Dynamic remote monitoring system for paving speed of asphalt mixture in tunnel environment

Abstract

The invention discloses a dynamic remote monitoring system for the paving speed of an asphalt mixture in a tunnel environment, which relates to the technical field of asphalt road construction, and comprises a wheel speed sensor arranged on a paver and used for detecting the advancing speed in real time and representing the paving speed of asphalt by the advancing speed, the system further comprises a plurality of distance sensors which are arranged perpendicular to the advancing direction of the paver and used for detecting the vertical distance and forming a smooth distance value sequence of each distance sensor after smooth processing, and the linear correlation degree of the multiple smooth distance value sequences is calculated in the same time period so as to judge whether the asphalt road laid in the time period is uniform or not. When the condition that the uniformity degree of the set paving thickness is not met is detected, the system marks and stores the paving speed and the advancing track of the paver at the corresponding detection moment, so that follow-up personnel can carry out quality traceability and repair processing conveniently.

Description

Technical Field

[0001] The present invention relates to the technical field of asphalt road construction, in particular to a dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment. Background Art

[0002] Asphalt mixture, a mixture of mineral aggregate and asphalt, possesses excellent mechanical properties and is a primary material in modern highway pavement construction. Paving with asphalt mixtures has become a common construction method in most highway construction. During asphalt road construction, paving uniformity is a key factor affecting the road's structural safety, usability, and project quality. The speed of asphalt paving directly impacts the paving quality, which in turn determines the road's ultimate performance. This impact is particularly pronounced in tunnels, where operating space is limited and construction environments are complex.

[0003] Traditionally, the paver's travel speed is often used to indicate asphalt paving speed, but this method fails to fully reflect fluctuations in paving quality. Particularly in tunnel construction environments, due to the limited flexibility of paver operation, speed fluctuations can lead to uneven paving thickness, thus affecting the quality of the asphalt pavement. Currently, asphalt road quality is often monitored during construction using methods such as random inspections. This method cannot achieve real-time monitoring of construction quality, nor can it accurately identify the specific locations and extent of uneven sections.

[0004] Furthermore, due to unstable network conditions at construction sites, especially deep in tunnels, traditional systems struggle to transmit real-time data wirelessly. Quality issues arising during construction often go undetected and uncorrected. Uneven sections can only be identified through post-construction road quality inspections, which makes it difficult to provide effective data support for subsequent quality analysis and traceability, hindering appropriate construction adjustments.

[0005] In summary, existing technologies have many limitations in monitoring asphalt paving quality, especially in special construction environments such as tunnels. They lack the ability to detect paving quality fluctuations in real time, accurately record specific locations and unevenness, and support quality traceability. Summary of the Invention

[0006] 1) Technical issues solved The present invention provides a dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment, so as to realize subsequent paving road quality analysis and quality traceability.

[0007] 2) Technical solution To achieve the above objectives, the present invention provides the following technical solution: a dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment, applicable to a paver capable of simultaneously paving and compacting asphalt mixture, comprising: The detection module includes a speed detection unit for detecting the traveling speed of the paver and a distance detection unit for detecting the vertical distance to the compacted asphalt road; wherein, The speed detection unit includes a wheel speed sensor provided on the paver, for detecting the travel speed in real time, and using the travel speed to represent the asphalt paving speed; The distance detection unit includes a plurality of distance sensors arranged perpendicular to the traveling direction of the paver, each of the distance sensors having a fixed position and the same detection frequency, and is used to detect the vertical distance; a calculation module for receiving the travel speed and vertical distance in real time, smoothing the vertical distance to obtain a smoothed distance value at each detection moment, sequentially arranging a plurality of the smoothed distance values ​​of each distance sensor in a continuous time period to form a smoothed distance value sequence, and calculating a linear correlation between the plurality of smoothed distance value sequences within the same time period to determine whether the asphalt road paved within the time period is uniform; if the linear correlation between any two smoothed distance value sequences deviates from a preset range, it is determined that the asphalt road in the area is unevenly paved; A storage module locally stores the paving speed and smoothed distance values ​​detected and calculated in real time, and uploads the data to the cloud in segments after storage; wherein, when an unevenly paved road section is detected, the smoothed distance value sequence whose linear correlation deviates from the preset range and the paving speed detected within the time period are marked.

[0008] Furthermore, the calculation module smoothes the received vertical distance data to eliminate data noise, and obtains a smoothed distance value of each distance sensor at each detection moment after smoothing, including the following steps: Set a first time window of fixed length; The vertical distance of each distance sensor at each detection moment is sequentially input into the first time window in the order of detection time; wherein the number of vertical distances entering the first time window is always kept equal to the length unit of the window, and the vertical distance at the most recent detection moment of the distance sensor is placed at the end of the first time window; Whenever the last digit of the first time window is input into the vertical distance at the latest detection moment, the average value of all vertical distances in the first time window is calculated, and the average value is output as the smoothed distance value at the latest detection moment.

[0009] Furthermore, the calculation module receives the travel speed detected by the wheel speed sensor in real time and calculates it to represent the asphalt paving speed, including the following steps: Set a second time window of fixed length; The travel speeds detected in real time by the wheel speed sensors are sequentially entered into the second time window in order of detection time; wherein the number of travel speeds entering the second time window is always kept equal to the unit of length of the window, and the travel speed at the most recent detection moment of the wheel speed sensor is at the end of the second time window; Whenever the last digit of the second time window is input as the traveling speed at the latest detection moment, the average value of all traveling speeds within the second time window is calculated, and the average value is output as the paving speed at the latest detection moment.

[0010] Furthermore, the detection module also includes a positioning unit, which is used to record the travel trajectory of the paver each time the distance sensor detects.

[0011] Furthermore, within the same time period, the calculation module determines whether the thickness uniformity of the asphalt road paved within the time period meets the setting by calculating the linear correlation value of the smoothed distance value sequences of different distance sensors and comparing the calculated value with the set range, including the following steps: Set a third time window of fixed length; Simultaneously inputting the smoothed distance values ​​of different distance sensors into the third time window, forming a first smoothed distance value sequence from all smoothed distance values ​​of each distance sensor in the third time window in order of detection time; wherein the number of smoothed distance values ​​in each of the first smoothed distance value sequences always remains equal to the length unit of the third time window, and the smoothed distance value of each distance sensor at the latest detection moment is located at the end of the third time window; Whenever the last digit of the third time window is simultaneously input with the smoothed distance values ​​of the latest detection moments of different distance sensors, the linear correlation value of any two of the first smoothed distance value sequences is calculated; When the linear correlation values ​​of any two of the first smoothed distance value sequences do not fall within the set range, the travel trajectory of the paver at the corresponding detection moment is marked and stored.

[0012] Furthermore, the calculation module calculates the linear correlation value between any two of the first smoothed distance value sequences by using a Pearson correlation coefficient method.

[0013] Furthermore, the calculation module determines the thickness uniformity of the asphalt road along the traveling direction of the paver by calculating the linear regression slope of all smoothed distance values ​​detected by at least one distance sensor within a continuous time period, including the following steps: Setting a fourth time window of fixed length; The smoothed distance values ​​of the distance sensor are sequentially entered into the fourth time window according to a chronological order, and all the smoothed distance values ​​of the distance sensor in the fourth time window are formed into a second smoothed distance value sequence according to a detection time order; wherein the number of smoothed distance values ​​in the second smoothed distance value sequence always remains equal to the length unit of the fourth time window, and the smoothed distance value at the latest detection moment of the distance sensor is located at the end of the fourth time window; Whenever the last bit of the fourth time window is input into the smoothed distance value at the latest detection moment, a linear regression slope of each second smoothed distance value sequence is calculated; If the linear regression slope of the second smoothed distance value sequence is higher than a set value, the travel trajectory of the paver during the corresponding detection time is marked and stored.

[0014] Furthermore, the first time window, the second time window, the third time window and the fourth window all adopt a moving window approach. Each time new detection data enters, the data in the window will be updated slidingly to retain the latest detection data and perform real-time analysis and calculation.

[0015] 3) Beneficial effects: Compared with the prior art, this invention has the following beneficial effects: The present invention uses the paver's travel speed to directly represent the asphalt paving speed, while using multiple vertical distance sensors to detect the paving uniformity of the asphalt road in real time. By analyzing the linear correlation of the vertical distance sequences between different distance sensors in the same time period, the uniformity of the asphalt road thickness is dynamically monitored in the direction perpendicular to the paver's travel direction; and by analyzing the linear regression of the vertical distance sequence of each distance sensor in continuous time periods, the uniformity of the asphalt road thickness is dynamically monitored along the paver's travel direction.

[0016] To address the spatial limitations and unstable wireless networks of tunnel construction environments, local data storage and segmented transmission are employed to ensure effective data monitoring and storage, even when real-time wireless data transmission is impossible within the tunnel. Local computation and storage enable rapid feedback and real-time coordination of calculation results with the paver's operations, ensuring simultaneous monitoring of paving speed and quality. When uneven sections are detected, the system records the relevant data, providing a basis for subsequent construction adjustments. Segmented transmission ensures that data from each time period along the paver's path is continuously recorded and transmitted in an orderly manner. By recording data such as the paver's speed and thickness uniformity and uploading it to the cloud, relevant personnel can monitor construction progress at any time for subsequent analysis and quality traceability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a schematic diagram of an application scenario in which the dynamic remote monitoring system for asphalt mixture paving speed provided by an embodiment of the present invention is set on a paver; Figure 2 This is a schematic diagram of a scenario in which each distance sensor in the dynamic remote monitoring system for asphalt mixture paving speed provided by an embodiment of the present invention detects the vertical distance to the compacted asphalt road surface in a direction perpendicular to the horizontal plane; Figure 3 This is a functional block diagram of a dynamic remote monitoring system for asphalt mixture paving speed provided by an embodiment of the present invention; Figure 4 A flowchart showing the process of the calculation module for smoothly processing the detection data of each distance sensor in the dynamic remote monitoring system for asphalt mixture paving speed provided by an embodiment of the present invention; Figure 5 A flowchart showing the process of representing the paving speed after the calculation module smoothes the detection data of the wheel speed sensor in the dynamic remote monitoring system for asphalt mixture paving speed provided by an embodiment of the present invention; Figure 6 A flowchart showing a calculation module in the dynamic remote monitoring system for asphalt mixture paving speed provided by an embodiment of the present invention, which determines the thickness uniformity of an asphalt-paved road by calculating the linear correlation between detection data sequences of different distance sensors; Figure 7 A schematic diagram showing a comparison of smoothed distance values ​​detected by three distance sensors disposed at different positions within a third time window provided by an embodiment of the present invention; Figure 8 A flowchart showing a calculation module in a dynamic remote monitoring system for asphalt mixture paving speed provided by an embodiment of the present invention, which determines the thickness uniformity of an asphalt-paved road based on the linear regression slope of a data sequence detected by a distance sensor over a continuous period of time; In the picture: 8. Detection module; 101. Speed ​​detection unit; 102. Distance detection unit; 1021. Distance sensor; 9. Calculation module; 30. Storage module. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like to indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting the present invention.

[0020] In addition, the terms "first", "second", etc., if used, are merely used to distinguish and describe, and should not be understood as indicating or implying relative importance.

[0021] It should be noted that, in the absence of conflict, the features in the embodiments of the present invention may be combined with each other.

[0022] When paving asphalt mixture roads in a tunnel environment, the inventors found that: When monitoring the paving speed of asphalt mixtures, most methods currently use the asphalt paver's travel speed as a proxy for the asphalt paving speed. This method directly represents the asphalt paving speed, eliminating the need for further conversion formulas and estimating the asphalt paving progress based on travel speed. However, this method cannot fully reflect fluctuations in paving quality. Particularly in tunnel construction environments, due to the limited flexibility of the paver's operation, speed fluctuations can cause uneven paving thickness, thus affecting the quality of the asphalt pavement. Currently, asphalt road quality is often tested during construction using methods such as random sampling. This method cannot achieve real-time monitoring of construction quality, nor can it accurately identify specific uneven sections and the degree of unevenness.

[0023] Furthermore, due to unstable network conditions at construction sites, especially deep in tunnels, traditional systems struggle to transmit real-time data wirelessly. Quality issues arising during construction often go undetected and uncorrected, requiring only post-construction road quality inspections to identify uneven sections. This approach not only lacks real-time performance but also struggles to provide effective data support for subsequent quality analysis and traceability, leading to delayed construction adjustments and increased costs and risks associated with subsequent repairs.

[0024] In summary, existing technologies have many limitations in monitoring asphalt paving quality, especially in special construction environments such as tunnels. They lack the ability to detect paving quality fluctuations in real time, accurately record specific locations and unevenness, and support quality traceability.

[0025] Furthermore, in order to improve the various defects in the above-mentioned prior art, an embodiment of the present invention provides a dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment, which can directly represent the asphalt paving speed by the traveling speed of the paver, that is, use the wheel speed sensor arranged on the paver to directly represent the asphalt paving speed. At the same time, multiple distance sensors 1021 arranged perpendicular to the road surface are used to detect the paving uniformity of the asphalt road in real time. By analyzing the linear correlation of the vertical distance sequences between different distance sensors 1021 in the same time period, the uniformity of the thickness of the asphalt road is dynamically monitored in the direction perpendicular to the traveling direction of the paver. By analyzing the linear regression of the vertical distance sequence of each distance sensor 1021 in a continuous time period, the uniformity of the thickness of the asphalt road is dynamically monitored in the direction of the traveling direction of the paver.

[0026] In view of the spatial limitations and unstable wireless network conditions in tunnel construction environments, local data storage and segmented transmission are adopted to ensure that even when real-time wireless data transmission is not possible in the tunnel, the system can still effectively monitor and store data. When an uneven road section is detected, the system will immediately generate an alarm signal and record relevant data to provide a basis for subsequent construction adjustments.

[0027] It should be noted that the various defects existing in the technical solutions in the above-mentioned prior art are the results obtained by the inventor after careful practical research. Therefore, the process of discovering the above-mentioned problems and the solutions proposed in the embodiments of the present invention below for the above-mentioned problems should all be the contributions made by the inventor to the present invention in the process of realizing the embodiments of the present invention.

[0028] First, combine Figures 1 to 8As shown, the embodiment of the present invention provides a dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment. The monitoring system is mainly applicable to the situation where the initial compaction work is performed after the asphalt mixture is paved, and is specifically applicable to a paver that can perform asphalt paving and compaction work at the same time. Please refer to Figure 1 , Figure 1 This is a schematic diagram of the application scenario of this monitoring system set up on a paver.

[0029] Specifically, during the operation of an asphalt paver, a detection module 10 is installed on the paver to monitor its asphalt paving speed and the quality of the paved road. Specifically, a speed detection unit 101 is installed on the paver's driving wheel. In some embodiments, a wheel speed sensor is installed on the paver to monitor the paver's travel speed in real time. The paver's travel speed is the asphalt paving speed, eliminating the need for additional conversion formulas. The wheel speed sensor data is then input into the calculation module 20 and storage module 30 in real time for further calculation and analysis.

[0030] To more accurately monitor the thickness of the asphalt pavement after paving and compaction, a distance detection unit 102 is installed behind the rollers (i.e., after the compaction zone). This unit is used to detect distance data from the compacted asphalt pavement in real time, enabling analysis of thickness uniformity. This unit 102 comprises multiple distance sensors 1021, arranged parallel to the rollers and spaced apart. It is important to note that the detection direction of the distance sensors 1021 should be perpendicular to the horizontal plane to ensure real-time detection of thickness changes on the asphalt surface.

[0031] Each distance sensor 1021 continuously samples at the same frequency, measuring the vertical distance from the sensor to the compacted asphalt road surface. It should be noted that the installation layout of multiple distance sensors 1021 should be such that they cover multiple points across the entire paving width to ensure comprehensive thickness monitoring and lateral uniformity.

[0032] It is understood that since this distance data is continuously collected during the paving process, it can be converted into the thickness of the asphalt road after data processing. It is important to note that because distance sensor 1021 is installed on the paver and its installation height relative to the pre-paving reference road surface is fixed, distance sensor 1021 detects the vertical distance from the sensor to the compacted road surface, and the difference between this and the pre-paving reference road surface is used to represent the distance data of the asphalt road. Because the paver generates certain vibrations during operation, which may cause slight shifts in the sensor position, the impact of paver vibrations on the sensor position can be mitigated by designing anti-vibration brackets and using shock-absorbing materials (such as rubber pads, hydraulic shock absorbers, etc.), thereby ensuring that the installation height of distance sensor 1021 relative to the pre-paving reference road surface is fixed.

[0033] Regarding how to analyze thickness uniformity based on the distance data of the compacted asphalt road detected by distance sensor 1021, the monitoring system further includes a calculation module 20, which receives in real time the travel speed transmitted by speed detection unit 101 and the vertical distance to the paved road surface detected by distance detection unit 102. It then smoothes the vertical distance to obtain a smoothed distance value at each detection moment. Multiple smoothed distance values ​​from each distance sensor within a continuous time period are sequentially arranged to form a smoothed distance value sequence. It is important to note that these data are smoothed to reduce noise before being calculated.

[0034] Building on the above, since the detection direction of distance sensor 1021 is perpendicular to the paved and compacted asphalt surface, distance sensors 1021 distributed parallel to the width of the roller can be used to detect thickness variations across the paving width. Based on this thickness variation, the system identifies whether the road's paving thickness uniformity meets the project's specified thickness uniformity. Furthermore, real-time thickness detection can quickly reveal anomalies during paving, such as localized insufficient thickness or uneven fluctuations. The system marks the position of the paver when these defects are detected. These marked locations can subsequently facilitate road adjustments and repairs by staff, achieving quality traceability.

[0035] The aforementioned distance sensor 1021 may be, but is not limited to, a laser distance sensor, an ultrasonic distance sensor, or a millimeter-wave radar. In some feasible embodiments of the present invention, a laser distance sensor is used to perform the aforementioned detection work in this monitoring system. This sensor calculates distance by emitting a laser beam and detecting its reflection time or reflection angle. This sensor offers the advantages of high accuracy and rapid response, making it suitable for accurate thickness measurement of compacted asphalt pavement. It should be noted that different types of distance sensors 1021 have their own unique characteristics and can be selected based on the specific requirements of the tunnel environment (e.g., lighting conditions, spatial constraints, dust levels, etc.) to optimize measurement accuracy and system applicability. This is not specifically limited in the embodiments of the present invention.

[0036] Regarding the calculation process of the asphalt paving speed, more specifically, in some feasible embodiments of the present invention, considering that the distance sensor 1021 needs to perform real-time high-frequency detection, which inevitably brings data noise, to ensure the accurate calculation of the asphalt paving speed, the calculation module 20 smoothes the distance data obtained by each distance sensor 1021 in real time while receiving the data in real time, so as to reduce the data noise interference. Figure 4 , the specific workflow is as follows.

[0037] Step 101: Set a first time window of fixed length. Choose an appropriate window length that effectively smooths noise while maintaining the representativeness of the real-time data. This window length should be optimized based on the sampling frequency and noise characteristics to ensure a balance between smoothing and real-time performance.

[0038] Step 102: The vertical distance of each distance sensor 1021 at each detection moment is input into the first time window in sequence according to the detection time; wherein, the number of vertical distances entering the first time window is always kept equal to the length unit of the window, and the vertical distance of the distance sensor 1021 at the latest detection moment is located at the end of the first time window.

[0039] Step 103: Whenever the last digit of the first time window is input as the vertical distance at the most recent detection moment, the average of all vertical distances within the first time window is calculated and output as the smoothed distance value at the most recent detection moment. This means that whenever new vertical distance data enters the window, the window automatically discards the earliest digit at the beginning of the window to maintain a fixed length. This ensures that only the most recent fixed amount of data is retained within the window, achieving a "sliding window" smoothing effect. Using the smoothed distance value as the detection data at each detection moment eliminates interference from incidental data.

[0040] In conjunction with the above-mentioned smoothing steps for detection data, let's assume that a distance sensor 1021 detects paving distance data at a frequency of 10 Hz (i.e., 10 times per second). The first time window length unit is set to 10. This means that whenever new data enters, the 10 most recent detection data points are always retained within the window for smoothing calculation. In this way, the data within the window can represent the thickness variation within 1 second.

[0041] The following is a Python code to simulate the above smoothing data: import numpy as np # Simulate distance data thickness_data = [] # Define the unit size of the first time window of fixed length window_size = 10 # List for storing smoothed distance values smoothed_thickness_values ​​= [] # Smoothing: sliding window averaging for i in range(len(thickness_data)): # The starting position of the window for the current data point (keep the window size at 10) if i <window_size: # If the number of data points is less than the window size, use the existing data to calculate the mean window_data = thickness_data[:i + 1] else: # Otherwise take out the last 10 data points window_data = thickness_data[i - window_size + 1:i + 1] # Calculate the average value within the window as the smoothing distance value smoothed_value = np.mean(window_data) smoothed_thickness_values.append(smoothed_value) # Output the distance value after each smoothing print(f"Smoothed distance value ({i+1}th data): {smoothed_value:.2f} mm") # Display all smoothed distance values print("\nSmoothed distance data sequence:", smoothed_thickness_values) The above code implements a sliding window, using the mean of the most recent data in the first time window as the current smoothed distance value. For real-time processing of large amounts of data, you can implement a sliding window by replacing the contents of window_data without storing all historical data, improving memory efficiency.

[0042] Considering that the measurement data of the wheel speed sensor is sometimes affected by external factors, such as the vibration of the paver, uneven ground, sensor errors, etc., directly using the raw data may cause more drastic changes in speed, while the smoothed data can reduce such mutations, present a more stable trend, and more accurately reflect the actual working status of the paver. In addition, during the paving process, the travel speed of the paver is usually affected by many factors, such as turns, ramps, paver adjustments, etc. If these short-term fluctuations are averaged and then analyzed, the asphalt paving speed can be better characterized, especially for rapidly changing speed changes such as sudden deceleration when turning. These instantaneous fluctuations can be removed through smoothing. Therefore, in some embodiments of the present invention, the calculation module 20 also smoothes the received travel speed, please refer to Figure 5 , the specific workflow is as follows.

[0043] Step 201: Set a second time window of fixed length. In some feasible embodiments of the present invention, the length unit of the second time window should be small to reduce the delay effect caused by the smoothing process, thereby avoiding the influence of excessive historical data. The selection of the length unit of the second time window is generally related to the sampling frequency of the wheel speed sensor.

[0044] Step 202: The vehicle speeds detected in real time by the wheel speed sensors are sequentially entered into a second time window in the order of detection time. The number of speeds entering the second time window is always kept equal to the unit of the window length, and the speed at the most recent detection by the wheel speed sensor is placed at the end of the second time window. This means that a fixed number of the latest speed values ​​are retained within the second time window, ensuring that the data within the second time window is updated in real time. Whenever a newly detected speed value enters the window, the earliest data value at the beginning of the window is automatically discarded to maintain a fixed number of data within the window.

[0045] Step 203: Whenever the last digit of the second time window inputs the travel speed at the latest detection moment, the average value of all travel speeds in the second time window is calculated and the average value is output as the paving speed at the latest detection moment.

[0046] Based on the above example, the following is a Python code to continue simulating the calculation of asphalt paving speed: sampling_frequency = 10 # The wheel speed sensor samples 10 times per second window_length = 20 # The second time window length, containing 20 data points time_interval = 1 / sampling_frequency # The time interval between each sampling #Simulate the speed value sampled by the wheel speed sensor # Generate simulated travel speed value (unit: meters / second) smooth_thickness_data = np.cumsum(np.random.uniform( …, )) # Initialize the storage list of paving speed paving_speeds = [] # Slide the second time window to calculate the paving speed for i in range(len(smooth_thickness_data) - window_length + 1): # The smoothed distance value in the current second time window current_window = smooth_thickness_data[i : i + window_length] # Calculate the average value in the current second time window average_speed = np.mean(np.abs(thickness_change_rates)) # Take the absolute value and average paving_speeds.append(average_speed) # Output paving speed print("Paving speed:", paving_speeds) print(paving_speeds[:10]) # Display the first 10 results It can be understood that the above process realizes a method for calculating the asphalt paving speed based on the smoothed traveling speed value while avoiding the interference of noise.

[0047] Regarding how to check the thickness uniformity of the paved asphalt road, based on the smoothed distance value after the calculation module 20 smoothing, how the distance detection unit 102 determines the thickness uniformity of the asphalt road paved within the same time period according to the linear correlation of the smoothed distance value sequences of different distance sensors 1021. Figure 6 , the specific workflow is as follows.

[0048] Step 301: A third time window of fixed length is set, which is used to analyze asphalt road thickness variation data detected by different distance sensors 1021 within the same time period.

[0049] It should be noted that the unit length of the third time window should also be determined based on the sampling frequency of distance sensor 1021. Taking the distance sensor 1021 with a sampling frequency of 10 Hz as an example, the unit size of the first time window is 10. Typically, the unit size of the third time window is also an integer multiple of the unit size of the first time window. Furthermore, the length of the third time window can be adjusted based on actual needs to balance real-time performance and data stability.

[0050] Step 302: The smoothed distance values ​​of different distance sensors 1021 are simultaneously input into a third time window. All smoothed distance values ​​of each distance sensor in the third time window form a first smoothed distance value sequence in the order of detection time. The number of smoothed distance values ​​in each first smoothed distance value sequence always remains equal to the length unit of the third time window, and the smoothed distance value of each distance sensor at the latest detection moment is located at the end of the third time window.

[0051] Step 303: Whenever the last digit of the third time window is simultaneously input with the smoothed distance values ​​of the latest detection time from different distance sensors 1021, the linear correlation between any two first smoothed distance value sequences is calculated. In some feasible embodiments of the present invention, the Pearson correlation function can be used for calculation. This is a method for measuring the degree of linear correlation between two data sequences. The Pearson coefficient ranges from [-1 to 1], where a Pearson coefficient of 1 for two numerical sequences indicates a perfect positive correlation; a Pearson coefficient of 0 indicates no linear relationship; and a Pearson coefficient of -1 indicates a perfect negative correlation. The closer the Pearson correlation coefficient is to 1, the more consistent the thickness variation trends detected by different distance sensors 1021 for asphalt pavement laid and compacted perpendicular to the direction of travel of the paver. A Pearson correlation coefficient close to 0 or a negative value indicates inconsistent fluctuations in the detection results of different distance sensors 1021, indicating uneven asphalt pavement in that direction.

[0052] Regarding how to determine the uniformity, step 304 is performed: when the linear correlation value of any two first smoothed distance value sequences does not fall within the set range, the travel trajectory of the paver at the corresponding detection time is marked and stored.

[0053] Specifically, in some feasible embodiments of the present invention, the detection module 10 also includes a positioning unit, which is used to record the travel trajectory of the paver. It should be noted that, due to the unstable transmission signal in the tunnel environment, which is not suitable for wireless transmission, a storage module 30 is also provided in the system to locally store the paving speed and smoothed distance value detected and calculated in real time, and upload the data to the cloud in segments after storage; wherein, when an unevenly paved section is detected, the smoothed distance value sequence whose linear correlation deviates from the preset range and the paving speed detected within the time period are marked. The storage module 30 also receives the trajectory data of the positioning unit, and records the travel trajectory information of the paver through local storage and alternative positioning technology, as follows.

[0054] An INS (Inertial Navigation System) module and an odometer are installed on the paver. The INS provides direction and acceleration, while the odometer calculates displacement. These two are combined to infer the trajectory. In conjunction with the distance sensor 1021 in the distance detection unit 102, the detection module 10 synchronously stores the detected distance data and trajectory information, using each detection moment of the distance sensor 1021 as a time reference. After tunnel construction is completed, the trajectory and distance data are exported via USB or a local network. Visualization tools are used to mark the locations of uneven road sections to guide subsequent repairs.

[0055] In summary, the combination of an INS and a mileage sensor in the positioning unit enables the paver's trajectory to be recorded in a tunnel environment. Although GPS signals are not available, accurate trajectory data can be obtained through relative displacement and direction calculations. This data is stored synchronously with the thickness information, facilitating subsequent analysis and repair work.

[0056] Taking the above-mentioned Pearson correlation function calculation as an example, a threshold is set. For example, the value of the Pearson correlation coefficient is lower than a certain critical value. When the value of the linear correlation between different distance sensors 1021 is lower than the threshold, it indicates that unevenness has occurred in the paving process. The system will locally store and mark the travel trajectory information of the paver at the corresponding detection moment.

[0057] Based on the above conditions, the following is a Python code to simulate the operating logic of the three distance sensors 1021. When the linear correlation value of the first smoothed distance value sequence of any two distance sensors 1021 is lower than the set threshold, the system will locally store and mark the travel trajectory information of the paver at the corresponding detection moment.

[0058] #Simulate the first smoothed distance value sequence of three distance sensors def generate_sensor_data(num_samples): np.random.seed(42) sensor_1_data = [ ] sensor_2_data = [ ] sensor_3_data = [ ] return sensor_1, sensor_2, sensor_3 # Initialization parameters num_samples = 100 # Number of simulation samples threshold = 0.85 # Set the Pearson correlation coefficient threshold timestamps = pd.date_range("xx:xx:xx", periods=num_samples, freq='S')# Simulate time series gps_coordinates = [(40.0 + i * 0.0001, -3.0 + i * 0.0001) for i inrange(num_samples)] # Simulated trajectory coordinates # Generate distance sensor data sensor_1, sensor_2, sensor_3 = generate_sensor_data(num_samples) # Store data as DataFrame data = pd.DataFrame({ "Timestamp": timestamps, "GPS_Coordinates": gps_coordinates, "Sensor_1": sensor_1, "Sensor_2": sensor_2, "Sensor_3": sensor_3 }) # Calculate the Pearson correlation coefficient in the third time window window_size = 30 # Define the window size results = [] for i in range(len(data) - window_size + 1): window = data.iloc[i:i+window_size] r12, _ = pearsonr(window["Sensor_1"], window["Sensor_2"]) r13, _ = pearsonr(window["Sensor_1"], window["Sensor_3"]) r23, _ = pearsonr(window["Sensor_2"], window["Sensor_3"]) # Determine whether it is below the threshold if r12 <threshold or r13<threshold or r23<threshold: results.append({ "Timestamp": window.iloc[-1]["Timestamp"], "GPS_Coordinates": window.iloc[-1]["GPS_Coordinates"], "Correlation_Sensor_1_2": r12, "Correlation_Sensor_1_3": r13, "Correlation_Sensor_2_3": r23, }) # Output annotation results if results: results_df = pd.DataFrame(results) print("Uneven road section marking information:") print(results_df) # Save as a local file results_df.to_csv("uneven_paving_segments.csv", index=False, encoding='utf-8-sig') else: print("No uneven paving was detected.")

[0059] On the basis of the above, considering the thickness uniformity of the paved and compacted asphalt road along the traveling direction of the paver, the calculation module 20 determines the thickness uniformity of the asphalt road along the traveling direction of the paver by calculating the linear regression slope of all smoothed distance values ​​detected by at least one distance sensor 1021 within a continuous period of time. Figure 8 , the specific workflow is as follows.

[0060] Step 401: Set a fourth time window of fixed length.

[0061] It should be noted that the duration of the third time window is less than or equal to that of the fourth time window. The third time window is primarily used to analyze the similarity of fluctuations in the smoothed distance values ​​between different distance sensors 1021 to determine whether the asphalt paving is uniform. Because it focuses on comparing thickness changes over a short period of time, its duration is relatively short and is typically used to capture real-time fluctuation trends.

[0062] The fourth time window is used to analyze and calculate the linear regression slope of all smoothed distance values ​​detected by a single distance sensor 1021 over a continuous period of time to determine the thickness uniformity of the asphalt road along the paver's travel direction. This window is longer to provide sufficient time to determine whether the thickness variation continues to decrease, thereby effectively identifying abnormal conditions such as excessive paving speed.

[0063] Step 402: The smoothed distance values ​​of a distance sensor 1021 are sequentially entered into a fourth time window according to a chronological order. Within the fourth time window, all smoothed distance values ​​of the distance sensor 1021 form a second smoothed distance value sequence according to the detection time sequence. The number of smoothed distance values ​​in the second smoothed distance value sequence always remains equal to the length unit of the fourth time window, and the smoothed distance value of the distance sensor 1021 at the latest detection moment is located at the end of the fourth time window.

[0064] Step 403: Whenever the last digit of the fourth time window is input into the smoothed distance value at the latest detection moment, the linear regression slope of each second smoothed distance value sequence is calculated.

[0065] Step 404: If the linear regression slope of the second smoothed distance value sequence exceeds a set value, the paver's trajectory during the corresponding detection time is annotated and stored. This means that when the calculated linear regression slope exceeds the set value, it is determined that the pavement thickness during the corresponding detection time period has significantly changed, indicating uneven paving thickness. The system will locally store and annotate the paver's trajectory information at the corresponding detection time for subsequent traceability processing.

[0066] Regarding how to calculate the linear regression slope of the second smoothed distance value sequence formed by the single distance sensor 1021 in the fourth time window, the operation logic is similar to that of the third time window and will not be described in detail here.

[0067] It's important to note that the first, second, third, and fourth time windows described above all utilize a sliding window operation. Each time new data enters, the data within the window is updated slidingly, retaining the latest data for real-time analysis and calculation. This not only provides the most accurate real-time data analysis at each detection moment, but also ensures that the system can respond promptly to any changes during the asphalt paving process.

[0068] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. The scope of patent protection of the present invention shall be based on the claims. Any equivalent structural changes made using the description and drawings of the present invention shall be included in the scope of protection of the present invention.

Claims

1. The dynamic remote monitoring system for asphalt mixture paving speed in tunnel environment is applicable to pavers that can simultaneously perform asphalt paving and compaction. It is characterized by: include: The detection module includes a speed detection unit for detecting the traveling speed of the paver and a distance detection unit for detecting the vertical distance to the compacted asphalt road; wherein, The speed detection unit includes a wheel speed sensor provided on the paver, for detecting the travel speed in real time, and using the travel speed to represent the asphalt paving speed; The distance detection unit includes a plurality of distance sensors arranged perpendicular to the traveling direction of the paver, each of the distance sensors having a fixed position and the same detection frequency, and is used to detect the vertical distance; a calculation module for receiving the travel speed and vertical distance in real time, smoothing the vertical distance to obtain a smoothed distance value at each detection moment, sequentially arranging a plurality of the smoothed distance values ​​of each distance sensor in a continuous time period to form a smoothed distance value sequence, and calculating a linear correlation between the plurality of smoothed distance value sequences within the same time period to determine whether the asphalt road paved within the time period is uniform; if the linear correlation between any two smoothed distance value sequences deviates from a preset range, it is determined that the asphalt road in the area is unevenly paved; A storage module locally stores the paving speed and smoothed distance values ​​detected and calculated in real time, and uploads the data to the cloud in segments after storage; wherein, when an unevenly paved road section is detected, the smoothed distance value sequence whose linear correlation deviates from the preset range and the paving speed detected within the time period are marked.

2. The dynamic remote monitoring system for asphalt mixture paving speed in tunnel environment according to claim 1 is characterized in that: The calculation module smoothes the received vertical distance data to eliminate data noise, and obtains the smoothed distance value of each distance sensor at each detection moment after smoothing, including the following steps: Set a first time window of fixed length; The vertical distance of each distance sensor at each detection moment is sequentially input into the first time window in the order of detection time; wherein the number of vertical distances entering the first time window is always kept equal to the length unit of the window, and the vertical distance at the most recent detection moment of the distance sensor is placed at the end of the first time window; Whenever the last digit of the first time window is input into the vertical distance at the latest detection moment, the average value of all vertical distances in the first time window is calculated, and the average value is output as the smoothed distance value at the latest detection moment.

3. The dynamic remote monitoring system for asphalt mixture paving speed in tunnel environment according to claim 1 is characterized in that: The calculation module receives the travel speed detected by the wheel speed sensor in real time and calculates the speed to represent the asphalt paving speed, including the following steps: Set a second time window of fixed length; The travel speeds detected in real time by the wheel speed sensors are sequentially entered into the second time window in order of detection time; wherein the number of travel speeds entering the second time window is always kept equal to the unit of length of the window, and the travel speed at the most recent detection moment of the wheel speed sensor is at the end of the second time window; Whenever the last digit of the second time window is input as the traveling speed at the latest detection moment, the average value of all traveling speeds within the second time window is calculated, and the average value is output as the paving speed at the latest detection moment.

4. The dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment according to claim 1 is characterized in that: The detection module further includes a positioning unit, which is used to record the travel trajectory of the paver each time the distance sensor detects.

5. The dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment according to claim 1 is characterized in that: In the same time period, the calculation module determines whether the thickness uniformity of the asphalt road paved in the time period meets the setting by calculating the linear correlation value of the smoothed distance value sequences of different distance sensors and comparing it with the set range, including the following steps: Set a third time window of fixed length; Simultaneously inputting the smoothed distance values ​​of different distance sensors into the third time window, forming a first smoothed distance value sequence from all smoothed distance values ​​of each distance sensor in the third time window in order of detection time; wherein the number of smoothed distance values ​​in each of the first smoothed distance value sequences always remains equal to the length unit of the third time window, and the smoothed distance value of each distance sensor at the latest detection moment is located at the end of the third time window; Whenever the last digit of the third time window is simultaneously input with the smoothed distance values ​​of the latest detection moments of different distance sensors, the linear correlation value of any two of the first smoothed distance value sequences is calculated; When the linear correlation values ​​of any two of the first smoothed distance value sequences do not fall within the set range, the travel trajectory of the paver at the corresponding detection moment is marked and stored.

6. The dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment according to claim 5 is characterized in that: The calculation module calculates the linear correlation value of any two first smoothed distance value sequences by using the Pearson correlation coefficient method.

7. The dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment according to claim 1 is characterized in that: The calculation module determines the thickness uniformity of the asphalt road along the traveling direction of the paver by calculating the linear regression slope of all smoothed distance values ​​detected by at least one distance sensor in a continuous time period, including the following steps: Setting a fourth time window of fixed length; The smoothed distance values ​​of the distance sensor are sequentially entered into the fourth time window according to a chronological order, and all the smoothed distance values ​​of the distance sensor in the fourth time window are formed into a second smoothed distance value sequence according to a detection time order; wherein the number of smoothed distance values ​​in the second smoothed distance value sequence always remains equal to the length unit of the fourth time window, and the smoothed distance value at the latest detection moment of the distance sensor is located at the end of the fourth time window; Whenever the last bit of the fourth time window is input into the smoothed distance value at the latest detection moment, a linear regression slope of each second smoothed distance value sequence is calculated; If the linear regression slope of the second smoothed distance value sequence is higher than a set value, the travel trajectory of the paver during the corresponding detection time is marked and stored.

8. The dynamic remote monitoring system for asphalt mixture paving speed in a tunnel environment according to any one of claims 1 to 7, characterized in that: The first time window, the second time window, the third time window and the fourth window all adopt a moving window method. Every time new detection data enters, the data in the window will be updated slidingly to retain the latest detection data and perform real-time analysis and calculation.

Images