric form of the loose paving surface can directly map the thickness parameter, and through high-frequency sampling, process monitoring is achieved, meeting the basic needs of early visualization and trend control. However, with the continuous improvement of high-grade highway construction standards, the increasing complexity of material formulations, and the stringent requirements of closed-loop precision for unmanned construction, the simplification premise implied in this technical path has presented insurmountable technical problems.

[0004] The essential defect of single geometric ranging means lies in its complete neglect of the dynamic disturbance of the measurement reference by the physical state of asphalt mixture as a non-homogeneous viscoelastic-plastic medium. Specifically, the temperature gradient of the mixture during paving, the variation of aggregate gradation, the fluctuation of asphalt binder content, and the difference in initial paving density jointly determine the local stiffness and energy absorption characteristics of the material surface layer. For example, under high temperature conditions, the viscosity of asphalt decreases, the loose layer shows soft characteristics, and the impedance matching changes when the ultrasonic wave penetrates, resulting in signal attenuation and phase shift. Laser ranging produces scattering errors due to surface micro-relief and thermal radiation interference; on the contrary, low temperature or high aggregate content mixture surface is rigid, and the sensor is easy to misjudge the local protrusions as the increase of the overall thickness. Further, different gradation designs (such as SMA and AC) have different mechanical responses to the same sensor excitation due to the difference in void ratio and skeleton structure, resulting in systematic deviation of the measurement value from the true compaction potential. This measurement drift caused by the material state is not random noise, but structural error with strong working condition correlation, and its amplitude often exceeds the engineering tolerance, leading to the adjustment of paving parameters by the control system based on distorted data, which further aggravates the thickness dispersion, forming a negative feedback cycle of measurement error—control failure—quality deterioration. SUMMARY

[0005] The present application aims to solve the technical problems of temperature, gradation, density and other working condition parameters interfering with the virtual paving thickness measurement value due to the single geometric ranging sensor being unable to perceive the material physical state in existing asphalt paving operations, and further causing the measurement reference to drift, the control to be inaccurate, and the compaction thickness dispersion to increase. The existing technology relies on ultrasonic or laser displacement sensors to obtain the spatial coordinates of the loose paving surface, and its measurement logic is based on the simplified assumption that the geometric form is equivalent to the compaction potential, ignoring the nonlinear difference in response to sensor excitation by asphalt mixture as a non-homogeneous viscoelastic-plastic medium under different physical states. The present application realizes the quantitative characterization of the instantaneous mechanical state of the material by constructing a multi-physical field synchronous perception architecture and a real-time stiffness compensation model, and dynamically corrects the geometric ranging results accordingly, thereby outputting stable, reliable and directly usable for compaction process closed-loop control virtual paving thickness data.

[0006] As an embodiment of the present application, the method is deployed in front of or to the side of the screed structure of an asphalt paver, including a non-contact ranging sensor array, a contact pressure sensor array, a temperature sensor module, an embedded data processing unit and a communication interface module.

[0007] The non-contact ranging sensor array is composed of three laser displacement sensors, which are arranged at equal intervals along the paving width direction, with the sensor installation height being 1.2 m from the theoretical reference surface, the measurement range being 0.3 m to 1.5 m, the repeatability being ±0.5 mm, and the sampling frequency being 500 Hz, which is used to obtain the absolute height data of the loose material top surface at multiple transverse positions.

[0008] The contact pressure sensor array is composed of 9 piezoresistive thin film pressure sensors in a 3x3 matrix structure, with an effective sensing area of 20mmx20mm, a range of 0 to 50KPa, a non-linear error of less than 1%, and a hysteresis of less than 0.5%. The sensor is installed at the end of the elastic floating pressure foot, with an initial compression of 5mm and a spring stiffness coefficient of 800N / m, ensuring constant light pressure contact between the sensor and the material surface without damaging the original structure of the material.

[0009] The temperature sensing module uses a platinum resistance temperature sensor installed at the outlet of the paver spiral distributor, with a temperature measurement range of 0 to 300℃, an accuracy of ±1℃, and a response time of less than 200ms, used to obtain the average temperature value of the mixture entering the paving area in real time.

[0010] Further, the embedded data processing unit is an industrial-grade ARM Cortex-A53 architecture processor with a main frequency of 1.2GHz, equipped with 512MB of running memory and 4GB of storage space, running a real-time operating system, and having multi-thread parallel processing capability.

[0011] The unit communicates with the paver main controller through the CAN bus interface to obtain process parameters such as paving speed, spiral speed, and vibration frequency; and synchronously collects the original data streams of the non-contact distance measuring sensor array, the contact pressure sensor array, and the temperature sensing module through the RS485 interface. The data collection period is set to 20ms, and all sensor data are equipped with hardware-level time stamps with a time synchronization error of less than 1ms.

[0012] Further, the embedded data processing unit executes a stiffness compensation algorithm model, which includes a data preprocessing submodule, a stiffness coefficient calculation submodule, a thickness correction submodule, and an output submodule. The data preprocessing submodule performs sliding average filtering on the original pressure data with a window length of 5 sampling points to eliminate transient impact interference; performs outlier rejection on the distance measuring data, identifies and replaces abnormal points using criteria; and performs zero-order hold interpolation on the temperature data to strictly align it with the pressure and distance measuring data on the time axis.

[0013] The stiffness coefficient calculation submodule calculates the stiffness coefficient based on the preprocessed pressure value and the temperature value , calls a pre-stored stiffness mapping table , which is established through offline calibration experiments, covering a temperature range of 120℃ to 180℃, a pressure range of 5KPa to 45KPa, and a stiffness coefficient Defined as the ranging correction caused by a unit pressure change, in mm / kPa, the mapping table is stored in embedded memory as a two-dimensional array with a resolution of 5°C for temperature and 2kPa for pressure. It is calculated in real-time using bilinear interpolation. Combination corresponding value.

[0014] The thickness correction submodule will convert the original non-contact ranging value With stiffness coefficient Multiply to obtain the correction amount Final output value of virtual piling thickness ,in A height compensation constant of 1.2m is installed in the system. The output submodule groups and packages the corrected thickness data according to the paver's lateral position and sends it to the paver's main controller via the CAN bus at a frequency of 50 frames per second. At the same time, it is uploaded to the cloud monitoring platform via the Ethernet interface.

[0015] Furthermore, the stiffness mapping table The offline calibration method is as follows: In a laboratory environment, standard-graded AC-13 asphalt mixture specimens were prepared, with the initial compaction degree controlled at 85%. Tests were conducted at four temperature points: 120℃, 140℃, 160℃, and 180℃. At each temperature point, the specimens were placed in a constant temperature chamber for 30 minutes, then moved to a testing platform. A pressure sensor array and distance sensor, consistent with those used in engineering projects, were installed on top. Five constant pressure levels of 5KPa, 15KPa, 25KPa, 35KPa, and 45KPa were applied to the specimen surface using a hydraulic loading device, with each pressure level maintained for 10 seconds. The pressure sensor output values ​​were recorded simultaneously. Distance sensor output value and infrared thermometer readings .

[0016] In each group Under these conditions, the actual thickness of the specimen was manually measured using a high-precision vernier caliper. Repeat the measurement three times and take the average value. Stiffness coefficient. Through formula Calculations show that for each The experiment was repeated 5 times, and the results were taken. The arithmetic mean of the values ​​is used as the calibration value for the corresponding cell in the mapping table. After calibration, the mapping table is permanently written to the embedded memory and cannot be modified during field operation.

[0017] Furthermore, the floating pressure foot structure of the contact pressure sensor array includes a guide sleeve, a compression spring, a pressure sensor base, and a wear-resistant pressure head. The guide sleeve has an inner diameter of 25mm, an outer diameter of 30mm, a length of 60mm, and is made of hard anodized aluminum alloy.

[0018] The compression spring has a free length of 50 mm, a working length of 45 mm after compression, a wire diameter of 2 mm, a middle diameter of 20 mm, and an effective number of turns of 8 turns. The base of the pressure sensor is a cylindrical stainless steel component with a diameter of 24 mm and a height of 10 mm, and a central hole for leading out the sensor signal line;

[0019] The wear-resistant pressure head is a hemispherical structure made of polytetrafluoroethylene, with a curvature radius of 10 mm and a surface roughness Ra of 0.8 μm.

[0020] The entire pressure foot is fixed on the ironing plate support beam by screw connection, and after installation, it is ensured that all the bottom surfaces of the pressure heads are on the same horizontal plane, with a height error of less than 0.1 mm.

[0021] Further, the laser emitter wavelength of the non-contact distance measuring sensor array is 650 nm, the spot diameter is 3 mm at a distance of 1 m, the protection level is IP67, the shell material is die-cast aluminum alloy, and it is installed through a universal adjusting support, which has a pitch angle adjustment capability of ± 15 degrees and a horizontal angle adjustment capability of ± 10 degrees. After installation, the light axis perpendicularity needs to be calibrated through a laser target to ensure that the angle between the measurement beam and the theoretical reference surface normal is less than 1 degree. The sensor power supply voltage is 24 V DC, the power consumption is less than 3 W, the signal output is 0 to 10 V analog voltage, and after sampling by a 16-bit analog-to-digital converter, it is input into an embedded processing unit.

[0022] Further, the embedded data processing unit performs a self-checking program when starting, including sensor communication link test, memory integrity check, and stiffness mapping table boundary value verification. If the pressure sensor zero drift is detected to be more than ± 0.5 KPa, or the distance measuring sensor reference value deviates from the factory calibration value by more than ± 1 mm, an alarm is triggered and the thickness output is paused until manual reset or replacement of the sensor. During system operation, the zero point value of each sensor is automatically recorded every 10 minutes and compared with the initial value. If the drift exceeds the threshold, software compensation is started, and the compensation coefficient is written to the non-volatile memory and applied in subsequent calculations.

[0023] Further, the method supports multi-grade material adaptive compensation by pre-storing independent mapping sub-tables of AC-13, SMA-13, and OGFC-13 three typical gradations in the stiffness mapping table. The gradation selection switch is set on the paver operation interface, the switch signal is transmitted to the embedded processing unit through the digital input interface, and the processing unit calls the corresponding sub-table to perform stiffness calculation according to the currently selected gradation. When the gradation is switched, the system automatically clears the current data cache and reinitializes the filter state to ensure data continuity is not disturbed.

[0024] Further, the output sub-module appends a data quality identification bit when sending the thickness data, where a value of 0 indicates that the current data is calculated based on complete sensor input, a value of 1 indicates that the pressure sensor is partially failed, and only the ranging data and temperature are used to estimate the stiffness, and a value of 2 indicates that the temperature sensor is failed, and the last valid temperature value is used for compensation. The main controller of the paver determines whether to adopt the data for automatic leveling control according to the identification bit, and if the identification bits of five consecutive frames are 1 or 2, the control mode is degraded, and the manual intervention mode is switched.

[0025] Compared with the prior art, the asphalt paver virtual paving thickness real-time measurement method based on thickness measurement provided by the present application has the following beneficial effects:

[0026] 1. By synchronously collecting geometric height, contact pressure and material temperature, a multi-dimensional perception ability for the instantaneous mechanical state of the asphalt mixture is constructed, which breaks through the limitation of the single ranging sensor that can only reflect the surface topography, and solves the problem of measurement reference drift caused by material property fluctuation from the principle level.

[0027] 2. The established stiffness compensation model is based on offline calibration experimental data, has clear mapping relationship, does not require complex iteration or machine learning training, has low resource occupation, can be run in real time on an embedded platform, meets the millisecond-level response requirement of paving operation, and ensures that the control system obtains continuous, stable and high-confidence thickness feedback signals.

[0028] 3. The pressure sensor array adopts a floating pressure foot structure, which ensures reliable contact with the surface of the loose paving material and avoids excessive compression and damage to the original structure of the material, and the measured pressure value truly reflects the local support stiffness of the material, and the temperature parameter is jointly input into the compensation model, so that the corrected thickness value is closer to the actual potential thickness of the material after compaction, and the process control precision is improved.

[0029] 4. The system has a sensor fault diagnosis and data degradation output mechanism, which can still provide identified estimated data when part of the sensors are failed, ensures the continuity of construction, avoids the paralysis of the entire measurement system due to a single point failure, and improves the robustness and usability of the equipment in harsh working conditions.

[0030] 5. Self-adaptive compensation of multiple gradations of materials is supported, the stiffness mapping sub-tables of different gradations are pre-stored, and the manual selection switch is used, so that the same set of hardware system can adapt to multiple material formulas, without the need to replace the sensors or recalibrate, reducing the equipment maintenance cost and operation complexity, and improving the engineering practicability and promotion value of the system. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 is the overall technical scheme architecture schematic diagram of the asphalt paver virtual paving thickness real-time measurement method based on thickness measurement provided by the present application;

[0032] Figure 2 is the core principle framework diagram of the multi-physical field synchronous perception and stiffness compensation model in the application;

[0033] Figure 3 is the logical flow framework diagram of the sensor data acquisition and preprocessing stage in the application;

[0034] Figure 4 is the logical flow framework diagram of the stiffness coefficient dynamic calculation and thickness correction stage in the application;

[0035] Figure 5 is the multi-level interaction relationship and data flow diagram of the paver control system and the cloud platform in the application; DETAILED DESCRIPTION

[0036] The application provides a virtual paving thickness real-time measurement method based on thickness measurement of asphalt pavers, which is characterized by constructing a multi-physical field synchronous perception architecture and a real-time stiffness compensation model, realizing quantitative characterization of the instantaneous mechanical state of asphalt mixture, and dynamically correcting the geometric ranging results accordingly, so as to output stable, reliable and directly used for virtual paving thickness data of the compaction process closed-loop control. The method is deployed in front of or beside the screed structure of the asphalt paver, including a non-contact ranging sensor array, a contact pressure sensor array, a temperature sensor module, an embedded data processing unit and a communication interface module. All hardware components and software algorithms work together to form a complete closed-loop measurement and control system, with a running cycle of 20 ms, ensuring that data acquisition, processing and output meet the stringent real-time requirements of paving operations.

[0037] The non-contact ranging sensor array is composed of three laser displacement sensors, which are arranged at equal intervals along the paving width direction. The sensor installation height is 1.2 m away from the theoretical reference surface, the measurement range is 0.3 m to 1.5 m, the repeatability is ±0.5 mm, and the sampling frequency is 500 Hz. Each sensor works independently, the wavelength of its laser emitter is 650 nm, the spot diameter is 3 mm at a distance of 1 m, the protection level is IP67, and the shell material is die-cast aluminum alloy.

[0038] The sensor is installed through a universal adjusting support, which has the adjusting ability of pitch angle ±15 degrees and horizontal angle ±10 degrees. After installation, the laser target calibration is used to calibrate the perpendicularity of the optical axis, so as to ensure that the angle between the measurement beam and the normal line of the theoretical reference surface is less than 1 degree. The sensor power supply voltage is 24 V DC, the power consumption is less than 3 W, the signal output is 0 to 10 V analog voltage, and after sampling by a 16-bit analog-to-digital converter, it is input into the embedded processing unit. The array is used to obtain the absolute height data of the top surface of the loose paving material at multiple transverse positions, providing the original geometric reference for subsequent thickness calculation.

[0039] The contact pressure sensor array is composed of 9 piezoresistive thin film pressure sensors in a 3x3 matrix structure. The effective sensing area of the sensor is 20mmx20mm, the range is 0-50KPa, the non-linear error is less than 1%, and the hysteresis is less than 0.5%.

[0040] All sensors are installed at the end of the elastic floating foot, the initial compression of the foot is set to 5mm, the spring stiffness coefficient is 800N / m, which ensures that the sensor maintains constant light pressure contact with the surface of the material without damaging the original structure of the material. The floating foot structure includes a guide sleeve, a compression spring, a pressure sensor base and a wear-resistant pressure head.

[0041] The guide sleeve has an inner diameter of 25mm, an outer diameter of 30mm, a length of 60mm and is made of hard anodized aluminum alloy; the compression spring has a free length of 50mm, a working length of 45mm after compression, a wire diameter of 2mm, a middle diameter of 20mm and an effective number of turns of 8; the pressure sensor base is a cylindrical stainless steel component with a diameter of 24mm and a height of 10mm, and a central hole is provided for leading out the sensor signal line; the wear-resistant pressure head is a hemispherical structure made of polytetrafluoroethylene with a curvature radius of 10mm and a surface roughness Ra of 0.8μm.

[0042] The foot is fixed to the leveling plate support beam by screw connection, and after installation, it is ensured that the bottom surfaces of all pressure heads are in the same horizontal plane with a height error of less than 0.1mm. The array is used to sense the local support force of the sensor on the surface of the material, which directly reflects the instantaneous stiffness characteristics of the material under the current temperature and gradation.

[0043] The temperature sensing module uses a platinum resistance temperature sensor installed at the outlet of the paver spiral distributor, with a measurement range of 0-300℃, an accuracy of ±1℃ and a response time of less than 200ms. The module is used to obtain the average temperature value of the mixture entering the paving area in real time, which is a key parameter affecting the viscoelastic-plastic behavior of asphalt mixture, and is used together with pressure sensing data as the core input of the stiffness compensation model.

[0044] The embedded data processing unit is an industrial-grade ARM Cortex-A53 architecture processor with a main frequency of 1.2 GHz, equipped with 512 MB of running memory and 4 GB of storage space, running a real-time operating system, and having multi-thread parallel processing capability. The unit communicates with the paver main controller through the CAN bus interface to obtain process parameters such as paving speed, spiral rotation speed, and vibration frequency; and synchronously collects the original data streams of the non-contact distance measuring sensor array, the contact pressure sensor array, and the temperature sensing module through the RS485 interface. The data acquisition cycle is set to 20 ms, all sensor data are provided with hardware-level time stamps, and the time synchronization error is less than 1 ms. The unit is the core of the entire system, responsible for executing the stiffness compensation algorithm model, which includes a data preprocessing submodule, a stiffness coefficient calculation submodule, a thickness correction submodule, and an output submodule.

[0045] When the system starts, the embedded data processing unit first executes a self-checking program. The self-checking program includes sensor communication link testing, memory integrity verification, and stiffness mapping table boundary value verification. If the pressure sensor zero drift is detected to be more than ±0.5 KPa, or the ranging sensor reference value deviates from the factory calibration value by more than ±1 mm, an alarm is triggered and the thickness output is suspended until manual reset or replacement of the sensor. During system operation, the zero point values of each sensor are automatically recorded every 10 minutes and compared with the initial values. If the drift exceeds the threshold, software compensation is started, the compensation coefficient is written to the non-volatile memory and applied in subsequent calculations. This mechanism ensures the stability of the measurement accuracy of the system in long-term operation.

[0046] After the system completes self-checking and enters normal operation, the method step S1: data acquisition and time synchronization is executed. The embedded data processing unit polls the non-contact distance measuring sensor array, the contact pressure sensor array, and the temperature sensing module through the RS485 bus at a period of 20 ms. Each sensor immediately sends its current sampling value together with the hardware time stamp upon receiving the polling instruction.

[0047] After the data processing unit receives all data packets, it first verifies the continuity and consistency of the time stamps to ensure that all sensor data are collected within the same 20 ms period. If a sensor data packet is missing or the time stamp is abnormal, the system records the event and starts the data recovery mechanism. For pressure and temperature data, the last valid value is used for zero-order hold; for distance measuring data, if a single point is missing, linear interpolation is performed using adjacent sensor data. After data acquisition and preliminary verification, the raw data are sent to the data preprocessing submodule.

[0048] Performing method step S2: data preprocessing. The data preprocessing submodule processes three types of raw data respectively. For the 9-channel raw pressure data output by the contact pressure sensor array, a moving average filter is performed with a window length of 5 sampling points, i.e. the arithmetic mean of the pressure values of the current point and its previous 4 historical points is calculated to eliminate high-frequency noise caused by the vibration of the paver or the instantaneous impact of the material. The filtered pressure data is marked as to , corresponding to the 9 positions in the 3x3 matrix respectively.

[0049] For the 3-channel raw height data output by the non-contact ranging sensor array, outlier rejection is performed. The criterion is adopted, i.e. the mean value and the standard deviation of the 10 sampling points of the current period and the previous 9 periods are calculated, and if the current sampling value satisfies , it is determined as an outlier and replaced by .

[0050] The processed height data is marked as , , . For the single temperature value output by the temperature sensing module, since its sampling frequency is low and changes slowly, zero-order hold interpolation is performed, i.e. its value is copied and kept until the next temperature sampling period arrives, ensuring that it has corresponding temperature values for pressure and ranging data in each 20ms processing period. The preprocessed data , , is sent to the stiffness coefficient calculation submodule.

[0051] Performing method step S3: stiffness coefficient calculation. The stiffness coefficient calculation submodule calls the pre-stored stiffness mapping table according to the pre-processed pressure value and temperature value . The mapping table is established through offline calibration experiments, covering the temperature interval of 120℃ to 180℃ and the pressure interval of 5KPa to 45KPa. The stiffness coefficient is defined as the ranging correction amount caused by unit pressure change, with the unit of mm / KPa. The mapping table is stored in the embedded memory in the form of a two-dimensional array, with a temperature step of 5℃ and a pressure step of 2KPa.

[0052] For each pressure sensor position , its corresponding stiffness coefficient is calculated in real time by bilinear interpolation. The specific process is as follows: first, find the current temperature and pressure The four nearest grid points , , , where and are the nearest temperature grid points less than and greater than , and are the nearest pressure grid points less than and greater than . Then, linearly interpolate the pressure on the and temperature lines to obtain and , and finally linearly interpolate and in the temperature dimension to obtain the final .

[0053] This calculation process is performed in parallel for the 9 pressure sensor positions to obtain 9 stiffness coefficients to .

[0054]

[0055] where , , , are the stiffness coefficient calibration values at grid points , , , . The system supports multi-level material adaptive compensation by pre-storing independent mapping sub-tables for AC-13, SMA-13, and OGFC-13 three typical gradations in the stiffness mapping table. The paving machine operation interface sets the gradation selection switch, and the switch signal is transmitted to the embedded processing unit through the digital input interface. The processing unit calls the corresponding sub-table to perform stiffness calculation according to the currently selected gradation. When the gradation is switched, the system automatically clears the current data cache and reinitializes the filter state to ensure data continuity is not disturbed.

[0056] Perform method step S4: thickness correction and data fusion. The thickness correction submodule multiplies the non-contact distance measurement original value by the stiffness coefficient to obtain the correction amount . Since there are 3 distance measurement sensors and 9 pressure sensors, data space matching is required. The system divides the paving width into 3 regions, each corresponding to 1 distance measurement sensor and 3 pressure sensors below it. For the th region ( =1, 2, 3), its correction amount The arithmetic mean of the three pressure sensor correction values in the region is calculated, i.e. .

[0057] Finally, the virtual paving thickness output value of the region is , wherein is the non-contact distance measurement original value in the region, is a system installation height compensation constant, the value is 1.2 m, , , is the output of the three pressure sensors in the region, , , is the three corresponding stiffness coefficients of the region. This formula unifies the geometric height, material stiffness and installation reference, and outputs a thickness value with clear physical meaning.

[0058] The corrected thickness data , , represents the real-time virtual paving thickness of the three key positions in the transverse direction of the paver.

[0059] The method step S5 is executed: data output and quality identification. The output submodule groups and packs the corrected thickness data according to the transverse position of the paver, and sends it to the paver main controller through the CAN bus at a frequency of 50 frames per second, and uploads it to the cloud monitoring platform through the Ethernet interface. When sending data, the data quality identification bit is attached.

[0060] The calculation logic of the identification bit is as follows: first, check whether all 9 pressure sensor data is valid, if all are valid and the temperature data is valid, the identification bit takes value 0; if there is one or more pressure sensor data invalid (such as out of range or communication interruption), but the temperature data is valid, the system only uses the distance measurement data and the temperature interpolation to estimate a global stiffness coefficient , and uses this to correct all regions, at this time the identification bit takes value 1; if the temperature sensor data fails, use the last valid temperature value for compensation calculation, the identification bit takes value 2.

[0061] The paver main controller decides whether to adopt the data for automatic leveling control according to the identification bit, if the identification bit is 1 or 2 for 5 consecutive frames, the control mode is degraded, and the manual intervention mode is turned on. This mechanism ensures that the system can still provide estimated data with identification when some sensors fail, ensuring construction continuity.

[0062] Stiffness mapping table The offline calibration method is the core foundation of this invention. In a laboratory environment, standard-graded AC-13 asphalt mixture specimens were prepared, with the initial compaction degree controlled at 85%. Tests were conducted at four temperature points: 120℃, 140℃, 160℃, and 180℃. At each temperature point, the specimens were placed in a constant-temperature chamber for 30 minutes, and then moved to a testing platform, on which a pressure sensor array and distance sensor, consistent with those used in engineering applications, were installed.

[0063] A hydraulic loading device was used to apply five constant pressure levels (5 kPa, 15 kPa, 25 kPa, 35 kPa, and 45 kPa) to the surface of the specimen. Each pressure level was maintained for 10 seconds, and the output values ​​of the pressure sensor were recorded simultaneously. Distance sensor output value and infrared thermometer readings .

[0064] In each group Under these conditions, the actual thickness of the specimen was manually measured using a high-precision vernier caliper. Repeat the measurement three times and take the average value. Stiffness coefficient. It is calculated using the formula for each The experiment was repeated 5 times, and the results were taken. The arithmetic mean of the values ​​is used as the calibration value for the corresponding cell in the mapping table. After calibration, the mapping table is permanently written to the embedded memory and cannot be modified during field operation. This calibration process ensures the accuracy and reliability of the mapping table data, which is the fundamental guarantee for the effectiveness of the real-time compensation algorithm.

[0065] The system described in this invention tightly integrates hardware structure and software algorithms to form a highly robust and high-precision real-time measurement system. A non-contact ranging sensor array provides high-precision spatial geometric information, a contact pressure sensor array provides mechanical information reflecting material properties, and a temperature sensing module provides environmental status information. These three components are deeply fused through an embedded data processing unit. The stiffness compensation model, based on physical principles and experimental calibration, is computationally simple and efficient, requiring no complex iterations and fully meeting the real-time requirements of the embedded platform. The system's self-checking, self-compensation, fault diagnosis, and data degradation output mechanisms enable stable operation even in harsh construction site environments, improving the automation level of asphalt paving operations and the uniformity of the final pavement quality.

[0066] It is to be understood that the terminology used herein such as first and second, and the like, is only used to distinguish one entity or action from another entity or action, and does not necessarily require or imply any such actual relationship or order between such entities or actions. Moreover, the terms includes and comprising, and any other variant thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but can include other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0067] While embodiments of the application have been shown and described, it is to be understood that the application is not limited to the details of the embodiments described, since numerous modifications and changes can be made to the embodiments without departing from the spirit and scope of the application as defined by the appended claims and their equivalents.

Claims

1. A method for real-time measurement of virtual paving thickness of an asphalt paver based on thickness metrology, characterized in that, The application relates to a paving machine height sensor system. The application comprises: a non-contact distance sensor array, a contact pressure sensor array and a temperature sensor module are arranged in front of or on the side of a paving machine ironing plate; a height data, pressure data and temperature data are synchronously collected by an embedded data processing unit at a period of 20ms; Based on the pre-stored stiffness mapping table And bilinear interpolation algorithm, based on the filtered pressure value Interpolated temperature value Calculate the stiffness coefficient corresponding to each pressure sensing location. ; the height data is subjected to outlier elimination processing, the pressure data is subjected to sliding average filtering processing, and the temperature data is subjected to zero-order hold interpolation processing; the height data is multiplied by a correction value obtained by multiplying a corresponding area stiffness coefficient and a pressure value, and a system installation height compensation constant 1.2m is superimposed to obtain a virtual paving thickness output value of each area in the paving width direction; 2. The thickness gauge based real-time virtual paving thickness measurement method for asphalt pavers as claimed in claim 1, wherein, the virtual paving thickness output value is grouped and packaged according to areas, and a data quality identification bit is added to be sent to a paving machine main controller through a CAN bus. The non-contact distance sensor array is composed of three laser displacement sensors which are arranged at equal intervals along the paving width direction, and the installation height is 1.2m away from a theoretical reference surface, and the measurement range is 0.3m to 1.5m; the contact pressure sensor array is composed of nine piezoresistive thin film pressure sensors in a 3*3 matrix structure, the effective sensing area of the sensor is 20mm*20mm, and the range is 0 to 50KPa; 3. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 2, wherein, the temperature sensor module adopts a platinum resistance temperature sensor, is installed at the outlet of a spiral distributor, and the temperature measurement range is 0 to 300 DEG C. The contact pressure sensor array is installed at the end of an elastic and retractable floating foot, and the initial compression amount of the foot is 5mm, and the spring stiffness coefficient is 800N / m; the floating foot comprises a guide sleeve, a compression spring, a pressure sensor base and a wear-resistant pressure head; the guide sleeve has an inner diameter of 25mm, an outer diameter of 30mm and a length of 60mm; the compression spring has a free length of 50mm, a working length of 45mm, a wire diameter of 2mm, a middle diameter of 20mm and an effective number of turns of 8; 4. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 3, wherein, the wear-resistant pressure head is a polytetrafluoroethylene hemispherical structure with a curvature radius of 10mm and a surface roughness Ra of 0.8mu m. The embedded data processing unit is an ARM Cortex-A53 architecture processor with a main frequency of 1.2GHz, 512MB running memory and 4GB storage space, and runs a real-time operating system; 5. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 4, wherein, The rigidity map The temperature interval 120-180℃, the pressure interval 5-45KPa, stored in two-dimensional array, temperature step 5℃, pressure step 2KPa; Stiffness coefficient Defined as the distance measurement correction caused by the unit pressure change, with the unit of mm / KPa; paving speed, spiral rotating speed and vibration frequency process parameters are obtained through a CAN bus interface, and original sensor data streams are synchronously collected through an RS485 interface, and all data have hardware level time stamps.

6. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 5, wherein, In the bilinear interpolation calculation, four adjacent grid points of the current temperature and pressure values are located, intermediate values are obtained through linear interpolation on the temperature line and the pressure line, and finally the stiffness coefficient is obtained through temperature dimension interpolation. The stiffness mapping table pre-stores independent mapping sub-tables of AC-13, SMA-13 and OGFC-13 three gradations; a gradation selection switch is arranged on a paving machine operation interface, and a switch signal is transmitted into the embedded processing unit through a digital input interface; the processing unit calls corresponding sub-tables to execute stiffness calculation according to the selected gradation; when the gradation is switched, the data buffer is automatically emptied and the filter state is reinitialized.

7. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 6, wherein, The outlier rejection adopts 3 criteria, calculates the mean value of 10 sampling points of current period and previous 9 periods and standard deviation , if the current sampling value satisfies , replaces it with ; The sliding average filter window length is 5 sampling points, and the pressure values of the current point and the previous 4 historical points are arithmetically averaged; The zero-order hold interpolation copies and holds the temperature value to the next sampling period.

8. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 7, wherein, The paving width is divided into 3 regions, and each region corresponds to 1 ranging sensor and 3 pressure sensors; area correction amount ;​ zone virtual paving thickness output value ;​ For the first Non-contact distance measurement raw value in the region, System installation height compensation constant, value is 1.2m, , , 3 pressure sensor outputs in the region, , , 3 corresponding stiffness coefficients in the region.

9. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 8, wherein, The data quality identification bit is valued 0, indicating that the pressure and temperature data are complete and valid; Valued 1 indicates that the pressure sensor is partially failed, and the average value of the effective pressure sensor stiffness coefficient is used for global correction; Valued 2 indicates that the temperature sensor is failed, and the last valid temperature value is used for compensation; If the identification bit is 1 or 2 for 5 consecutive frames, the control mode is triggered to degrade.

10. The thickness gauge based virtual paving thickness real-time measurement method for asphalt pavers of claim 9, wherein, The system performs a self-checking program when starting, including sensor communication link test, memory integrity check, and stiffness mapping table boundary value verification; If the pressure sensor zero drift exceeds ±0.5KPa or the ranging sensor reference value deviates from the factory calibration value by more than ±1mm, an alarm is triggered and the thickness output is suspended; During operation, the sensor zero point value is recorded every 10 minutes, and if the drift exceeds the threshold, software compensation is started and the compensation coefficient is written to the non-volatile memory.

Images